Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron
Category
Journal Article
Authors
Ambard, M, Rotter, S
Year
2012
Title
Support vector machines for spike pattern classification with a leaky integrate-and-fire neuron
Journal / book / conference
Frontiers in Computational Neuroscience
Abstract
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
Keywords
machine learning, synaptic kernel, supervised learning rule, Tempotron, linear separation