title: Supervised learning with decision margins in pools of spiking neurons.
abstract: Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.
authors.affiliation: UCL Institute of Neurology and UCL Department of Neuroscience, Physiology, and Pharmacology, London, UK, lemouel@clipper.ens.fr.
authors.forename: Charlotte
authors.initials: C
authors.lastname: Le Mouel
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Harris
authors.forename: Pierre
authors.initials: P
authors.lastname: Yger
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D060388:Support Vector Machine; D013569:Synapses / Q000502:physiology