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IJCNN
2007
IEEE

System Identification for the Hodgkin-Huxley Model using Artificial Neural Networks

9 years 4 months ago
System Identification for the Hodgkin-Huxley Model using Artificial Neural Networks
— A single biological neuron is able to perform complex computations that are highly nonlinear in nature, adaptive, and superior to the perceptron model. A neuron is essentially a nonlinear dynamical system. Its state depends on the interactions among its previous states, its intrinsic properties, and the synaptic input it receives. These factors are included in Hodgkin-Huxley (HH) model, which describes the ionic mechanisms involved in the generation of an action potential. This paper proposes training of an artificial neural network to identify and model the physiological properties of a biological neuron, and mimic its input-output mapping. An HH simulator was implemented to generate the training data. The proposed model was able to mimic and predict the dynamic behavior of the HH simulator under novel stimulation conditions; hence, it can be used to extract the dynamics (in vivo or in vitro) of a neuron without any prior knowledge of its physiology. Such a model can in turn be us...
Manish Saggar, Tekin Meriçli, Sari Andoni,
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IJCNN
Authors Manish Saggar, Tekin Meriçli, Sari Andoni, Risto Miikkulainen
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