Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron
Catégorie
Journal Article
Auteurs
Ambard, M, Rotter, S
Année
2012
Titre
Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron
Journal / Livre / Conférence
Frontiers in Computational Neuroscience
Résumé
Spike pattern classification is a key topic in machine learning, computational neuroscience, and electronic device design. Here, we offer a new supervised learning rule based on Support Vector Machines (SVM) to determine the synaptic weights of a leaky integrate-and-fire (LIF) neuron model for spike pattern classification. We compare classification performance between this algorithm and other methods sharing the same conceptual framework. We consider the effect of postsynaptic potential (PSP) kernel dynamics on patterns separability, and we propose an extension of the method to decrease computational load. The algorithm performs well in generalization tasks. We show that the peak value of spike patterns separability depends on a relation between PSP dynamics and spike pattern duration, and we propose a particular kernel that is well-suited for fast computations and electronic implementations.
Issue
6
Volume
78
Mots-clés
machine learning, synaptic kernel, supervised learning rule, Tempotron, linear separation