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Firing statistics of inhibitory neuron with delayed feedback. I. Output ISI probability density
1Bogolyubov Institute for Theoretical Physics, Metrologichna Str. 14-B, 03680 Kyiv, Ukraine. vidybida@bitp.kiev.ua
Bio Systems
|January 15, 2013
Summary
Delayed inhibitory feedback in neurons significantly alters firing patterns, making them bimodal. This study analyzes neural activity using point stochastic processes and models like the binding neuron (BN) and leaky integrate-and-fire (LIF) neuron.
Area of Science:
- Computational Neuroscience
- Theoretical Neuroscience
- Stochastic Processes
Background:
- Neuronal activity is often modeled using stochastic processes.
- Understanding the impact of feedback mechanisms on neuronal firing is crucial.
- Previous studies explored excitatory feedback; this work focuses on inhibitory feedback.
Purpose of the Study:
- To investigate how delayed inhibitory feedback affects the output firing statistics of neurons.
- To derive analytical and numerical expressions for neuronal output characteristics.
Main Methods:
- Utilized point stochastic processes to model neuronal activity.
- Employed the binding neuron (BN) model for analytical derivations (threshold 2).
- Used the leaky integrate-and-fire (LIF) and higher-threshold BN models for numerical analysis.
Main Results:
- Inhibitory feedback leads to bimodal interspike interval (ISI) probability densities with jump discontinuities.
- The ISI coefficient of variation remains largely unaffected by inhibitory feedback, ranging from 0.5 to 1.
- Output firing statistics differ significantly from those with excitatory feedback.
Conclusions:
- Delayed inhibitory feedback fundamentally alters neuronal output firing statistics.
- The presence of inhibitory feedback introduces distinct statistical properties compared to no feedback or excitatory feedback scenarios.
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