Temporal resolution of spike coding in feedforward networks with signal convergence and divergence.

Authors

Zach Mobille, Usama Bin Sikandar, Simon Sponberg, Hannah Choi

Published in

PLoS computational biology. Volume 21. Issue 4. Pages e1012971. Apr 21, 2025. Epub Apr 21, 2025.

Abstract

Convergent and divergent structures in the networks that make up biological brains are found across many species and brain regions at various spatial scales. Neurons in these networks fire action potentials, or "spikes," whose precise timing is becoming increasingly appreciated as large sources of information about both sensory input and motor output. In this work, we investigate the extent to which feedforward convergent/divergent network structure is related to the gain in information of spike timing representations over spike count representations. While previous theories on coding in convergent and divergent networks have largely neglected the role of precise spike timing, our model and analyses place this aspect at the forefront. For a suite of stimuli with different timescales, we demonstrate that structural bottlenecks-small groups of neurons post-synaptic to network convergence-have a stronger preference for spike timing codes than expansion layers created by structural divergence. We further show that this relationship can be generalized across different spike-generating models and measures of coding capacity, implying a potentially fundamental link between network structure and coding strategy using spikes. Additionally, we found that a simple network model based on convergence and divergence ratios of a hawkmoth (Manduca sexta) nervous system can reproduce the relative contribution of spike timing information in its motor output, providing testable predictions on optimal temporal resolutions of spike coding across the moth sensory-motor pathway at both the single-neuron and population levels.

PMID:
40258062
Bibliographic data and abstract were imported from PubMed on 22 Apr 2025.

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