Capacity Analysis for Integrate-and-Fire Neurons With Descending Action Potential Thresholds

Understanding how a biological neuron works has been a major goal in neuroscience. Under the Poisson-excitation assumption, results from earlier study by Suksompong and Berger on the timing jitter in the leaky integrate-and-fire (LIF) model of neurons are used to determine families of neural thresho...

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Bibliographic Details
Published in:IEEE transactions on information theory 2010-02, Vol.56 (2), p.838-851
Main Authors: Suksompong, P., Berger, T.
Format: Article
Language:English
Subjects:
Analysis
Applied sciences
Artificial intelligence
Bernoulli equation
Biological system modeling
Biomembranes
Blahut-Arimoto algorithm
capacity per unit cost
Coding, codes
Communication channels
Computer science
control theory
systems
Connectionism. Neural networks
Costs
Energy efficiency
energy-efficient coding
Exact sciences and technology
filtered Poisson process
Information
Information theory
Information, signal and communications theory
integrate-and-fire (IF) neuron
Jimbo-Kunisawa algorithm
Neurons
Neuroscience
Neurosciences
Poisson equations
rate matching
Shape
Signal and communications theory
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Theory
threshold
Timing jitter
Transmission and modulation (techniques and equipments)
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Summary:Understanding how a biological neuron works has been a major goal in neuroscience. Under the Poisson-excitation assumption, results from earlier study by Suksompong and Berger on the timing jitter in the leaky integrate-and-fire (LIF) model of neurons are used to determine families of neural thresholding functions that are appropriate in certain interesting senses. Next, the neuron is treated as a communication channel for which information-theoretic quantities can be calculated. In particular, the optimal distribution of the Poisson excitation intensity is numerically evaluated along with the corresponding capacity using the Blahut-Arimoto algorithm. Simple formulas which approximate the optimal intensity distribution are given. Furthermore, the Jimbo-Kunisawa algorithm is used to explore energy-efficient operations for neuron. Finally, a rate-matching argument leads to a unique operating condition which turns out to agree with experimentally observed rate.
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2009.2037042