Computational Advances in Taste Perception: From Ion Channels to Neural Coding
Abstract
Recent advances in computational neuroscience demand models that balance biophysical realism with scalability. We present a hybrid neuron model combining the biophysical fidelity of Hodgkin-Huxley (HH) dynamics for taste receptor cells with the computational efficiency of Izhikevich spiking neurons for large-network simulations. Our framework incorporates biomorphic taste cell models, featuring modality-specific receptor dynamics (T1R/T2R, ENaC, PKD) and Goldman-Hodgkin-Katz (GHK)-driven ion currents to accurately simulate gustatory transduction. Synaptic interactions are modeled via glutamate release kinetics with alpha-function profiles, AMPA receptor trafficking regulated by phosphorylation, and spike-timing-dependent plasticity (STDP) to enforce temporal coding. At the network level, we optimize multiscale learning, leveraging both temporal spike synchrony (van Rossum metrics) and combinatorial population coding (rank-order patterns). This approach bridges single-cell biophysics with ensemble-level computation, enabling efficient simulation of gustatory pathways while retaining biological fidelity.
Cite
@article{arxiv.2510.00010,
title = {Computational Advances in Taste Perception: From Ion Channels to Neural Coding},
author = {Vladimir A. Lazovsky and Sergey V. Stasenko and Victor B. Kazantsev},
journal= {arXiv preprint arXiv:2510.00010},
year = {2025}
}