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Input-output relations in binding neuron
1Bogolyubov Institute for Theoretical Physics, Metrologichna str. 14B, Kyiv, Ukraine. vidybida@bitp.kiev.ua
Bio Systems
|February 28, 2007
Summary
The binding neuron model features finite memory, unlike traditional models. This allows for precise mathematical analysis of its output when processing Poisson input streams.
Area of Science:
- Computational Neuroscience
- Mathematical Biology
Background:
- The binding neuron model is inspired by Hodgkin-Huxley and leaky integrate-and-fire models.
- Unlike other models, its memory trace disappears after a fixed time, not through exponential decay.
Purpose of the Study:
- To mathematically describe the output stochastic process of the binding neuron.
- To analyze the output stream characteristics for specific firing thresholds.
Main Methods:
- Utilizing the finite memory property of the binding neuron.
- Mathematical analysis of the output stochastic process under Poissonian input.
- Characterizing the output stream in terms of probability density distribution of interspike intervals (threshold 2) and transmission function (threshold 3).
Main Results:
- Exact mathematical description of the output stochastic process for a binding neuron driven by a Poissonian input stream.
- Characterization of the non-Poissonian output stream for threshold 2.
- Obtained the transmission function for threshold 3.
Conclusions:
- The finite memory of the binding neuron enables exact mathematical descriptions of its output.
- This model facilitates the construction of fast recurrent neural networks for computational modeling.
- The study provides detailed characterization of the neuron's output for different firing thresholds.
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