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The Complexity of Dynamics in Small Neural Circuits
Diego Fasoli1, Anna Cattani1, Stefano Panzeri1
1Laboratory of Neural Computation, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy.
Plos Computational Biology
|August 6, 2016
Summary
Small neural networks exhibit complex dynamics not captured by mean-field approximations. This study reveals finite-size effects like symmetry-breaking, crucial for understanding mesoscopic brain circuits and information processing.
Area of Science:
- Computational Neuroscience
- Systems Neuroscience
- Theoretical Neuroscience
Background:
- Mean-field approximations are standard for large neural networks but fail for smaller, mesoscopic networks.
- Understanding dynamics in small neural networks is crucial for neuroscience but lacks adequate mathematical tools.
- Mesoscopic networks, composed of tens of neurons, are relevant to many neuroscience problems.
Purpose of the Study:
- To develop a systematic analysis for the dynamics of small, homogeneous networks of excitatory and inhibitory firing-rate neurons.
- To investigate local and global bifurcations in neural activity within these small networks.
- To uncover differences between mesoscopic network behavior and mean-field approximations.
Main Methods:
- Analytical and numerical study of local and global bifurcations in neural activity.
- Focus on homogeneous populations of excitatory and inhibitory firing-rate neurons.
- Investigation of spontaneous symmetry-breaking phenomena.
Main Results:
- Strong inhibition in small networks leads to complex dynamics via branching solutions.
- Spontaneous symmetry-breaking is identified as a key mechanism driving these complex dynamics.
- These dynamics represent a finite-size effect, diverging from mean-field predictions.
Conclusions:
- Mesoscopic neural networks exhibit unique dynamics distinct from mean-field approximations due to finite-size effects.
- Spontaneous symmetry-breaking allows small networks to regulate functional heterogeneity.
- This regulation may mitigate the impact of noise correlations on cortical information processing.
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