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Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, U.S.A.
Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, U.S.A.
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, weare 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 tothe 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.
This article has been cited by other articles:
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  B. Doiron, A.-M. M. Oswald, and L. Maler Interval Coding. II. Dendrite-Dependent Mechanisms J Neurophysiol, April 1, 2007; 97(4): 2744 - 2757. [Abstract] [Full Text] [PDF]
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F. S. Chance Receiver Operating Characteristic (ROC) Analysis for Characterizing Synaptic Efficacy J Neurophysiol, February 1, 2007; 97(2): 1799 - 1808. [Abstract] [Full Text] [PDF]
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P. Crotty, T. Sangrey, and W. B Levy Metabolic Energy Cost of Action Potential Velocity J Neurophysiol, September 1, 2006; 96(3): 1237 - 1246. [Abstract] [Full Text] [PDF]
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 G. A Jacobson, K. Diba, A. Yaron-Jakoubovitch, Y. Oz, C. Koch, I. Segev, and Y. Yarom Subthreshold voltage noise of rat neocortical pyramidal neurones J. Physiol., April 1, 2005; 564(1): 145 - 160. [Abstract] [Full Text] [PDF]
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 K. Diba, H. A. Lester, and C. Koch Intrinsic Noise in Cultured Hippocampal Neurons: Experiment and Modeling J. Neurosci., October 27, 2004; 24(43): 9723 - 9733. [Abstract] [Full Text] [PDF]
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 M. Rudolph and A. Destexhe Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes Neural Comput., November 1, 2003; 15(11): 2577 - 2618. [Abstract] [Full Text] [PDF]
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G. Svirskis, V. Kotak, D. H. Sanes, and J. Rinzel Enhancement of Signal-to-Noise Ratio and Phase Locking for Small Inputs by a Low-Threshold Outward Current in Auditory Neurons J. Neurosci., December 15, 2002; 22(24): 11019 - 11025. [Abstract] [Full Text] [PDF]
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A. Manwani and C. Koch Detecting and Estimating Signals over Noisy and Unreliable Synapses: Information-Theoretic Analysis Neural Comput., January 1, 2001; 13(1): 1 - 33. [Abstract] [Full Text]
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