# Computational Advances in Taste Perception: From Ion Channels and Taste Receptors to Neural Coding

## TL;DR

The paper introduces a detailed and efficient computational model of taste perception that simulates how taste is processed from receptors to neural activity.

## Contribution

The novel contribution is a multiscale model integrating biophysical and computational elements to simulate taste perception in real-time.

## Key findings

- The model generates distinct spiking patterns for different taste qualities and mixtures.
- It combines Hodgkin–Huxley and Izhikevich models with realistic synaptic plasticity for efficient simulation.
- The approach supports neuromorphic sensors requiring real-time and energy-efficient operation.

## Abstract

We present a multiscale model of taste that is both biophysically faithful and computationally efficient, enabling end-to-end simulation from receptor transduction to network-level coding. The novelty lies in coupling Hodgkin–Huxley taste receptor cells with Goldman–Hodgkin–Katz ion currents and modality-specific receptors (T1R/T2R, ENaC), to an Izhikevich spiking network equipped with realistic glutamatergic synapses and spike-timing-dependent plasticity. Training combines spike synchrony and a genetic approach in order to reach both globally optimized network structure and biomorphic synaptic plasticity. This hybrid design yields distinct, sparse spiking “fingerprints” for taste qualities and mixtures, and provides a practical foundation for neuromorphic gustatory sensors that require real-time, energy-efficient operation.