Manwani, Amit and Koch, Christof (1999) Detecting and Estimating Signals in Noisy Cable Structures, II: Information Theoretical Analysis. Neural Computation, 11 (8). pp. 1831-1873. ISSN 0899-7667 http://resolver.caltech.edu/CaltechAUTHORS:20111207-092921662
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This is the second in a series of articles that seek to recast classical single-neuron biophysics in information-theoretical terms. Classical cable theory focuses on analyzing the voltage or current attenuation of a synaptic signal as it propagates from its dendritic input location to the spike initiation zone. On the other hand, we are interested in analyzing the amount of information lost about the signal in this process due to the presence of various noise sources distributed throughout the neuronal membrane. We use a stochastic version of the linear one-dimensional cable equation to derive closed-form expressions for the second-order moments of the fluctuations of the membrane potential associated with different membrane current noise sources: thermal noise, noise due to the random opening and closing of sodium and potassium channels, and noise due to the presence of “spontaneous” synaptic input. We consider two different scenarios. In the signal estimation paradigm, the time course of the membrane potential at a location on the cable is used to reconstruct the detailed time course of a random, band-limited current injected some distance away. Estimation performance is characterized in terms of the coding fraction and the mutual information. In the signal detection paradigm, the membrane potential is used to determine whether a distant synaptic event occurred within a given observation interval. In the light of our analytical results, we speculate that the length of weakly active apical dendrites might be limited by the information loss due to the accumulated noise between distal synaptic input sites and the soma and that the presence of dendritic nonlinearities probably serves to increase dendritic information transfer.
|Additional Information:||© 1999 Massachusetts Institute of Technology. Received August 14, 1998; accepted March 15, 1999. Posted Online March 13, 2006. This research was supported by NSF, NIMH, and the Sloan Center for Theoretical Neuroscience. We are grateful to the reviewers in helping us improve the quality of this article. We thank our collaborators, Peter Steinmetz and Miki London, for their invaluable suggestions and Idan Segev, Elad Schneidman, Yosef Yarom, Fabrizio Gabbiani, Andreas Andreou, and Pamela Abshire for illuminating discussions. We also acknowledge initial discussions with Bill Bialek and Tony Zador on the use of information theory to understand single-neuron biophysics.|
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|Deposited By:||Tony Diaz|
|Deposited On:||11 Jan 2012 23:06|
|Last Modified:||26 Dec 2012 14:34|
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