Reading Neural Encodings using Phase Space Methods

Henry D. I. Abarbanel and Evren Tumer

Chapter 1 in Perspectives and Problems in Nolinear Science, 2003, pp 1-21 from Springer

Abstract: Abstract Environmental signals sensed by nervous systems are often represented in spike trains carried from sensory neurons to higher neural functions where decisions and functional actions occur. Information about the environmental stimulus is contained (encoded) in the train of spikes. We show how to “read” the encoding using state space methods of nonlinear dynamics. We create a mapping from spike signals which are output from the neural processing system back to an estimate of the analog input signal. This mapping is realized locally in a reconstructed state space embodying both the dynamics of the source of the sensory signal and the dynamics of the neural circuit doing the processing. We explore this idea using a Hodgkin—Huxley conductance based neuron model and input from a low dimensional dynamical system, the Lorenz system. We show that one may accurately learn the dynamical input/output connection and estimate with high precision the details of the input signals from spike timing output alone. This form of “reading the neural code” has a focus on the neural circuitry as a dynamical system and emphasizes how one interprets the dynamical degrees of freedom in the neural circuit as they transform analog environmental information into spike trains.

Keywords: Input Signal; Spike Train; Input Current; Lorenz System; Interspike Interval (search for similar items in EconPapers)
Date: 2003
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-21789-5_1

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DOI: 10.1007/978-0-387-21789-5_1

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