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

Authors:
Jonghan Shin;Christof Koch;Rodney Douglas
Affiliations:
Computationand Neural Systems Program,California Institute of Technology,Pasadena, CA 91125, U.S.A.;Computationand Neural Systems Program,California Institute of Technology,Pasadena, CA 91125, U.S.A.;Institut für Neuroinformatik, UNI/ETH, Zu¨rich, Switzerland
Venue:
Neural Computation
Year:
1999

Citing 7
Cited 0

Adaptive signal processing

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Methods in neuronal modeling: From synapses to networks
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Analysis of neuron models with dynamically regulated conductances

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An analog memory circuit for spiking silicon neurons

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Spikes: exploring the neural code

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Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)

Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)

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Abstract

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 an analog VLSI-designed silicon neuron.