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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
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Collective dynamics and shot-noise-induced switching in a two-population neural network
S Yu Kirillov1, P S Smelov1, V V Klinshov1,2
1Institute of Applied Physics of the Russian Academy of Sciences, Ulyanova Street 46, Nizhny Novgorod 603950, Russia.
Chaos (Woodbury, N.Y.)
|May 8, 2024
Summary
Neural mass models simulate neural populations but can differ from microscopic systems. Finite-size effects, or shot noise, can cause discrepancies, leading to blurred dynamics or metastable states in neural network simulations.
Area of Science:
- Computational neuroscience
- Theoretical neuroscience
- Complex systems analysis
Background:
- Neural mass models (NMMs) are essential for simulating large-scale neural networks and whole-brain activity.
- Understanding the dynamics of interconnected neural populations is crucial for neuroscience.
Purpose of the Study:
- To perform a systematic bifurcation analysis of a two-population (excitatory-inhibitory) neural mass model.
- To investigate the emergence of complex collective behaviors like chaotic oscillations and multistability.
- To compare the dynamics of NMMs with their corresponding microscopic systems and identify sources of discrepancy.
Main Methods:
- Systematic bifurcation analysis of a coupled excitatory-inhibitory neural mass model.
- Comparison of NMM dynamics against exact microscopic simulations.
- Interpretation of discrepancies through the lens of finite-size effects and shot noise.
Main Results:
- Identified scenarios for the emergence of complex collective behaviors, including chaotic oscillations and multistability.
- Demonstrated that NMM dynamics may not perfectly agree with microscopic systems.
- Interpreted discrepancies as shot noise effects arising from finite-size limitations.
Conclusions:
- Shot noise can significantly impact NMM dynamics, blurring attractors or inducing metastability.
- The findings highlight potential limitations of NMMs in accurately capturing all aspects of neural population dynamics.
- Further research is needed to refine NMMs to account for finite-size effects and improve simulation accuracy.
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