Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation
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12559_2022_9994_ReferencePDF.pdf
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Author(s)
Xue, Xiaohe
Wimmer, Ralf D.
Halassa, Michael M.
Chen, Zhe S.
Date Issued
February 5, 2022
Publisher
Springer US
Citation
Xue, Xiaohe, Wimmer, Ralf D., Halassa, Michael M. and Chen, Zhe S. 2022. "Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation."
Version
Author's final manuscript
Abstract
Abstract
Prefrontal cortical neurons play essential roles in performing rule-dependent tasks and working memory-based decision making. Motivated by PFC recordings of task-performing mice, we developed an excitatory–inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN and adapted spike frequency adaptation (SFA) and SuperSpike gradient methods to train the SRNN efficiently. The trained SRNN produced emergent rule-specific tunings in single-unit representations, showing rule-dependent population dynamics that resembled experimentally observed data. Under various test conditions, we manipulated the SRNN parameters or configuration in computer simulations, and we investigated the impacts of rule-coding error, delay duration, recurrent weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control and provides new experimentally testable hypotheses in future experiments.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s12559-022-09994-2
