Nerve Equivalent Circuit Synapse Modeling for Signal Processing
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Solution Overview
Problem
Existing nerve equivalent circuits fail to faithfully reproduce the ignition phenomenon of nerve cells and do not consider the mechanism of synapses, making it difficult to realize information processing functions and signal transmission among nerve cells.
Innovation Solution
A synapse equivalent circuit and nerve cell body equivalent circuit are designed with specific formulas and circuit configurations to simulate the electric characteristics of synapses and nerve cell bodies, incorporating input/output relationships and ion channel equivalents to accurately replicate physiological functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Hodgkin Huxley equations are used to describe nerve cell membrane, then the electric equivalent circuit can be established, but the mechanism of synapses is not taken into consideration and information processing function cannot be realized
Solution Approach 1:
The invention divides the nerve cell into three separate equivalent circuits: nerve cell body equivalent circuit, synapse equivalent circuit, and axon equivalent circuit. This segmentation allows each component to be modeled independently with appropriate complexity, enabling synapse mechanisms to be incorporated without overwhelming the entire system.
Solution Approach 2:
The invention introduces synapse equivalent circuits as intermediary components between nerve cell bodies. These synapse circuits act as mediators that receive signals from presynaptic cells, process them through transmitter substance mechanisms, and transmit to postsynaptic cells, thereby enabling information processing function while maintaining overall system reliability.
2Ease of operation
If Patent Document 1 technique is used to output pulse from axon circuit, then pulse transmission can be achieved, but the circuit cannot provide arithmetic function like nerve cells because electric potential never falls
Solution Approach 1:
The invention introduces dynamic elements including capacitors that charge and discharge, and voltage-dependent resistors that change resistance based on voltage thresholds. These dynamic components enable the circuit to exhibit falling potential phases, allowing it to perform arithmetic operations such as integration and threshold detection, thereby gaining adaptability while maintaining signal transmission capability.
Solution Approach 2:
The invention utilizes parameter changes in circuit components, particularly the voltage-dependent resistance of ion channels and the charging/discharging characteristics of capacitors. By changing resistance and capacitance parameters dynamically, the circuit can switch between signal transmission mode and arithmetic processing mode, enabling both functions.
3Ease of manufacture
If sigmoid function is employed in neural network units, then the network can be constructed, but they are not dynamic systems and cannot control dynamic objects
Solution Approach 1:
The invention replaces the static mathematical sigmoid function with a dynamic electrical circuit model that includes capacitors, resistors, and voltage sources. This substitution transforms the static neural network unit into a dynamic system that can respond to changing inputs over time, enabling control of dynamic objects while maintaining ease of network construction through modular circuit design.
4Productivity
If existing neural networks are used, then algorithms can be obtained, but they do not involve use of characteristics of actual nerves and cannot faithfully reproduce electric characteristics
Solution Approach 1:
The invention creates accurate electrical circuit copies of actual nerve cell components, including ion channels with voltage-dependent resistance, synaptic terminals with transmitter substance release mechanisms, and axon membranes with action potential generation. These circuit copies faithfully reproduce the electric characteristics of actual nerves while maintaining algorithmic functionality for productivity.
Data Source
AI summary
A nerve equivalent circuit, a synapse equivalent circuit and a cell body equivalent circuit are provided whereby electrical characteristics in accordance with the physiological functions and physical structures of nerve cells are reproduced. A nerve equivalent circuit simulating the electrical characteristics of nerve cells wherein an input signal fin(t) and an output signal fout(t) satisfies the relationship represented by [Numerical formula 11], wherein kP, kI and TI are each a definite constant number, N represents the total number of synapses, M represents the total number of the kinds of the first transmitters carried by the synapses, and L represents the total number of the kinds of the second transmitters carried by the synapses.