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Updated: Aug 16, 2025
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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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Persistence in a large network of sparsely interacting neurons.
Maximiliano Altamirano1, Roberto Cortez2, Matthieu Jonckheere3
1Instituto de Cálculo, Argentina, Universidad de Buenos Aires/CONICET, Buenos Aires, Argentina. maximiliano.altamirano@ic.fcen.uba.ar.
Journal of Mathematical Biology
|December 19, 2022
Summary
This study models large neural networks with random synaptic connections. It reveals a phase transition where neuron activity either dies out or persists indefinitely based on interconnection intensity, explaining persistent activity from weak signals.
Area of Science:
- Computational neuroscience
- Complex systems
Background:
- Biological neural networks exhibit complex dynamics.
- Synaptic randomness and sparse interactions are key features of neural systems.
Purpose of the Study:
- To present a novel biological neural network model incorporating random synaptic processes.
- To investigate the emergence of persistent activity in large-scale neural networks.
Main Methods:
- Development of a neural network model driven by inhomogeneous Poisson processes.
- Mathematical analysis using nonlinear mean-field theory and stochastic differential equations.
- Examination of network convergence to an infinite neuron limit.
Main Results:
- The finite network converges to a nonlinear mean-field process.
- A phase transition is identified in the infinite network: activity either persists or dies out.
- Persistent activity emerges from weak input signals depending on interconnection parameters.
Conclusions:
- The model provides insights into the mechanisms underlying persistent neural activity.
- Sparse interactions and global parameters critically influence network dynamics.
- This framework aids in understanding information processing in large biological neural networks.
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