Probabilistically segregated neural circuits and subcritical linguistics

Abstract

Early studies of cortical information codes and memory capacity have assumed large neural networks, which, subject to evenly probable binary (on/off) activity, were found to be endowed with large storage and retrieval capacities under the Hebbian paradigm. Here, we show that such networks are plagued with exceedingly high cross-network connectivity, yielding long code words, which are linguistically non-realistic and difficult to memorize and comprehend. Noting that the neural circuit activity code is jointly governed by somatic and synaptic activity states, termed neural circuit polarities, we show that, subject to subcritical polarity probability, random-graph-theoretic considerations imply small neural circuit segregation. Such circuits are shown to represent linguistically plausible cortical code words which, in turn, facilitate storage and retrieval of both circuit connectivity and firing-rate dynamics.

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Acknowledgement

The author thanks Yuval Filmus for a very helpful introduction to random graph theory.

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Correspondence to Yoram Baram.

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Baram, Y. Probabilistically segregated neural circuits and subcritical linguistics. Cogn Neurodyn (2020). https://doi.org/10.1007/s11571-020-09602-9

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Keywords

  • Cortical linguistics
  • Neural circuit polarity
  • Random graphs
  • Subcritical cortical connectivity
  • Segregated neural circuits
  • Subcritical Hebbian capacity
  • Firing-rate dynamics