Research on a new impulse neuron model and its neural network / 国际生物医学工程杂志

ABSTRACT

Objective:To study a new type of impulse neuron model and its network, describe its modeling method, and verify its performance by computer simulation.Methods:Based on full consideration of biological adaptability (activation potential threshold and refractory period switch) and its dynamic regulation mechanism for the generation and transmission of spike discharge pulses, a post-potential multi-channel filter was introduced in the new impulse neuron model. The filter can realize the dynamic adjustment of output current and neuron synaptic strength. An error back-propagation (BP) learning algorithm based on adaptive least mean square (LMS) was proposed, and applied to the regulation of spike discharge neural networks.Results:Under spontaneous noise, the pulsation interval signal histogram of the new impulse neuron model satisfies Poisson distribution. Through the simple connection of two new pulsed neurons, a variety of complex spike discharge patterns can be formed. The new impulse neuron model has the characteristics of spontaneous intrinsic noise, can form complex periodic spike discharge patterns. For input noise control, the refractory period and threshold potential adaptability parameters of the new impulse neuron model has good stability. The linear relationship between the stimulation current and the frequency of the spike discharge pulse is good.Conclusions:The proposed new model can generate multiple modes of oscillation and coherent oscillation under the condition of spontaneous noise, which is very similar to biological neurons and can realize complex noise processing. The multi-channel post-synaptic potential filters with different frequency bands can make some post-synaptic potential signals become smooth. The proposed BP learning algorithm based on adaptive LMS can overcome the problem that the error-driven learning algorithm cannot be applied due to the transient characteristics of the spike discharge signal.