Volume 9 Supplement 1
Evaluating feedforward spiking neuron networks using a novel decoding strategy
© VanderKraats and Banerjee; licensee BioMed Central Ltd. 2008
Published: 11 July 2008
Investigating how information is represented within a population of model neurons is a primary focus of computational neuroscience research. In feed-forward systems, a fundamentally related question is how this representation changes as it advances through the network. In this letter, we explore the capabilities of several kinds of feed-forward network architectures at transmitting complexly coded information using a large, heterogeneous populations of model neurons. For a suitably elaborate input, we employ a realistic model of the auditory periphery, the Meddis Inner-Hair Cell Model . To interpret the spike train responses for sizeable neuronal populations, we introduce a novel method for decoding based on a discrete version of the reconstruction method . By combining an interspike interval (ISI) representation with support vector machine (SVM) classifiers, we successful decode information from layers of 200 spiral ganglion cells of 20 different types. Furthermore, this method makes no assumptions about the spike train's encoding.
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This article is published under license to BioMed Central Ltd.