title: Pattern recall in networks of chaotic neurons.
abstract: This research investigates the potential utility of chaotic dynamics in neural information processing. A novel chaotic spiking neural network model is presented which is composed of non-linear dynamic state (NDS) neurons. The activity of each NDS neuron is driven by a set of non-linear equations coupled with a threshold based spike output mechanism. If time-delayed self-connections are enabled then the network stabilises to a periodic pattern of activation. Previous publications of this work have demonstrated that the chaotic dynamics which drive the network activity ensure that an extremely large number of such periodic patterns can be generated by this network. This paper presents a major extension to this model which enables the network to recall a pattern of activity from a selection of previously stabilised patterns.
authors.affiliation: Department of Computing, Oxford Brookes University, Wheatley Campus, Oxford, United Kingdom. ntcrook@brookes.ac.uk
authors.forename: Nigel
authors.initials: N
authors.lastname: Crook
authors.forename: Wee Jin
authors.initials: WJ
authors.lastname: Goh
authors.forename: Mohammad
authors.initials: M
authors.lastname: Hawarat
mesh_terms: D000465:Algorithms; D008390:Markov Chains; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D049490:Systems Biology