Sequential state generation by model neural networks.
Sequential patterns of neural output activity form the basis of many biological processes, such as the cyclic pattern of outputs that control locomotion. I show how such sequences can be generated by a class of model neural networks that make defined sets of transitions between selected memory states. Sequence-generating networks depend upon the interplay between two sets of synaptic connections. One set acts to stabilize the network in its current memory state, while the second set, whose action is delayed in time, causes the network to make specified transitions between the memories. The dynamic properties of these networks are described in terms of motion along an energy surface. The performance of the networks, both with intact connections and with noisy or missing connections, is illustrated by numerical examples. In addition, I present a scheme for the recognition of externally generated sequences by these networks.
Kleinfeld
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D001185:Artificial Intelligence; D005246:Feedback; D008568:Memory / Q000502:physiology; D008962:Models, Theoretical; D009046:Motor Neurons / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009424:Nervous System Physiological Phenomena; D009474:Neurons / Q000502:physiology; D010507:Periodicity; D013997:Time Factors