A balanced-network model with completely random recurrent excitatory connectivi…

A balanced-network model with completely random recurrent excitatory connectivity reproduces strong V1 orientation selectivity in mice and rats, demonstrating that feature-similarity-based wiring is not strictly required for selectivity in salt-and-pepper V1.

{
  "n": 0,
  "doi": "10.1523/jneurosci.6284-11.2012",
  "claim": "A balanced-network model with completely random recurrent excitatory connectivity reproduces strong V1 orientation selectivity in mice and rats, demonstrating that feature-similarity-based wiring is not strictly required for selectivity in salt-and-pepper V1.",
  "cite_key": "Hansel2012",
  "evidence": "Computational analysis of a balanced L2/3 network with random recurrent connectivity and random feedforward orientation preferences, applied to mouse/rat V1 architecture.",
  "effect_size": "Strong orientation tuning emerges in the balanced regime without like-to-like recurrence",
  "text_access": "abstract_only",
  "study_system": "balanced cortical network model for mouse/rat V1 L2/3; theoretical study",
  "argument_role": "supporting",
  "replication_status": "single_study",
  "claim_source_sentence": "Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [],
  "effect_size_source_sentence": "We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime."
}

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