Efficient and robust coding in heterogeneous recurrent networks

PLoS Comput Biol. 2021 Apr 30;17(4):e1008673. doi: 10.1371/journal.pcbi.1008673. eCollection 2021 Apr.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Models, Neurological*
  • Nerve Net / physiology*
  • Neural Networks, Computer
  • Neurons / physiology*

Grant support

FZ acknowledges support from the Netherlands Organisation for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO) Veni grant (863.150.25) and the Radboud University (Christine Mohrmann Foundation), SD acknowledges support from Neuropole Region Île de France (NERF) and ERC consolidator grant “predispike” and BSG acknowledges funding from the Basic Research Program at the National Research University Higher School of Economics (HSE University), ANR-17-EURE- 1553-0017, and ANR-10-IDEX-0001-02. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.