Linear-nonlinear cascades capture synaptic dynamics

PLoS Comput Biol. 2021 Mar 15;17(3):e1008013. doi: 10.1371/journal.pcbi.1008013. eCollection 2021 Mar.

Abstract

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials
  • Algorithms
  • Likelihood Functions
  • Linear Models*
  • Models, Neurological
  • Nerve Net
  • Neuronal Plasticity
  • Nonlinear Dynamics*
  • Reproducibility of Results
  • Stochastic Processes
  • Synapses / physiology*
  • Synaptic Transmission / physiology*