Quantized Three-Ion-Channel Neuron Model for Neural Action Potentials

Tasio Gonzalez-Raya1, Enrique Solano1,2,3, and Mikel Sanz1

1Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
2International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Physics Department, Shanghai University, 200444 Shanghai, China
3IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain

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The Hodgkin-Huxley model describes the conduction of the nervous impulse through the axon, whose membrane's electric response can be described employing multiple connected electric circuits containing capacitors, voltage sources, and conductances. These conductances depend on previous depolarizing membrane voltages, which can be identified with a memory resistive element called memristor. Inspired by the recent quantization of the memristor, a simplified Hodgkin-Huxley model including a single ion channel has been studied in the quantum regime. Here, we study the quantization of the complete Hodgkin-Huxley model, accounting for all three ion channels, and introduce a quantum source, together with an output waveguide as the connection to a subsequent neuron. Our system consists of two memristors and one resistor, describing potassium, sodium, and chloride ion channel conductances, respectively, and a capacitor to account for the axon's membrane capacitance. We study the behavior of both ion channel conductivities and the circuit voltage, and we compare the results with those of the single channel, for a given quantum state of the source. It is remarkable that, in opposition to the single-channel model, we are able to reproduce the voltage spike in an adiabatic regime. Arguing that the circuit voltage is a quantum variable, we find a purely quantum-mechanical contribution in the system voltage's second moment. This work represents a complete study of the Hodgkin-Huxley model in the quantum regime, establishing a recipe for constructing quantum neuron networks with quantum state inputs. This paves the way for advances in hardware-based neuromorphic quantum computing, as well as quantum machine learning, which might be more efficient resource-wise.

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Cited by

[1] G. Alvarado Barrios, J. C. Retamal, E. Solano, and M. Sanz, "Analog simulator of integro-differential equations with classical memristors", Scientific Reports 9 1, 12928 (2019).

[2] Lucas Lamata, "Quantum machine learning and quantum biomimetics: A perspective", Machine Learning: Science and Technology 1 3, 033002 (2020).

[3] Lasse Bjørn Kristensen, Matthias Degroote, Peter Wittek, Alán Aspuru-Guzik, and Nikolaj T. Zinner, "An artificial spiking quantum neuron", npj Quantum Information 7 1, 59 (2021).

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The above citations are from Crossref's cited-by service (last updated successfully 2021-10-20 00:22:49) and SAO/NASA ADS (last updated successfully 2021-10-20 00:22:50). The list may be incomplete as not all publishers provide suitable and complete citation data.