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Driving neural oscillations with correlated spatial input and topographic feedback
Axel Hutt1, Connie Sutherland, André Longtin
1INRIA CR Nancy-Grand Est, CS20101, 54603 Villers-ls-Nancy Cedex, France. axel.hutt@loria.fr
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 15, 2008
Summary
Spatial correlations in random inputs enhance neural network oscillations, particularly in the gamma band. This effect is strongest when input correlation length surpasses feedback coupling range, impacting network dynamics.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Theoretical Neuroscience
Background:
- Neural networks exhibit complex oscillatory activity crucial for information processing.
- Understanding the influence of input statistics and network connectivity on oscillations is a key challenge.
Purpose of the Study:
- To investigate how spatial correlations of random inputs and feedback coupling range affect oscillatory activity in neural networks.
- To elucidate the mechanisms underlying frequency-specific modulation of neural oscillations.
Main Methods:
- Analysis of a neural field model with topographic delayed recurrent feedback.
- Numerical simulations of the neural field model and a network of stochastic spiking neurons.
Main Results:
- Increased input correlation length enhances oscillations in specific frequency bands, including the gamma band.
- Oscillatory power is maximally enhanced when input correlation length exceeds feedback coupling range.
- Suppression of oscillatory power is observed in other frequency bands, dependent on the ratio of input and feedback length scales.
Conclusions:
- The spatial scale of inputs and feedback critically shapes network oscillations.
- Results provide insights into the emergence of frequency-specific network activity in noise-driven excitable systems.
- Findings are applicable to diverse noise-driven excitable element networks.
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