Version history

{
  "scope": "computational network model",
  "claim_text": "Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework",
  "raw_fields": {
    "n": null,
    "doi": "10.1371/journal.pcbi.1004792",
    "claim": "Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework",
    "cite_key": "Song2016",
    "evidence": "The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, \"trained\" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them a",
    "effect_size": "qualitative",
    "text_access": "fulltext",
    "study_system": "computational network model",
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    "claim_source_sentence": "We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions.",
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    "replication_evidence_dois": [],
    "effect_size_source_sentence": "We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions."
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  "section_id": "section_13",
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  "study_system": "computational network model",
  "evidence_refs": [
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  "section_title": "13. Attractor-network models — Hopfield, ring, line, bump; what each model requires of the cortical E→E matrix and what the mouse empirical record provides",
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    "notes": [
      "Local review repositories are read-only inputs.",
      "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
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  "evidence_summary": "The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, \"trained\" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them a",
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  "origin_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewRecurrence/blob/79ce062d54a924ce05953ec90aa9d26044d2b48f/evidence/section_13_evidence_package.json",
  "commit_sha": "79ce062d54a924ce05953ec90aa9d26044d2b48f",
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}