CITATION.cff

cff-version: 1.2.0
title: >-
  Sufficient conditions for offline reactivation in
  recurrent neural networks
message: >-
  If this code was useful to you, please consider citing our
  work.
type: software
authors:
  - family-names: Krishna
    given-names: Nanda H
    orcid: "https://orcid.org/0000-0001-8036-2789"
  - family-names: Bredenberg
    given-names: Colin
    orcid: "https://orcid.org/0000-0002-9749-9228"
  - family-names: Levenstein
    given-names: Daniel
    orcid: "https://orcid.org/0000-0002-5507-9145"
  - family-names: Richards
    given-names: Blake Aaron
    orcid: "https://orcid.org/0000-0001-9662-2151"
  - family-names: Lajoie
    given-names: Guillaume
    orcid: "https://orcid.org/0000-0003-2730-7291"
identifiers:
  - type: url
    value: "https://openreview.net/forum?id=RVrINT6MT7"
    description: Paper
repository-code: "https://github.com/nandahkrishna/RNNReactivation"
abstract: >-
  During periods of quiescence, such as sleep, neural
  activity in many brain circuits resembles that observed
  during periods of task engagement. However, the precise
  conditions under which task-optimized networks can
  autonomously reactivate the same network states
  responsible for online behavior is poorly understood. In
  this study, we develop a mathematical framework that
  outlines sufficient conditions for the emergence of neural
  reactivation in circuits that encode features of smoothly
  varying stimuli. We demonstrate mathematically that noisy
  recurrent networks optimized to track environmental state
  variables using change-based sensory information naturally
  develop denoising dynamics, which, in the absence of
  input, cause the network to revisit state configurations
  observed during periods of online activity. We validate
  our findings using numerical experiments on two canonical
  neuroscience tasks: spatial position estimation based on
  self-motion cues, and head direction estimation based on
  angular velocity cues. Overall, our work provides
  theoretical support for modeling offline reactivation as
  an emergent consequence of task optimization in noisy
  neural circuits.
keywords:
  - computational neuroscience
  - offline reactivation
  - replay
  - recurrent neural networks
  - path integration
  - noise
license: BSD-3-Clause
preferred-citation:
  type: conference-paper
  authors:
    - family-names: Krishna
      given-names: Nanda H
      orcid: "https://orcid.org/0000-0001-8036-2789"
    - family-names: Bredenberg
      given-names: Colin
      orcid: "https://orcid.org/0000-0002-9749-9228"
    - family-names: Levenstein
      given-names: Daniel
      orcid: "https://orcid.org/0000-0002-5507-9145"
    - family-names: Richards
      given-names: Blake Aaron
      orcid: "https://orcid.org/0000-0001-9662-2151"
    - family-names: Lajoie
      given-names: Guillaume
      orcid: "https://orcid.org/0000-0003-2730-7291"
  title: >-
    Sufficient conditions for offline reactivation in
    recurrent neural networks
  collection-title: >-
    The Twelfth International Conference on Learning
    Representations
  year: 2024
  url: "https://openreview.net/forum?id=RVrINT6MT7"