Asynchronous Neural Network for Biological CPG Simulation
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Solution Overview
Problem
Current technologies lack efficient methods to replicate the complex coordination of biological central pattern generators (CPGs) for motor activities like heartbeats and breathing, which are essential for developing prosthetic devices and medical therapies, as they struggle to accurately simulate the spatio-temporal sequences of electrical pulses and adaptability to stimuli.
Innovation Solution
A non-biological asynchronous neural network system comprising multiple interconnected CMOS-based neurons with mutually inhibitory and excitatory links, using differential current amplifiers to control conductance and mimic biological neuron responses, allowing for the generation of spatio-temporal sequences of rhythmic electric pulses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current technologies are used to replicate biological CPGs, then the basic function of generating rhythmic patterns can be achieved, but the accuracy in simulating spatio-temporal sequences of electrical pulses and adaptability to stimuli is insufficient
Solution Approach 1:
The patent creates an artificial neural network that copies the structure and function of biological central pattern generators. Multiple neurons are interconnected with mutually inhibitory links to replicate the biological CPG's ability to generate spatio-temporal sequences of electrical pulses. The system includes excitatory and inhibitory synapses that mimic biological neuron behavior, enabling accurate simulation of rhythmic motor patterns while maintaining adaptability to external stimuli.
Solution Approach 2:
The artificial neural network implements dynamic conductance values for synaptic links that can be adjusted in real-time. The conductance of excitatory and inhibitory synapses is controlled by control signals, allowing the system to adapt its response characteristics dynamically. This enables the network to modify its spatio-temporal pulse sequences in response to varying stimuli, replicating the adaptability of biological CPGs.
2Productivity
If mutually inhibitory links are used to coordinate neuron firing, then spatio-temporal sequences can be generated, but the system complexity increases
Solution Approach 1:
The neural network is segmented into multiple independent neurons, each with its own membrane voltage control and firing threshold. These neurons are interconnected through standardized synapse structures with可控 conductance. This modular segmentation allows complex coordination functions to be achieved through simple local interactions, managing system complexity while maintaining high productivity in generating coordinated motor patterns.
Solution Approach 2:
The system uses parameter changes in synaptic conductance values to control network behavior. By adjusting the conductance of excitatory and inhibitory links through control signals, the network can generate different spatio-temporal sequences without changing its structural complexity. This allows flexible coordination of motor activities while maintaining a fixed architectural design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables the accurate simulation of biological CPGs, allowing for coordinated motor activities, adaptability to stimuli, and synchronization with body rhythms, improving prosthetic devices and medical therapies by providing efficient and scalable coordination of heartbeats and other motor functions.
Implementation Method 1
The differential current amplifier can receive a control signal as input to control the conductance of the synapse
Implementation Method 2
The conductance of the neuron membrane channels can control the membrane voltage of a neuron
Data Source
Figure 1(a)~1(c)
Figure 2
Figure 3
AI summary
A non-biological asynchronous neural network system comprising multiple neurons to receive respective input signals representing an input stimulus for the network, supply an output signal representing a spatio-temporal sequence of rhythmic electric pulses to an external system, wherein respective ones of the multiple neurons are connected using multiple mutually inhibitory links.