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Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, U.S.A.

Christof Koch

Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, U.S.A.

Rodney Douglas

Institut für Neuroinformatik, UNI/ETH, Zürich, Switzerland

It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neurons' firing dynamics. This principle is shown to work in ananalog VLSI-designed silicon neuron.

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