Recovery of hidden information through synaptic dynamics.
The role of synaptic dynamics in processing neural information is investigated in a neural information channel with realistic model neurons having chaotic intrinsic dynamics. Our neuron models are realized in simple analogue circuits, and our synaptic connections are realized both in analogue circuits and through a dynamic clamp program. The information which is input to the first chaotic neuron in the channel emerges partially absent and partially 'hidden'. Part is absent because of the dynamical effects of the chaotic oscillation that effectively acts as a noisy channel. The 'hidden' part is recoverable. We show that synaptic parameters, most significantly receptor binding time constants, can be tuned to enhance the information transmission by the combination of a neuron plus a synapse. We discuss how the dynamics of the synapse can be used to recover 'hidden' information using average mutual information as a measure of the quality of information transport.
Rabinovich
Misha I
MI
Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA 92093-0402, USA.
Pinto
R D
RD
Abarbanel
Henry D I
HD
Tumer
Evren
E
Stiesberg
Gregg
G
Huerta
R
R
Selverston
Allen I
AI
D000818:Animals; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D013569:Synapses / Q000502:physiology*