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Efficient and robust coding in heterogeneous recurrent networks

View ORCID ProfileFleur Zeldenrust, View ORCID ProfileBoris Gutkin, View ORCID ProfileSophie Denéve
doi: https://doi.org/10.1101/804864
Fleur Zeldenrust
1Department of Neurophysiology, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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  • For correspondence: f.zeldenrust@neurophysiology.nl
Boris Gutkin
2Group for Neural Theory, INSERM U960, Département d’Études Cognitives, École Normal Supérieure, Paris, France
3Department of Psychology, Center for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russia
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Sophie Denéve
2Group for Neural Theory, INSERM U960, Département d’Études Cognitives, École Normal Supérieure, Paris, France
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Abstract

Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, ‘type 1’ and ‘type 2’ neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that ‘type 2’ neurons are more coherent with the overall network activity than ‘type 1’ neurons.

Footnotes

  • https://github.com/fleurzeldenrust/Efficient-coding-in-a-spiking-predictive-coding-network

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 16, 2019.
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Efficient and robust coding in heterogeneous recurrent networks
Fleur Zeldenrust, Boris Gutkin, Sophie Denéve
bioRxiv 804864; doi: https://doi.org/10.1101/804864
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Efficient and robust coding in heterogeneous recurrent networks
Fleur Zeldenrust, Boris Gutkin, Sophie Denéve
bioRxiv 804864; doi: https://doi.org/10.1101/804864

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