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Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks

View ORCID ProfileRobert Kim, Terrence J. Sejnowski
doi: https://doi.org/10.1101/2020.02.11.944751
Robert Kim
1Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
2Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
3Medical Scientist Training Program, University of California San Diego, La Jolla, CA 92093, USA
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  • For correspondence: rkim@salk.edu terry@salk.edu
Terrence J. Sejnowski
1Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
4Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA
5Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA
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  • For correspondence: rkim@salk.edu terry@salk.edu
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Abstract

Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timescales stable enough to support WM are unclear. By analyzing a spiking recurrent neural network (RNN) model trained on a WM task and activity of single neurons in the primate prefrontal cortex, we show that the temporal properties of our model and the neural data are remarkably similar. Dissecting our RNN model revealed strong inhibitory-to-inhibitory connections underlying a disinhibitory microcircuit as a critical component for long neuronal timescales and WM maintenance. We also found that enhancing inhibitory-to-inhibitory connections led to more stable temporal dynamics and improved task performance. Finally, we show that a network with such microcircuitry can perform other tasks without disrupting its pre-existing timescale architecture, suggesting that strong inhibitory signaling underlies a flexible WM network.

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Posted February 12, 2020.
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Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks
Robert Kim, Terrence J. Sejnowski
bioRxiv 2020.02.11.944751; doi: https://doi.org/10.1101/2020.02.11.944751
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Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks
Robert Kim, Terrence J. Sejnowski
bioRxiv 2020.02.11.944751; doi: https://doi.org/10.1101/2020.02.11.944751

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