Neuronal Synchronization Can Control the Energy Efficiency of Inter-Spike Interval Coding

TL;DR

This study models how neuronal synchronization influences energy-efficient information transmission, revealing an optimal synchronization level around 30% that maximizes information per energy cost in neural communication.

Contribution

It introduces synchronization as an independent control parameter and applies the Berger-Levy theory to analyze energy-efficient neural coding with a Hodgkin-Huxley model.

Findings

Optimal synchronization level around 30% maximizes information efficiency.

Synchronization influences the interspike interval distribution of postsynaptic neurons.

Energy efficiency of neural communication can be dynamically modulated by synchronization.

Abstract

The role of synchronous firing in sensory coding and cognition remains controversial. While studies, focusing on its mechanistic consequences in attentional tasks, suggest that synchronization dynamically boosts sensory processing, others failed to find significant synchronization levels in such tasks. We attempt to understand both lines of evidence within a coherent theoretical framework. We conceptualize synchronization as an independent control parameter to study how the postsynaptic neuron transmits the average firing activity of a presynaptic population, in the presence of synchronization. We apply the Berger-Levy theory of energy efficient information transmission to interpret simulations of a Hodgkin-Huxley-type postsynaptic neuron model, where we varied the firing rate and synchronization level in the presynaptic population independently. We find that for a fixed presynaptic firing rate the simulated postsynaptic interspike interval distribution depends on the synchronization level and is well-described by a generalized extreme value distribution. For synchronization levels of 15% to 50%, we find that the optimal distribution of presynaptic firing rate, maximizing the mutual information per unit cost, is maximized at ~30% synchronization level. These results suggest that the statistics and energy efficiency of neuronal communication channels, through which the input rate is communicated, can be dynamically adapted by the synchronization level.