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Energy flow accounts for the adaptive property of functional synapses

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Abstract

In the presence of external stimuli and electromagnetic radiation (EMR), biological neurons can exhibit different firing patterns and switch to appropriate firing modes because of intrinsic self-adaption. Coupling to memristive synapses can discern the EMR effect, and memristive synapses connecting to neurons can be effectively regulated by external physical fields. From a dynamical viewpoint, the appropriate setting for memristive synapse intensity can trigger changes in neural activities; however, the biophysical mechanism of adaptive regulation in the memristive biophysical neuron has not been clarified. Herein, a memristor is used to control a simple neural circuit by generating a memristive current, and an equivalent memristive neuron model is obtained. A single firing mode can be stabilized in the absence of EMR, while multiple firing modes occur in the neuron under EMR. The gain of the memristive synaptic current is dependent on the energy flow, and the shunted energy flow in the memristive channel can control the energy ratio between the electric field and magnetic field. The growth and enhancement of the memristive synapse depend on the energy flow across the memristive channel. The memristive synapse is enhanced when its field energy is below the threshold, and it is suppressed when its field energy is above the threshold. These results explain why and how multiple firing modes are induced and controlled in biological neurons. Furthermore, the self-adaption property of memristive neurons was also clarified. Thus, the control of energy flow in the memristive synapse can effectively regulate the membrane potentials, and neural activities can be effectively controlled to select suitable body gaits.

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Authors and Affiliations

  1. Department of Physics, Lanzhou University of Technology, Lanzhou, 730050, China

    FuQiang Wu & Jun Ma

  2. School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, China

    FuQiang Wu

  3. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    YiTong Guo & Jun Ma

  4. School of Science, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jun Ma

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  1. FuQiang Wu
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  2. YiTong Guo
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Correspondence to Jun Ma.

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This work was supported by the National Natural Science Foundation of China (Grant No. 12072139).

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Wu, F., Guo, Y. & Ma, J. Energy flow accounts for the adaptive property of functional synapses. Sci. China Technol. Sci. 66, 3139–3152 (2023). https://doi.org/10.1007/s11431-023-2441-5

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