Fast rate coding in heterogeneous spiking networks
- 1EPFL
Abstract
A network of spiking neurons is said to operate in the rate coding regime if its dynamics can be accurately described by a continuous rate network. But how can discrete spike-based communication between neurons give rise to continuous rate-based dynamics? Computational studies showing that heterogeneous spiking networks can approximate the dynamics of recurrent neural networks (RNNs), a special case of rate network, assume that spikes are approximately Poisson or that network dynamics are slow. Here, we show that neither assumption is necessary: heterogeneous networks of non-Poisson neurons can produce fast rate-based dynamics. To demonstrate this, we show that signal transmission in feedforward networks of integrate-and-fire-type neurons can be described by a history-dependent rate network that is asymptotically exact as network size grows, even if heterogeneity persists, while linear-nonlinear rate networks fail. By providing a constructive model of fast rate coding in heterogeneous networks, this work lays the foundation for extending rate-based analyses of neural recordings beyond the assumption of slow dynamics or Poisson rate codes.
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BibTeX
@misc{smeets2026fast,
title = {{Fast rate coding in heterogeneous spiking networks}},
author = {K. Smeets and S. Wang and V. Schmutz and W. Gerstner},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.30.763048},
url = {https://www.biorxiv.org/content/10.64898/2026.10.30.763048v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Fast rate coding in heterogeneous spiking networks
AU - K. Smeets
AU - S. Wang
AU - V. Schmutz
AU - W. Gerstner
PY - 2026
DA - 2026/10/30
PB - bioRxiv
DO - 10.64898/2026.10.30.763048
UR - https://www.biorxiv.org/content/10.64898/2026.10.30.763048v1
AB - A network of spiking neurons is said to operate in the rate coding regime if its dynamics can be accurately described by a continuous rate network. But how can discrete spike-based communication between neurons give rise to continuous rate-based dynamics? Computational studies showing that heterogeneous spiking networks can approximate the dynamics of recurrent neural networks (RNNs), a special case of rate network, assume that spikes are approximately Poisson or that network dynamics are slow. Here, we show that neither assumption is necessary: heterogeneous networks of non-Poisson neurons can produce fast rate-based dynamics. To demonstrate this, we show that signal transmission in feedforward networks of integrate-and-fire-type neurons can be described by a history-dependent rate network that is asymptotically exact as network size grows, even if heterogeneity persists, while linear-nonlinear rate networks fail. By providing a constructive model of fast rate coding in heterogeneous networks, this work lays the foundation for extending rate-based analyses of neural recordings beyond the assumption of slow dynamics or Poisson rate codes.
ER -