Abstract
We study the attractor dynamics of a Boolean model of the basal ganglia-thalamocortical network as a function of its interactive synaptic connections and global threshold. We show that the regulation of the interactive feedback and global threshold are significantly involved in the maintenance and robustness of the attractor basin. These results support the hypothesis that, beyond mere structural architecture, global plasticity and interactivity play a crucial role in the computational and dynamical capabilities of biological neural networks.
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Notes
- 1.
The basic attractors of a Boolean network are given by the basic cycles of its corresponding automaton, i.e., the cycles that do not visit the same vertex twice.
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Cabessa, J., Villa, A.E.P. (2016). Attractor Dynamics Driven by Interactivity in Boolean Recurrent Neural Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_14
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