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Published on: August 28, 2019
Antireliability of noise-driven neurons
Denis S Goldobin1, Arkady Pikovsky
1Department of Physics, University of Potsdam, Postfach 601553, D-14415 Potsdam, Germany.
Summary
Firing neurons exhibit unreliable responses to noisy stimuli, generating varied spike patterns from identical inputs. This antireliability stems from noise sensitivity and oscillatory non-isochronicity in neural dynamics.
Area of Science:
- Computational neuroscience
- Nonlinear dynamics
Background:
- Neurons exhibit complex dynamics and respond to external stimuli.
- Stochasticity plays a crucial role in neural information processing.
Purpose of the Study:
- To investigate the reliability of neuronal firing in response to noisy driving within the FitzHugh-Nagumo model.
- To characterize and explain the phenomenon of 'antireliability' in neuronal responses.
Main Methods:
- Utilized the FitzHugh-Nagumo model to simulate neuronal dynamics.
- Employed Lyapunov exponent calculations to analyze system stability.
- Calculated event synchronization correlations to quantify response patterns.
- Developed a theoretical framework to explain observed antireliability.
Main Results:
- Demonstrated that identical neurons driven by the same Gaussian white noise produce different spike patterns.
- Quantified this unreliable firing, termed 'antireliability'.
- Identified high noise sensitivity and non-isochronicity of oscillations as key contributing factors.
- Described the antireliability geometrically using a random noninvertible one-dimensional map.
Conclusions:
- Neuronal firing can be inherently unreliable even under identical noisy conditions.
- The antireliability phenomenon is explained by specific features of neural dynamics and oscillations.
- This finding has implications for understanding information processing in noisy biological systems.
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