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Percolation in living neural networks
Ilan Breskin1, Jordi Soriano, Elisha Moses
1Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.
Researchers explored living neural networks using electrical stimulation, revealing a percolation transition in neural connectivity. This transition, marked by a power law, indicates a Gaussian degree distribution, not scale-free, regardless of neuron balance.
Area of Science:
- Neuroscience
- Complex Systems
- Network Science
Background:
- Living neural networks exhibit complex connectivity crucial for function.
- Understanding neural network dynamics under varying conditions is essential.
Purpose of the Study:
- To investigate the behavior of neural connectivity during a percolation transition.
- To characterize the disintegration of the giant component in neural networks.
- To determine the nature of the degree distribution in these networks.
Main Methods:
- Utilizing global electrical stimulation to probe living neural networks.
- Modulating neural connectivity by reducing synaptic strength and blocking neurotransmitter receptors.
- Applying graph-theoretic analysis to study network transitions.
Main Results:
- Neural connectivity was observed to undergo a percolation transition.
- The disintegration of the giant component followed a power law with an exponent beta ≈ 0.65.
- The exponent beta was independent of the balance between excitatory and inhibitory neurons.
Conclusions:
- The study demonstrates a percolation transition in living neural networks.
- The observed power-law exponent suggests a Gaussian degree distribution, differing from typical scale-free network models.
- These findings offer insights into the fundamental principles governing neural network organization and dynamics.
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