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Supervised learning with decision margins in pools of spiking neurons. (English) Zbl 1409.92047

Summary: 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 (SVM), 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.

MSC:

92C20 Neural biology
68T05 Learning and adaptive systems in artificial intelligence

Software:

Scikit; Pynn; NEST
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References:

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