Computational Models of Inhibitory Diversity: What Can Theory Explain?

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^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference} introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 Within single-I frameworks, ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 Within single-I frameworks, ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 Within single-I frameworks, ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 Within single-I frameworks, ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 Within single-I frameworks, ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 demonst...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 demonst...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 demonst...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 demonst...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 demonst...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 extended SSN predictions to re...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 extended SSN predictions to re...
^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{2Citationpaper:paper-0e76c44c20caThe theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}0 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}1 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}2 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}3 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}4 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}5 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}6 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}7 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}8 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}9 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}00 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}01 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}02 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}03 Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}04 found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}05 confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}06 extended SSN predictions to re...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}07 The SSN has also been adapted for computational inference: ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}08 trained an SSN on amortized Markov chain Monte Carlo inference, demonstrating that the network’s nonlinear dynamics can implement sophisticated probabilistic computations. These extensions illustrate both the flexibility and the limitation of SSN models: mathematical tractability enables analytical insight, but the same tractability relies on simpl...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}09 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}10 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}11 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}12 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}13 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}14 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}15 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}16 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}17 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}18 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}19 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}20 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}21 The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}22 studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}23 The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}24 identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}25 The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}26 identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}27 The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}28 identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}29 The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}30 identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}31 The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}32 identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...
^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}33 The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. ^{1Citationpaper:paper-1af5e6854959The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...content/12_computational_models.md:line 10Open reference}34 identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...
... 132 additional anchors in refs_json

References

[Wilson1972] paper:paper-1af5e6854959 “The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...”
[VanVreeswijk1996] paper:paper-0e76c44c20ca “The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...”
[Tsodyks1997] paper:paper-2d755fbddd3b “The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...”
[Hennequin2014] paper:paper-1e48f47c8295 “The theoretical study of cortical inhibition begins with models that collapse all GABAergic neurons into a single population. [Wilson1972] introduced the excitatory-inhibitory (E-I) firing rate framework, demonstrating that coupled excitatory and inhibitory populations can generate oscillations, bistability, and winner-take-all competition---phenomena that require no interneuron diversity whatsoever. The balanced ne...”
[Hennequin2018] paper:paper-818c0c4864de “[Hennequin2014] showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. [Hennequin2018] further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...”
[Ferguson2018] paper:paper-0b942dc09a15 “[Hennequin2014] showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. [Hennequin2018] further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...”
[Uliana2024] paper:paper-1f6bffd7dee0 “[Hennequin2014] showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. [Hennequin2018] further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...”
[Tai2014] paper:paper-855d0bdbad3b “[Hennequin2014] showed that in networks where excitation and inhibition are as tightly balanced as experimentally observed, inhibitory control of complex excitatory recurrence emerges as a general organizational principle. [Hennequin2018] further demonstrated that fluctuations about a single, stimulus-driven attractor in a loosely balanced E-I network---the stochastic stabilized supralinear network regime---can acco...”
[Wu2022b] paper:paper-8f953d2e2798 “Within single-I frameworks, [Tsodyks1997] made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...”
[Seay2020] paper:paper-773912422ac5 “Within single-I frameworks, [Tsodyks1997] made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...”
[Godin2025] paper:paper-274acddaaf46 “Within single-I frameworks, [Tsodyks1997] made a surprising prediction: increasing direct external inhibitory input to the inhibitory interneurons can, under some circumstances, cause a paradoxical increase in their firing rate---the hallmark of what would later be formalized as the inhibition-stabilized network (ISN) regime. This paradoxical effect arises because, in strongly recurrent networks, suppressing inhibit...”
[Kraynyukova2018] paper:paper-5989bc007d71 “The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. [Kraynyukova2018] showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. [Rubin2015] demonst...”
[Rubin2015] paper:paper-e15053dfa039 “The stabilized supralinear network (SSN) model represents an important intermediate step between single-I and multi-population architectures. [Kraynyukova2018] showed that the SSN framework, originally proposed for sensory integration phenomena, generalizes to explain contrast invariance, normalization, and surround suppression within a unified architecture incorporating two to three populations. [Rubin2015] demonst...”
[Liu2017] paper:paper-9b5c8cc09b41 “Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. [Liu2017] found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and [Liu2018b] confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. [Holt2023] extended SSN predictions to re...”
[Liu2018b] paper:paper-a3e681dbc8f3 “Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. [Liu2017] found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and [Liu2018b] confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. [Holt2023] extended SSN predictions to re...”
[Holt2023] paper:paper-21a697680a63 “Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. [Liu2017] found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and [Liu2018b] confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. [Holt2023] extended SSN predictions to re...”
[Ozeki2009] paper:paper-85bafa501bc9 “Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. [Liu2017] found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and [Liu2018b] confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. [Holt2023] extended SSN predictions to re...”
[Ozeki2004] paper:paper-d168a47d39ff “Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. [Liu2017] found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and [Liu2018b] confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. [Holt2023] extended SSN predictions to re...”
[Obeid2021] paper:paper-8fe5f361e26a “Subsequent work has tested and extended SSN predictions across cortical areas and stimulus conditions. [Liu2017] found that MT neurons integrate across motion directions for low-contrast stimuli as predicted by the SSN, and [Liu2018b] confirmed these predictions in additional recording conditions, though the degree of cross-orientation suppression varied across preparations. [Holt2023] extended SSN predictions to re...”
[Soo2022] paper:paper-6382892a8ab9 “The SSN has also been adapted for computational inference: [Soo2022] trained an SSN on amortized Markov chain Monte Carlo inference, demonstrating that the network's nonlinear dynamics can implement sophisticated probabilistic computations. These extensions illustrate both the flexibility and the limitation of SSN models: mathematical tractability enables analytical insight, but the same tractability relies on simpl...”
[Litwin-Kumar2016] paper:paper-8f25714ab7fe “The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. [Litwin-Kumar2016] studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...”
[Kim2025] paper:paper-732f0f33eade “The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. [Litwin-Kumar2016] studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...”
[Mahrach2020] paper:paper-38fe71bdf9b6 “The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. [Litwin-Kumar2016] studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...”
[Kepecs2014] paper:paper-ac3588f709c2 “The transition from single-I or two-population models to architectures incorporating PV, SST, and VIP interneurons as distinct populations was driven by experimental findings that could not be accommodated within simpler frameworks. [Litwin-Kumar2016] studied the dynamics of recurrent networks with PV, SOM, and VIP interneurons using connectivity derived from experimental measurements, demonstrating that the three i...”
[Pi2013] paper:paper-065a0eae69a5 “The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. [Pi2013] identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...”
[Piet2024] paper:pmid:38447579 “The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. [Pi2013] identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...”
[Furutachi2023] paper:paper-2e605ae7ac17 “The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. [Pi2013] identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...”
[Keller2020] paper:paper-174232a0ceff “The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. [Pi2013] identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...”
[Zhao2023] paper:paper-d11396d9b508 “The discovery of VIP-mediated disinhibition provided a compelling case for multi-population models. [Pi2013] identified a basic disinhibitory circuit module in which VIP interneurons transiently suppress primarily SST-expressing and a fraction of PV-expressing inhibitory interneurons, thereby releasing pyramidal cells from inhibition. This VIP ⊣ SST ⊣ pyramidal motif cannot be represented in any single-I framework a...”

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