Research On Neuron Construction Method Based On Non-associative Learning Mechanism

With the rapid development of artificial intelligence,artificial neural network,as an important branch of artificial intelligence,has been deeply concerned by scholars at home and abroad.As the basic subject of artificial neural network,neural computation plays an important role in the development of artificial neural networks.Most of the existing artificial neural networks are established by simulating the synaptic plasticity of biological neurons and the network topology between cells.The simulation process ignores the advanced learning functions of biological neurons and greatly attenuates the advanced processing capabilities of neuron models.In this paper,the topic was based on the non-associative learning mechanism of biological neurons.First,it simplified the description of the habituation mechanism,dishabituation mechanism and habituation mechanism of long-and short-term memory habituation.Secondly,combined with the McCulloch-Pitts(M-P)neuron model,we established three learning neuron mathematical models with non-associative learning ability—habituation learning neurons,dishabituation learning neurons,and long-and short-term memory habituation neurons.Finally,simulation experiments verified that the three neuron models have similar non-associative learning abilities to biological neurons.The habituation CIFAR-10 deep convolutional network designed by the topic is obtained by embedding the habituation learning neuron model into the CIFAR-10 deep convolutional network.The training results of CIFAR-10 deep convolution network and habituation CIFAR-10 deep convolution network under TensorFlow framework are compared.The comparison results show that the training effect of habituation CIFAR-10 deep convolution network is better.The habituation learning mechanism optimizes the performance of convolutional neural networks.