A real-time, event-driven neuromorphic system for goal-directed attentional selection

Authors:
Francesco Galluppi;Kevin Brohan;Simon Davidson;Teresa Serrano-Gotarredona;José-Antonio Pérez Carrasco;Bernabé Linares-Barranco;Steve Furber
Affiliations:
School of Computer Science, The University of Manchester, United Kingdom;School of Electronic and Electrical Engineering, The University of Manchester, United Kingdom;School of Computer Science, The University of Manchester, United Kingdom;Instituto de Microelectrónica de Sevilla, Sevilla, Spain;Departemento de Teoría de la Señal y Comunicaciones, Universidad de Seville, Sevilla, Spain;Instituto de Microelectrónica de Sevilla, Sevilla, Spain;School of Computer Science, The University of Manchester, United Kingdom
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

Computation with spiking neurons takes advantage of the abstraction of action potentials into streams of stereotypical events, which encode information through their timing. This approach both reduces power consumption and alleviates communication bottlenecks. A number of such spiking custom mixed-signal address event representation (AER) chips have been developed in recent years. In this paper, we present i) a flexible event-driven platform consisting of the integration of a visual AER sensor and the SpiNNaker system, a programmable massively parallel digital architecture oriented to the simulation of spiking neural networks; ii) the implementation of a neural network for feature-based attentional selection on this platform.