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Information maximization and stochastic resonance in single neurons

Citation

Stemmler, Martin Bernard (1997) Information maximization and stochastic resonance in single neurons. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechETD:etd-12182007-104908

Abstract

Does the nervous system "tune" itself to perform at peak efficiency? Optimal transmission of information in a single nerve cell occurs when the response is matched to the statistics of naturally occurring stimuli, such that all firing rates are used with equal probability and that redundant temporal correlations in the input are removed. Non-Hebbian, local learning rules are developed to adapt the voltage-dependent ionic conductances in Hodgkin-Huxley models of neurons with the goal of matching the neuron's response to the statistics of natural stimuli. These learning rules allow a nerve cell to maximize the amount of information transmitted about arriving stimuli. At a more detailed level, information transmission in neurons is limited by the noise in the input, defined as the root mean square of the fluctuations in the input. Three different performance measures are shown to scale identically as a function of the noise in simple models of neurons that have both a voltage and current threshold. These performance measures are: the probability of correctly detecting a constant input in a limited time, the signal-to-noise ratio in response to sinusoidal input, and the mutual information between an arbitrarily varying input and the output spike train of the model neuron. Of these, detecting a constant signal is the simplest and most fundamental quantity. For subthreshold signals, the model exhibits stochastic resonance, a non-zero noise amplitude that optimally enhances signal detection. In this case, noise paradoxically does not limit, but instead improves performance. Even though the noise amplitude can dwarf the signal, detection of a weak constant signal using stochastic resonance is still possible when the signal elicits on average only one additional spike. Stochastic resonance could thus play a role in neurobiological sensory systems, where speed is of the utmost importance and averaging over many individual spikes is not possible.

Item Type:Thesis (Dissertation (Ph.D.))
Degree Grantor:California Institute of Technology
Division:Biology
Major Option:Computation and Neural Systems
Thesis Availability:Restricted to Caltech community only
Research Advisor(s):
  • Koch, Christof
Thesis Committee:
  • Unknown, Unknown
Defense Date:1 January 1997
Record Number:CaltechETD:etd-12182007-104908
Persistent URL:http://resolver.caltech.edu/CaltechETD:etd-12182007-104908
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:5049
Collection:CaltechTHESIS
Deposited By: Imported from ETD-db
Deposited On:08 Jan 2008
Last Modified:26 Dec 2012 03:14

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