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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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Highly connected neurons spike less frequently in balanced networks
Ryan Pyle1, Robert Rosenbaum1,2
1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana 46556, USA.
Physical Review. E
|May 14, 2016
Summary
Biological neuronal networks show variable spiking. Balanced network models, considering complex connectivity, reveal that neuron in-degree heterogeneity can break balance, impacting firing rates.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Network Science
Background:
- Biological neuronal networks display highly variable spiking activity.
- Balanced network models explain this variability through balanced excitatory and inhibitory inputs.
- Previous models often assumed homogeneous connectivity, unlike intricate biological networks.
Purpose of the Study:
- To investigate the impact of heterogeneous connectivity on balanced neuronal networks.
- To explore how non-uniform in-degrees affect network balance and neuronal firing rates.
Main Methods:
- Development and application of a heterogeneous mean-field theory for balanced networks.
- Analysis of network balance under varying in-degree distributions.
Main Results:
- Heterogeneous in-degrees can disrupt the balance in neuronal networks.
- Balanced heterogeneous architectures lead to lower firing rates in neurons with higher in-degrees.
- Findings align with recent experimental observations in biological networks.
Conclusions:
- Network architecture, specifically in-degree heterogeneity, plays a crucial role in maintaining neuronal network balance.
- The developed heterogeneous mean-field theory provides a more realistic model for biological neuronal networks.
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