Automatic Estimation of the Dynamics of Channel Conductance Using a Recurrent Neural Network. (27th October 2008)

• Record Type: Journal Article
• Authors: Takahashi, Masaaki; Natsume, Kiyohisa
• Other Names: Imada Akira Academic Editor.
• Is Part Of: Advances in artificial neural systems. (2009)
• Publication Date: 2008-10-27
• Subjects: Neural networks (Computer science) -- Periodicals; Neural networks (Computer science); Periodicals; Electronic journals; 006.32
• DOI: 10.1155/2009/724092
• Languages: English
• ISSNs: 1687-7594

Abstract

Abstract : In order to simulate neuronal electrical activities, we must estimate the dynamics of channel conductances from physiological experimental data. However, this approach requires the formulation of differential equations that express the time course of channel conductance. On the other hand, if the dynamics are automatically estimated, neuronal activities can be easily simulated. By using a recurrent neural network (RNN), it is possible to estimate the dynamics of channel conductances without formulating the differential equations. In the present study, we estimated the dynamics of theNa + andK + conductances of a squid giant axon using two different fully connected RNNs and were able to reproduce various neuronal activities of the axon. The reproduced activities were an action potential, a threshold, a refractory phenomenon, a rebound action potential, and periodic action potentials with a constant stimulation. RNNs can be trained using channels other than theNa + andK + channels. Therefore, using our RNN estimation method, the dynamics of channel conductance can be automatically estimated and the neuronal activities can be simulated using the channel RNNs. An RNN can be a useful tool to estimate the dynamics of the channel conductance of a neuron, and by using the method presented here, it is possible to simulate neuronal activities more easily than by using the previous methods.