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Updated: Jul 18, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Chaotic hopping between attractors in neural networks.
Joaquín Marro1, Joaquín J Torres, Jesús M Cortés
1Institute Carlos I for Theoretical and Computational Physics, and Departamento de Electromagnetismo y Física de la Materia, University of Granada, E-18071-Granada, Spain.
Summary
This study introduces a neurobiologically-inspired model that exhibits attention-like adaptability through stochastic state jumps between patterns. Short-term noise induces synaptic changes, leading to adaptable, irregular system behavior.
Area of Science:
- Computational Neuroscience
- Complex Systems Theory
- Artificial Intelligence
Background:
- Understanding the neural mechanisms underlying attention and adaptability is crucial for developing intelligent systems.
- Stochastic processes and cellular automata are valuable tools for modeling complex biological phenomena.
Purpose of the Study:
- To develop a neurobiologically-inspired computational model that mimics attentional states.
- To investigate the role of stochasticity and synaptic dynamics in system adaptability.
Main Methods:
- Development of a stochastic cellular automaton model.
- Analysis of state transitions between attractors representing stored patterns.
- Mathematical analysis using mean-field theory.
- Validation through Monte Carlo simulations.
Main Results:
- The model demonstrates state jumps between attractors, mimicking pattern recall.
- Model parameters control the transition from regular to chaotic dynamics.
- Short-term presynaptic noise induces synaptic depression, leading to irregular, attention-like behavior.
- The system exhibits adaptability to changing stimuli.
Conclusions:
- Stochastic cellular automata can effectively model complex cognitive functions like attention.
- Presynaptic noise and synaptic depression are key mechanisms for adaptability in neural systems.
- The model provides insights into the neurobiological basis of attention and adaptive behavior.
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