Development of a Self-Regulating Evolving Spiking Neural Network for classification problem

作者：Dora S; Subramanian K; Suresh S; Sundararajan N

来源：Neurocomputing, 2016, 171: 1216-1229.

DOI：10.1016/j.neucom.2015.07.086

• Spiking neural network
• Self-regulation
• Pattern classification

This paper presents a new spiking neural network for pattern classification problems, referred to as the Self-Regulating Evolving Spiking Neural (SRESN) classifier, that regulates the learning process of the network. It uses a two layered spiking neural network and the input layer consists of receptive field neurons, which convert a real valued input to spikes using the population coding scheme without any delays. The output layer consists of leaky integrate-and-fire neurons. Since SRESN does not use any delays, the number of network parameters for SRESN is significantly lower than that used by other spiking neural networks, used in this study. During training, the learning algorithm for SRESN, automatically evolves neurons in the output layer based on the training data stream and the current knowledge stored in the network. Depending on the knowledge in the sample and the class specific knowledge stored in the network, it can choose to either add a neuron or update the network parameters or skip learning the sample resulting in self-regulation of the learning process. In case of neuron addition, the weights for the newly added neuron are initialized using a modified rank order scheme which facilitates SRESN for use in online/sequential as well as batch learning modes. The parameter update strategy in SRESN ensures that connections with non-zero postsynaptic potential at the time of the spike are alone updated which helps prevent over training. While evaluating the performance of SRESN, first a study is conducted to assess the impact of various parameters on its performance and establish guidelines to choose suitable values for these parameters. Next, the performance of SRESN, operating in batch mode, is compared with other spiking neural classifiers, including SpikeProp and MuSpiNN, for the UCI benchmark problems of Iris flower classification and Wisconsin breast cancer. Subsequently, the performance of SRFSN in online and batch learning mode is compared with an evolving spiking neural classifier for five benchmark data sets from the UCI machine learning repository. Finally, SRESN is applied to solve the practical problem of Epilepsy detection. The performance comparison clearly indicates that SRESN provides a higher generalization accuracy using fewer network parameters.

• 出版日期2016-1-1