Abstract — The applicability of complex networks of spiking neurons as a general purpose machine learning technique remains open. Building on previous work using macroscopic exploration of the parameter space of an (artificial) neural microcircuit, we investigate the possibility of using a liquid state machine to solve two real-world problems: stockpile surveillance signal alignment and spoken phoneme recognition. I.

Abstract. Source separation and speech recognition are very difficult in the context of noisy and corrupted speech. Most conventional techniques need huge databases to estimate speech (or noise) density probabilities to perform separation or recognition. We discuss the potential of perceptive speech analysis and processing in combination with biologically plausible neural networks processors. We illustrate the potential of such non-linear processing of speech on two applications. The first is a source separation system inspired by Auditory Scene Analysis paradigm and the second is a crude spoken digit recogniser. We present preliminary results and discuss them.

Abstract. This paper presents a novel system that performs text-independent speaker authentication using new spiking neural network (SNN) architectures. Each speaker is represented by a set of prototype vectors that is trained with standard Hebbian rule and winner-takes-all approach. For every speaker there is a separated spiking network that computes normalized similarity scores of MFCC (Mel Frequency Cepstrum Coefficients) features considering speaker and background models. Experiments with the VidTimit dataset show similar performance of the system when compared with a benchmark method based on vector quantization. As the main property, the system enables optimization in terms of performance, speed and energy efficiency. A procedure to create/merge neurons is also presented, which enables adaptive and on-line training in an evolvable way.

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Abstract. Spiking Neuron Networks (SNNs) are often referred to as the 3 rd generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developping models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of current connectionist models (such as MLP, RBF or SVM). The present survey relates the history of the “spiking neuron ” and summarizes the most currenlty in use models of neurons and networks, in Section 1. The computational power of SNNs is addressed in Section 2 and the problem of learning in networks of spiking neurons is tackled in Section 3, with insights into the tracks currently explored for solving it. Section 4 reviews the tricks of implementation and discuss several simulation frameworks. Examples of application domains are proposed in Section 5, mainly in speech processing and computer vision, emphasizing the temporal aspect of pattern recognition by SNNs.

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