Learning in a neural network model in real time using real world stimuli

MA Sánchez-Montañés, P König, PFMJ Verschure - Neurocomputing, 2001 - Elsevier
Neurocomputing, 2001Elsevier
In this paper we present a model of the auditory system that is trained using real-world
stimuli and running in real-time. The system consists of different sound sources, a
microphone, an A/D board, a peripheral auditory system implemented in software and a
central network of spiking neurons. The synapses formed by peripheral neurons on the
central ones are subject to synaptic plasticity. We implemented a learning rule that depends
on the precise temporal relation of pre-and post-synaptic action potentials. We demonstrate …
In this paper we present a model of the auditory system that is trained using real-world stimuli and running in real-time. The system consists of different sound sources, a microphone, an A/D board, a peripheral auditory system implemented in software and a central network of spiking neurons. The synapses formed by peripheral neurons on the central ones are subject to synaptic plasticity. We implemented a learning rule that depends on the precise temporal relation of pre- and post-synaptic action potentials. We demonstrate that this mechanism allows the development of receptive fields combining learning in real-time, learning with few stimulus presentations and robust learning in the presence of large imbalances in the probability of occurrence of individual stimuli.
Elsevier
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