The Optimal Size of Stochastic Hodgkin-Huxley Neuronal Systems for Maximal Energy Efficiency in Coding of Pulse Signals

TL;DR

This study explores how the size of stochastic Hodgkin-Huxley neuronal systems affects energy efficiency in pulse signal coding, identifying optimal neuron and ion channel numbers through simulations and analytical models.

Contribution

It introduces a combined simulation and analytical approach to determine the optimal neural system size for energy-efficient pulse coding.

Findings

Optimal ion channel number maximizes energy efficiency.

Optimal neuron population size depends on input signal characteristics.

Trade-offs between reliability and energy cost influence neural system size.

Abstract

The generation and conduction of action potentials represents a fundamental means of communication in the nervous system, and is a metabolically expensive process. In this paper, we investigate the energy efficiency of neural systems in a process of transfer pulse signals with action potentials. By computer simulation of a stochastic version of Hodgkin-Huxley model with detailed description of ion channel random gating, and analytically solve a bistable neuron model that mimic the action potential generation with a particle crossing the barrier of a double well, we find optimal number of ion channels that maximize energy efficiency for a neuron. We also investigate the energy efficiency of neuron population in which input pulse signals are represented with synchronized spikes and read out with a downstream coincidence detector neuron. We find an optimal combination of the number of neurons in neuron population and the number of ion channels in each neuron that maximize the energy efficiency. The energy efficiency depends on the characters of the input signals, e.g., the pulse strength and the inter-pulse intervals. We argue that trade-off between reliability of signal transmission and energy cost may influence the size of the neural systems if energy use is constrained.