A spiking half-cognitive model for classification
Article
Huyck, C. and Kulkarni, R. 2018. A spiking half-cognitive model for classification. Connection Science. 30 (3), pp. 285-305. https://doi.org/10.1080/09540091.2018.1443317
Type | Article |
---|---|
Title | A spiking half-cognitive model for classification |
Authors | Huyck, C. and Kulkarni, R. |
Abstract | 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 |
Research Group | Artificial Intelligence group |
Publisher | Taylor & Francis (Routledge) |
Journal | Connection Science |
ISSN | 0954-0091 |
Publication dates | |
Online | 26 Feb 2018 |
03 Jul 2018 | |
Publication process dates | |
Deposited | 23 Feb 2018 |
Accepted | 22 Jan 2018 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | 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 |
Digital Object Identifier (DOI) | https://doi.org/10.1080/09540091.2018.1443317 |
Language | English |
https://repository.mdx.ac.uk/item/87782
Download files
65
total views8
total downloads0
views this month0
downloads this month