# Dynamics and energy encoding of a star-like neuron network composed of the Wang-Zhang model induced by compressing a sphere into a fingertip

## TL;DR

This study explores how a star-like neuron network simulates fingertip compression, revealing how energy and synchronization work in tactile perception.

## Contribution

A novel star-like neuron network model is proposed to study energy encoding and synchronization during fingertip compression.

## Key findings

- The star-like network achieves remote synchronization between central and peripheral neurons.
- Central neurons consume more energy due to signal integration and direct compression.
- An optimal number of network layers balances energy consumption and information processing efficiency.

## Abstract

The information transmission of the tactile system is closely related to neuron network dynamics and energy metabolism. However, the correlation mechanism between neuron encoding and energy consumption for fingertip under compression remains unclear. In this study, a star-like neuron network is constructed using the Wang-Zhang model as the node, and it is combined with a contact mechanics model to simulate the phenomenon when a sphere being compressed into the fingertip. The remote synchronization characteristics is explored via average maximum correlation coefficient and Kuramoto order parameter, and energy encoding rules of the network are discussed. The results show that the star-like network can achieve remote synchronization between central and peripheral neurons. The energy consumption of central neurons is much higher than that of peripheral neurons due to signal integration and direct compression. The neuron energy consumption exhibits a spatial distribution of “high in the center and low in the periphery”. It is found that there is an optimal value for the number of network layers, at which energy consumption and information processing efficiency reach a balance. This study reveals the neurometabolic mechanism of tactile perception and provides a new theoretical reference for the study of tactile neuronal encoding.