# Computational Advances in Taste Perception: From Ion Channels to Neural Coding Source: [Computational Advances in Taste Perception: From Ion Channels to Neural Coding]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:22.443Z Authors: Vladimir A. Lazovsky, Sergey V. Stasenko, Victor B. Kazantsev Published: 2025-09-16T08:16:13.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()