Relating Neural Dynamics to Neural Coding

TL;DR

This paper establishes a theoretical link between the phase-resetting curve and spike-triggered average, connecting neural dynamics with coding and enabling better interpretation of neural responses.

Contribution

It proves that the STA is proportional to the derivative of the PRC, bridging dynamical systems and statistical analysis in neuroscience.

Findings

STA is proportional to the derivative of PRC

Validated results with Hodgkin-Huxley neuron simulations

Applied method to olfactory bulb neurons in mice

Abstract

We demonstrate that two key theoretical objects used widely in Computational Neuroscience, the phase-resetting curve (PRC) from dynamics and the spike triggered average (STA) from statistical analysis, are closely related under a wide range of stimulus conditions. We prove that the STA is proportional to the derivative of the PRC. We compare these analytic results to numerical calculations for the Hodgkin-Huxley neuron and we apply the method to neurons in the olfactory bulb of mice. This observation allows us to relate the stimulus-response properties of a neuron to its dynamics, bridging the gap between dynamical and information theoretic approaches to understanding brain computations and facilitating the interpretation of changes in channels and other cellular properties as influencing the representation of stimuli.