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Induction of an Isoelectric Brain State to Investigate the Impact of Endogenous Synaptic Activity on Neuronal Excitability In Vivo
Published on: March 31, 2016
Decorrelation of neural-network activity by inhibitory feedback
Tom Tetzlaff1, Moritz Helias, Gaute T Einevoll
1Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Jülich, Germany. t.tetzlaff@fz-juelich.de
Plos Computational Biology
|November 8, 2012
Summary
Neural networks use inhibitory feedback to actively reduce correlations between neurons, preventing information loss. This feedback mechanism is crucial for stable neural communication and accurate information encoding.
Area of Science:
- Computational neuroscience
- Neural network dynamics
- Information theory
Background:
- Correlations in neural spike trains can hinder information encoding.
- Common presynaptic input is a primary source of neural correlations.
- Previous studies noted lower-than-expected spike correlations in recurrent neural networks.
Purpose of the Study:
- To explain the observed reduction in spike correlations in recurrent neural networks.
- To investigate the role of inhibitory feedback in neural decorrelation.
- To quantify the impact of feedback statistics on population activity.
Main Methods:
- Utilized a linear network model.
- Performed simulations using leaky integrate-and-fire neuron networks.
- Compared intact recurrent networks (feedback) with perturbed systems (feedforward).
Main Results:
- Inhibitory feedback significantly suppresses pairwise correlations and population-rate fluctuations.
- Perturbing feedback statistics amplifies population-rate fluctuations by orders of magnitude.
- Negative feedback loops in network dynamics explain fluctuation suppression.
Conclusions:
- Inhibitory neurons play an active role in decorrelation.
- The structure of correlations between excitatory and inhibitory neurons is key to suppressing input correlations.
- Efficient neural coding relies on the dynamic interplay of feedback and correlation structures.
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