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Learning to synchronize : how biological agents can couple neural task modules for dealing with the stability-plasticity dilemma

Pieter Verbeke (UGent) and Tom Verguts (UGent)
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Abstract
We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing. In this way, they can address the stability-plasticity dilemma. For this purpose, we combine two prominent computational neuroscience principles, namely Binding by Synchrony and Reinforcement Learning. The model learns to synchronize task-relevant modules, while also learning to desynchronize currently task-irrelevant modules. As a result, old (but currently task-irrelevant) information is protected from overwriting (stability) while new information can be learned quickly in currently task-relevant modules (plasticity). We combine learning to synchronize with task modules that learn via one of several classical learning algorithms (Rescorla-Wagner, backpropagation, Boltzmann machines). The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules. We demonstrate that our combined model has significant computational advantages over the original network without synchrony, in terms of both stability and plasticity. Importantly, the resulting models' processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies.
Keywords
ANTERIOR CINGULATE CORTEX, PREFRONTAL CORTEX, INTEGRATIVE THEORY, COMPUTATIONAL MODEL, DECISION-MAKING, FRONTAL THETA, SYSTEMS, COMMUNICATION, OSCILLATIONS, ACCOUNT

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MLA
Verbeke, Pieter, and Tom Verguts. “Learning to Synchronize : How Biological Agents Can Couple Neural Task Modules for Dealing with the Stability-Plasticity Dilemma.” PLOS COMPUTATIONAL BIOLOGY, vol. 15, no. 8, 2019.
APA
Verbeke, P., & Verguts, T. (2019). Learning to synchronize : how biological agents can couple neural task modules for dealing with the stability-plasticity dilemma. PLOS COMPUTATIONAL BIOLOGY, 15(8).
Chicago author-date
Verbeke, Pieter, and Tom Verguts. 2019. “Learning to Synchronize : How Biological Agents Can Couple Neural Task Modules for Dealing with the Stability-Plasticity Dilemma.” PLOS COMPUTATIONAL BIOLOGY 15 (8).
Chicago author-date (all authors)
Verbeke, Pieter, and Tom Verguts. 2019. “Learning to Synchronize : How Biological Agents Can Couple Neural Task Modules for Dealing with the Stability-Plasticity Dilemma.” PLOS COMPUTATIONAL BIOLOGY 15 (8).
Vancouver
1.
Verbeke P, Verguts T. Learning to synchronize : how biological agents can couple neural task modules for dealing with the stability-plasticity dilemma. PLOS COMPUTATIONAL BIOLOGY. 2019;15(8).
IEEE
[1]
P. Verbeke and T. Verguts, “Learning to synchronize : how biological agents can couple neural task modules for dealing with the stability-plasticity dilemma,” PLOS COMPUTATIONAL BIOLOGY, vol. 15, no. 8, 2019.
@article{8626658,
  abstract     = {We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing. In this way, they can address the stability-plasticity dilemma. For this purpose, we combine two prominent computational neuroscience principles, namely Binding by Synchrony and Reinforcement Learning. The model learns to synchronize task-relevant modules, while also learning to desynchronize currently task-irrelevant modules. As a result, old (but currently task-irrelevant) information is protected from overwriting (stability) while new information can be learned quickly in currently task-relevant modules (plasticity). We combine learning to synchronize with task modules that learn via one of several classical learning algorithms (Rescorla-Wagner, backpropagation, Boltzmann machines). The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules. We demonstrate that our combined model has significant computational advantages over the original network without synchrony, in terms of both stability and plasticity. Importantly, the resulting models' processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies.},
  articleno    = {e1006604},
  author       = {Verbeke, Pieter and Verguts, Tom},
  issn         = {1553-7358},
  journal      = {PLOS COMPUTATIONAL BIOLOGY},
  keywords     = {ANTERIOR CINGULATE CORTEX,PREFRONTAL CORTEX,INTEGRATIVE THEORY,COMPUTATIONAL MODEL,DECISION-MAKING,FRONTAL THETA,SYSTEMS,COMMUNICATION,OSCILLATIONS,ACCOUNT},
  language     = {eng},
  number       = {8},
  pages        = {25},
  title        = {Learning to synchronize : how biological agents can couple neural task modules for dealing with the stability-plasticity dilemma},
  url          = {http://dx.doi.org/10.1371/journal.pcbi.1006604},
  volume       = {15},
  year         = {2019},
}

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