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.


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


Scikit; Pynn; NEST
Full Text: DOI


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