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Dendritic Synchrony and Transient Dynamics in a Coupled Oscillator Model of the Dopaminergic Neuron
Journal article   Peer reviewed

Dendritic Synchrony and Transient Dynamics in a Coupled Oscillator Model of the Dopaminergic Neuron

G.S Medvedev, C.J Wilson, J.C Callaway and N Kopell
Journal of computational neuroscience, v 15(1)
Jul 2003
PMID: 12843695

Abstract

Human Genetics Neurology Neurosciences Biomedicine basal ganglia transient dynamics synchronization Theory of Computation dopaminergic neuron coupled oscillator model
Transient increases in spontaneous firing rate of mesencephalic dopaminergic neurons have been suggested to act as a reward prediction error signal. A mechanism previously proposed involves subthreshold calcium-dependent oscillations in all parts of the neuron. In that mechanism, the natural frequency of oscillation varies with diameter of cell processes, so there is a wide variation of natural frequencies on the cell, but strong voltage coupling enforces a single frequency of oscillation under resting conditions. In previous work, mathematical analysis of a simpler system of oscillators showed that the chain of oscillators could produce transient dynamics in which the frequency of the coupled system increased temporarily, as seen in a biophysical model of the dopaminergic neuron. The transient dynamics was shown to be consequence of a slow drift along an invariant subset of phase space, with rate of drift given by a Lyapunov function. In this paper, we show that the same mathematical structure exists for the full biophysical model, giving physiological meaning to the slow drift and the Lyapunov function, which is shown to describe differences in intracellular calcium concentration in different parts of the cell. The duration of transients was long, being comparable to the time constant of calcium disposition. These results indicate that brief changes in input to the dopaminergic neuron can produce long lasting firing rate transients whose form is determined by intrinsic cell properties.

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Domestic collaboration
Web of Science research areas
Mathematical & Computational Biology
Neurosciences
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