Computational Models

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^{1Citationpaper:paper-534291e9b1f8The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}. This observation is precisely what the ISN...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference} provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}. This observation is precisely what the ISN...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1 provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2. This observation is precisely what the ISN...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5. This observation is precisely what the ISN...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6 ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7 provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8. This observation is precisely what the ISN...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1. This observation is precisely what the ISN...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2 The analytical foundations of the ISN were formalized by ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3, who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent n > 1) reproduces cross-orientation suppression...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5 The analytical foundations of the ISN were formalized by ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6, who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7 extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent n > 1) reproduces cross-orientation suppression...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 The analytical foundations of the ISN were formalized by ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9, who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent n > 1) reproduces cross-orientation suppression...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1 The analytical foundations of the ISN were formalized by ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2, who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent n > 1) reproduces cross-orientation suppression...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 The analytical foundations of the ISN were formalized by ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5, who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6 extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent n > 1) reproduces cross-orientation suppression...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7 A critical feature of the ISN/SSN framework is that it makes specific predictions about SST neurons that differ depending on stimulus context. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 demonstrated through a spiking network model incorporating short-term plasticity that the response reversal of SST neurons — from facilitation to suppression — coincides with a change in the indispensability of SST for network stabilization ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9. Th...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 A critical feature of the ISN/SSN framework is that it makes specific predictions about SST neurons that differ depending on stimulus context. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1 demonstrated through a spiking network model incorporating short-term plasticity that the response reversal of SST neurons — from facilitation to suppression — coincides with a change in the indispensability of SST for network stabilization ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2. Th...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 A critical feature of the ISN/SSN framework is that it makes specific predictions about SST neurons that differ depending on stimulus context. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 demonstrated through a spiking network model incorporating short-term plasticity that the response reversal of SST neurons — from facilitation to suppression — coincides with a change in the indispensability of SST for network stabilization ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5. Th...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6 A critical feature of the ISN/SSN framework is that it makes specific predictions about SST neurons that differ depending on stimulus context. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7 demonstrated through a spiking network model incorporating short-term plasticity that the response reversal of SST neurons — from facilitation to suppression — coincides with a change in the indispensability of SST for network stabilization ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8. Th...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 The ISN regime has recently been shown to extend beyond cortex. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 used optogenetic manipulations to demonstrate paradoxical network stabilizations in hippocampal CA1 and CA3 ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1, suggesting that the computational principles governing SST function in cortical ISN models may apply more broadly to hippocampal circuits where SST neurons (particularly O-LM cells; see {ref}`sec-hippocampa...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2 The ISN regime has recently been shown to extend beyond cortex. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 used optogenetic manipulations to demonstrate paradoxical network stabilizations in hippocampal CA1 and CA3 ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4, suggesting that the computational principles governing SST function in cortical ISN models may apply more broadly to hippocampal circuits where SST neurons (particularly O-LM cells; see {ref}`sec-hippocampa...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}0 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}1, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}2. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}3 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}4 demonstrate that networks can achieve stabil...
^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}5 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}6, while models that distinguish cell types find that SST contributions are context-dependent ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}7. ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}8 and ^{3Citationpaper:paper-85bafa501bc9The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}9 demonstrate that networks can achieve stabil...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}00 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}01, while models that distinguish cell types find that SST contributions are context-dependent ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}02. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}03 and ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}04 demonstrate that networks can achieve stabil...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}05 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}06, while models that distinguish cell types find that SST contributions are context-dependent ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}07. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}08 and ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}09 demonstrate that networks can achieve stabil...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}10 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}11, while models that distinguish cell types find that SST contributions are context-dependent ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}12. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}13 and ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}14 demonstrate that networks can achieve stabil...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}15 Whether SST neurons are essential for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}16, while models that distinguish cell types find that SST contributions are context-dependent ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}17. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}18 and ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}19 demonstrate that networks can achieve stabil...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}20 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}21 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}22 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}23 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}24 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}25 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}26 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}27 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}28 Convergent evidence for the inhibition-stabilized network (ISN) regime and paradoxical SST responses, spanning analytical theory, rate-based and spiking computational models, and in vivo experimental recordings. Studies span different brain regions (V1, A1, hippocampus); direct quantitative comparison should be interpreted with caution. Sample sizes not reported for all studies. Data from [Ozeki2009, Ahmadian2013, R...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}29 The SSN framework treats surround suppression as an emergent property of the inhibition-stabilized regime, arising from the network’s supralinear input-output functions without requiring SST-specific lateral connections ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}30. In this model, any inhibitory neuron class could contribute to surround suppression; the phenomenon does not depend on the particular connectivity of SST neurons. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}31 confirmed t...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}32 The SSN framework treats surround suppression as an emergent property of the inhibition-stabilized regime, arising from the network’s supralinear input-output functions without requiring SST-specific lateral connections ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}33. In this model, any inhibitory neuron class could contribute to surround suppression; the phenomenon does not depend on the particular connectivity of SST neurons. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}34 confirmed t...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}35 The SSN framework treats surround suppression as an emergent property of the inhibition-stabilized regime, arising from the network’s supralinear input-output functions without requiring SST-specific lateral connections ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}36. In this model, any inhibitory neuron class could contribute to surround suppression; the phenomenon does not depend on the particular connectivity of SST neurons. ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}37 confirmed t...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}38 In direct contrast, ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}39 proposed a model in which surround suppression requires the specific recruitment of Martinotti cells through long-range lateral excitatory connections ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}40. In this framework, lateral excitation from neighboring columns recruits SST Martinotti cells, whose ascending axons to layer 1 then suppress pyramidal neuron apical dendrites. The model further predicts that...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}41 In direct contrast, ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}42 proposed a model in which surround suppression requires the specific recruitment of Martinotti cells through long-range lateral excitatory connections ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}43. In this framework, lateral excitation from neighboring columns recruits SST Martinotti cells, whose ascending axons to layer 1 then suppress pyramidal neuron apical dendrites. The model further predicts that...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}44 In direct contrast, ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}45 proposed a model in which surround suppression requires the specific recruitment of Martinotti cells through long-range lateral excitatory connections ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}46. In this framework, lateral excitation from neighboring columns recruits SST Martinotti cells, whose ascending axons to layer 1 then suppress pyramidal neuron apical dendrites. The model further predicts that...
^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}47 A hybrid position has emerged from ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}48, whose SSN model incorporates cell-type-specific loops (PC→VIP⊣SST⊣PC and PC→SST⊣PC) that switch the dominant circuit motif depending on stimulus conditions ^{2Citationpaper:paper-e15053dfa039The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...content/12_computational_models.md:line 7Open reference}49. This context-dependent model predicts that the SST contribution to surround suppression varies with contrast, spatial frequency, and behavioral state — potentially reconciling the emergent and SST-s...
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References

[Ahmadian2013] paper:paper-534291e9b1f8 “The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...”
[Rubin2015] paper:paper-e15053dfa039 “The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...”
[Ozeki2009] paper:paper-85bafa501bc9 “The strongest computational framework for understanding SST neuron contributions to cortical dynamics is the inhibition-stabilized network (ISN). The ISN concept, first articulated in theoretical work and subsequently formalized across multiple modeling frameworks, describes a regime in which recurrent excitation within a cortical network is strong enough that, without inhibition, activity would grow without bound [...”
[Hennequin2018] paper:paper-818c0c4864de “[Ozeki2009] provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition [Ozeki2009]. This observation is precisely what the ISN...”
[WatkinsdeJong2023] paper:paper-f4a8cac00b71 “[Ozeki2009] provided foundational experimental evidence for the ISN regime through intracellular recordings during surround suppression in cat V1. Their central finding was that both excitation and inhibition decreased during surround suppression — directly contradicting the then-prevailing model that surround suppression arose from increased lateral inhibition [Ozeki2009]. This observation is precisely what the ISN...”
[Holt2024] paper:paper-21a697680a63 “The analytical foundations of the ISN were formalized by [Ahmadian2013], who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. [Rubin2015] extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent *n* > 1) reproduces cross-orientation suppression...”
[Obeid2025] paper:paper-87e1cd00ec60 “The analytical foundations of the ISN were formalized by [Ahmadian2013], who derived the conditions under which networks with supralinear input-output functions enter the inhibition-stabilized regime. [Rubin2015] extended this into the stabilized supralinear network (SSN) framework, demonstrating that a network with power-law neuronal input-output functions (exponent *n* > 1) reproduces cross-orientation suppression...”
[Waitzmann2024] paper:paper-9ba6315873d1 “A critical feature of the ISN/SSN framework is that it makes specific predictions about SST neurons that differ depending on stimulus context. [Waitzmann2024] demonstrated through a spiking network model incorporating short-term plasticity that the response reversal of SST neurons — from facilitation to suppression — coincides with a change in the indispensability of SST for network stabilization [Waitzmann2024]. Th...”
[GarciadelMolino2017] paper:paper-e67fae66877b “A critical feature of the ISN/SSN framework is that it makes specific predictions about SST neurons that differ depending on stimulus context. [Waitzmann2024] demonstrated through a spiking network model incorporating short-term plasticity that the response reversal of SST neurons — from facilitation to suppression — coincides with a change in the indispensability of SST for network stabilization [Waitzmann2024]. Th...”
[Bos2025] paper:paper-00de1a2c593e “Whether SST neurons are *essential* for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool [Ahmadian2013], while models that distinguish cell types find that SST contributions are context-dependent [Waitzmann2024, Bos2025]. [Sarkar2025] and [Agnes2020] demonstrate that networks can achieve stabil...”
[Sarkar2025] paper:paper-406a4ca8b0fd “Whether SST neurons are *essential* for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool [Ahmadian2013], while models that distinguish cell types find that SST contributions are context-dependent [Waitzmann2024, Bos2025]. [Sarkar2025] and [Agnes2020] demonstrate that networks can achieve stabil...”
[Agnes2020] paper:paper-2a405935adf4 “Whether SST neurons are *essential* for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool [Ahmadian2013], while models that distinguish cell types find that SST contributions are context-dependent [Waitzmann2024, Bos2025]. [Sarkar2025] and [Agnes2020] demonstrate that networks can achieve stabil...”
[Shore2024] paper:paper-07cdd22cd16b “Whether SST neurons are *essential* for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool [Ahmadian2013], while models that distinguish cell types find that SST contributions are context-dependent [Waitzmann2024, Bos2025]. [Sarkar2025] and [Agnes2020] demonstrate that networks can achieve stabil...”
[Seay2020] paper:paper-773912422ac5 “Whether SST neurons are *essential* for network stabilization in the ISN regime, or whether other inhibitory mechanisms can substitute, remains contested. Standard ISN models treat all inhibition as a single pool [Ahmadian2013], while models that distinguish cell types find that SST contributions are context-dependent [Waitzmann2024, Bos2025]. [Sarkar2025] and [Agnes2020] demonstrate that networks can achieve stabil...”
[Krishnamurthy2015] paper:paper-7cf52a12f6e4 “In direct contrast, [Krishnamurthy2015] proposed a model in which surround suppression requires the specific recruitment of Martinotti cells through long-range lateral excitatory connections [Krishnamurthy2015]. In this framework, lateral excitation from neighboring columns recruits SST Martinotti cells, whose ascending axons to layer 1 then suppress pyramidal neuron apical dendrites. The model further predicts that...”
[Hendricks2026] paper:paper-6922f4d24e18 “In direct contrast, [Krishnamurthy2015] proposed a model in which surround suppression requires the specific recruitment of Martinotti cells through long-range lateral excitatory connections [Krishnamurthy2015]. In this framework, lateral excitation from neighboring columns recruits SST Martinotti cells, whose ascending axons to layer 1 then suppress pyramidal neuron apical dendrites. The model further predicts that...”
[Mossing2021] paper:paper-6f3fdb602713 “A hybrid position has emerged from [Mossing2021], whose SSN model incorporates cell-type-specific loops (PC→VIP⊣SST⊣PC and PC→SST⊣PC) that switch the dominant circuit motif depending on stimulus conditions [Mossing2021]. This context-dependent model predicts that the SST contribution to surround suppression varies with contrast, spatial frequency, and behavioral state — potentially reconciling the emergent and SST-s...”

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