Development of new deep neural network architecture based on hodgkin huxley model

Abstract

Abstract There have been many studies to simulate the functions of the brain from the neural level to the cognitive function. Since the deep neural network has shown high performance for many tasks, researches for artificial intelligence are focused on deep neural networks. In terms of neurophysiology, the configuration of nodes used for a deep neural network is too simplistic to represent real neurons. Therefore, simulation of the brain activities in response to stimuli from the external environment using the deep neural networks is inappropriate. For that reason, our group makes a new deep neural network configuration based on the Hodgkin Huxley model. The Hodgkin Huxley model has high biological plausibility for representing the firing pattern of real neurons. Therefore, the deep neural network architecture based on the Hodgkin Huxley model successfully mimics human cognitive process and is suitable for simulation assuming external stimuli. The most important point for constructing the new architecture is setting the proper learning rule that updates the synaptic information for solving cognitive problems. We used the idea of reinforcement learning so that each neuron composing the neural layer does the synaptic information update as an agent. This learning method enables to increase the computing time linearly proportional to the number of neurons, unlike convolution neural networks. Also, we introduce an algorithm that optimizes the firing rate of neurons to generate synaptic information using learning. The algorithm represents the characteristics of brain waves found in brain imaging techniques. We trained the new architecture to classify 10 class mnist input data. The classification performance is very lower than other existing deep neural network architecture. However, the new architecture shows the potential to connect from neuronal level to cognitive level. Keywords: deep neural network, Hogkin Huxely, single neuron model, cognitive model