Supervised learning with decision margins in pools of spiking neurons

作者：Le Mouel Charlotte^*; Harris Kenneth D; Yger Pierre

来源：Journal of Computational Neuroscience, 2014, 37(2): 333-344.

DOI：10.1007/s10827-014-0505-9

• Supervised learning
• Spiking neurons
• Tempotron
• Support vector machine

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.

• 出版日期2014-10