A spiking half-cognitive model for classification

This paper describes a spiking neural network that learns classes. Following a classic Psychological task, the model learns some types of classes better than other types, so the net is a spiking cognitive model of classification. A simulated neural system, derived from an existing model, learns natural kinds, but is unable to form sufficient attractor states for all of the types of classes. An extension of the model, using a combination of singleton and triplets of input features, learns all of the types. The models make use of a principled mechanism for spontaneous firing, and a compensatory Hebbian learning rule. Combined, the mechanisms allow learning to spread to neurons not directly stimulated by the environment. The overall network learns the types of classes in a fashion broadly consistent with the Psychological data. However, the order of speed of learning the types is not entirely consistent with the
Psychological data, but may be consistent with one of two Psychological systems a given person possesses. A Psychological test of this hypothesis is proposed.

This is an Accepted Manuscript of an article published by Taylor & Francis in Connection Science on 26/02/18, available online: http://www.tandfonline.com/10.1080/09540091.2018.1443317