Computational Advances in Taste Perception: From Ion Channels to Neural Coding
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918152193966080 |
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| author | Lazovsky, Vladimir A. Stasenko, Sergey V. Kazantsev, Victor B. |
| author_facet | Lazovsky, Vladimir A. Stasenko, Sergey V. Kazantsev, Victor B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00010 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computational Advances in Taste Perception: From Ion Channels to Neural Coding Lazovsky, Vladimir A. Stasenko, Sergey V. Kazantsev, Victor B. Neurons and Cognition 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. |
| title | Computational Advances in Taste Perception: From Ion Channels to Neural Coding |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2510.00010 |