Energy flow accounts for the adaptive property of functional synapses

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.

Fractional-order memristive neural synaptic weighting achieved by pulse-based fracmemristor bridge circuit

We propose a novel circuit for the fractional-order memristive neural synaptic weighting(FMNSW).The introduced circuit is different from the majority of the previous integer-order approaches and offers important advantages. Since the concept of memristor has been generalized from the classic integer-order memristor to the fractional-order memristor(fracmemristor), a challenging theoretical problem would be whether the fracmemristor can be employed to implement the fractional-order memristive synapses or not. In this research, characteristics of the FMNSW, realized by a pulse-based fracmemristor bridge circuit, are investigated. First, the circuit configuration of the FMNSW is explained using a pulse-based fracmemristor bridge circuit. Second, the mathematical proof of the fractional-order learning capability of the FMNSW is analyzed. Finally, experimental work and analyses of the electrical characteristics of the FMNSW are presented. Strong ability of the FMNSW in explaining the cellular mechanisms that underlie learning and memory, which is superior to the traditional integer-order memristive neural synaptic weighting, is considered a major advantage for the proposed circuit.