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. The paper describes the integration of brain-inspired systems to perform audiovisual pattern recognition tasks. Individual sensory pathways as well as the integrative modules are implemented using a fast version of spiking neurons grouped in evolving spiking neural network (ESNN) architectures capable of lifelong adaptation. We design a new crossmodal integration system, where individual modalities can influence others before individual decisions are made, fact that resembles some characteristics of the biological brains. The system is applied to the person authentication problem. Preliminary results show that the integrated system can improve the accuracy in many operation points as well as it enables a range of multi-criteria optimizations.