title: Seven neurons memorizing sequences of alphabetical images via spike-timing dependent plasticity.
abstract: An artificial neural network, such as a Boltzmann machine, can be trained with the Hebb rule so that it stores static patterns and retrieves a particular pattern when an associated cue is presented to it. Such a network, however, cannot effectively deal with dynamic patterns in the manner of living creatures. Here, we design a dynamic Boltzmann machine (DyBM) and a learning rule that has some of the properties of spike-timing dependent plasticity (STDP), which has been postulated for biological neural networks. We train a DyBM consisting of only seven neurons in a way that it memorizes the sequence of the bitmap patterns in an alphabetical image "SCIENCE" and its reverse sequence and retrieves either sequence when a partial sequence is presented as a cue. The DyBM is to STDP as the Boltzmann machine is to the Hebb rule.
authors.affiliation: IBM, IBM Research - Tokyo, Tokyo, 103-8510, Japan.
authors.forename: Takayuki
authors.initials: T
authors.lastname: Osogami
authors.affiliation: IBM, IBM Research - Tokyo, Tokyo, 103-8510, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Otsuka
mesh_terms: D000200:Action Potentials; D003463:Cues; D007858:Learning; D008959:Models, Neurological; D009415:Nerve Net; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission