Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed

Evolving spiking neural networks: A Survey

This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its introduction in 2006 as a further extension of the ECoS paradigm introduced by Kasabov in 1998. We summarize the functioning of the method, discuss several of its extensions and present a number of applications in which the eSNN method was employed. We focus especially on some proposed extensions that allow the processing of spatio-temporal data and for feature and parameter optimisation of eSNN models to achieve better accuracy on classification/prediction problems and to facilitate new knowledge discovery. Finally, some open problems are discussed and future directions highlighted

Computing with Spiking Neuron Networks

International audienceSpiking Neuron Networks (SNNs) are often referred to as the 3rd generation ofneural networks. Highly inspired from natural computing in the brain andrecent advances in neurosciences, they derive their strength and interest from anaccurate modeling of synaptic interactions between neurons, taking into account thetime of spike firing. SNNs overcome the computational power of neural networksmade of threshold or sigmoidal units. Based on dynamic event-driven processing,they open up new horizons for developing models with an exponential capacity ofmemorizing and a strong ability to fast adaptation. Today, the main challenge is todiscover efficient learning rules that might take advantage of the specific featuresof SNNs while keeping the nice properties (general-purpose, easy-to-use, availablesimulators, etc.) of traditional connectionist models. This chapter relates the history of the “spiking neuron” in Section 1 and summarizes the most currently-in-usemodels of neurons and synaptic plasticity in Section 2. The computational power ofSNNs is addressed in Section 3 and the problem of learning in networks of spikingneurons is tackled in Section 4, with insights into the tracks currently explored forsolving it. Finally, Section 5 discusses application domains, implementation issuesand proposes several simulation frameworks