Shin, Jonghan and Koch, Christof and Douglas, Rodney (1999) Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current. Neural Computation, 11 (8). pp. 1893-1913. ISSN 0899-7667. http://resolver.caltech.edu/CaltechAUTHORS:20111208-084225679
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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 neuronś firing dynamics. This principle is shown to work in an analog VLSI-designed silicon neuron.
|Additional Information:||© 1999 Massachusetts Institute of Technology. Received December 1, 1997; accepted August 8, 1998. Posted Online March 13, 2006. We thank Christoph Rasche, Dave Lawrence, and Brian Baker for their assistance. This work was supported by the Office of Naval Research, the Center for Neuromorphic Systems Engineering as a part of the National Science Foundation Engineering Research Center Program, the Swiss National Fund SPP programme, and a Colvin fellowship of the Division of Biology at Cal Tech to J. S.|
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|Deposited By:||Tony Diaz|
|Deposited On:||13 Dec 2011 19:14|
|Last Modified:||26 Dec 2012 14:35|
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