Related Experiment Videos
Detecting and estimating signals in noisy cable structures, II: information theoretical analysis
1Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA 91125, USA.
Neural Computation
|December 1, 1999
Summary
This study quantifies information loss in neurons due to noise. We found that neuronal membrane noise limits information transfer, suggesting dendritic nonlinearities may enhance signal processing.
Area of Science:
- Computational Neuroscience
- Information Theory
- Biophysics
Background:
- Classical cable theory analyzes signal attenuation in neurons.
- Information theory offers a new framework for understanding neuronal signal processing.
- Neuronal noise sources impact signal fidelity during propagation.
Purpose of the Study:
- To quantify information loss in neuronal signal propagation due to various noise sources.
- To analyze neuronal signal processing using an information-theoretical approach.
- To investigate the role of noise in limiting dendritic information transfer.
Main Methods:
- Utilized a stochastic version of the linear one-dimensional cable equation.
- Derived closed-form expressions for membrane potential fluctuations.
- Analyzed signal estimation and detection paradigms using coding fraction and mutual information.
Main Results:
- Quantified information loss from thermal noise, channel noise, and synaptic noise.
- Demonstrated that noise accumulation limits information transfer along dendrites.
- Showcased the utility of coding fraction and mutual information in assessing neuronal information processing.
Conclusions:
- Neuronal membrane noise significantly impacts information transfer efficiency.
- Dendritic nonlinearities may play a crucial role in overcoming noise and enhancing information transfer.
- The length of apical dendrites might be constrained by noise-induced information loss.