Artificial neural network for system identification in neural networks

Abstract

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron of the desert locust, in response to displacement of a sensory organ, the femoral chordotonal organ, that monitors movements of the tibia relative to the femur of the leg. The aim of the study was twofold: first to determine the potential value of ANNs as tools to model and investigate neural networks, and second to quantify the variability of the responses of the same identified neuron across individual animals using ANNs. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results indicate that ANNs are significantly better than Wiener models in predicting neural responses. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model.