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Noise Shaping in Neural Populations with Global Delayed Feedback.
O Ávila Åkerberg1, M J Chacron2
1Department of Physics, McGill University, Montreal, H3G 1Y6, Canada.
Mathematical Modelling of Natural Phenomena
|November 22, 2016
Summary
Delayed feedback in neural networks affects information transmission. Intrinsic neuron properties, like interspike interval correlations, interact with network dynamics, surprisingly altering how coupling strength influences information flow in renewal networks.
Area of Science:
- Computational neuroscience
- Network dynamics
- Information theory
Background:
- The interaction between intrinsic neuronal properties and network dynamics is crucial for understanding neural computation.
- Delayed global feedback is a common feature in biological neural systems, yet its impact on information processing remains incompletely understood.
Purpose of the Study:
- To investigate the effects of delayed global feedback on information transmission in neural networks.
- To compare the influence of delays on networks with and without intrinsic interspike interval correlations (nonrenewal vs. renewal networks).
Main Methods:
- Theoretical analysis
- Numerical simulations
- Comparison of renewal and nonrenewal neural network models
Main Results:
- Delayed feedback can modulate information transmission at the single-neuron level but not for the entire network.
- The addition of delay significantly alters the relationship between coupling strength and information transmission specifically in renewal networks.
- Intrinsic interspike interval correlations exhibit complex interactions with network-induced phenomena.
Conclusions:
- Intrinsic neuronal properties play a significant role in how neural networks process information under delayed feedback conditions.
- Delayed feedback's effect on information transmission is dependent on the intrinsic dynamics of the neurons within the network.
- These findings highlight the importance of considering both intrinsic and network-level dynamics for a comprehensive understanding of neural information processing.
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