Automata Complete Computation with Hodgkin-Huxley Neural Networks Composed of Synfire Rings

0524679 - ÚI 2021 RIV GB eng J - Článek v odborném periodiku
Cabessa, Jérémie - Tchaptchet, A.
Automata Complete Computation with Hodgkin-Huxley Neural Networks Composed of Synfire Rings.
Neural Networks. Roč. 126, June (2020), s. 312-334. ISSN 0893-6080. E-ISSN 1879-2782
Grant CEP: GA ČR(CZ) GA19-05704S
Institucionální podpora: RVO:67985807
Klíčová slova: Neural computation * Recurrent neural networks * Cell assemblies * Synfire rings * Hodgkin–Huxley equations * Finite state automata
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impakt faktor: 8.050, rok: 2020
Způsob publikování: Omezený přístup
http://dx.doi.org/10.1016/j.neunet.2020.03.019

Synfire rings are neural circuits capable of conveying synchronous, temporally precise and self-sustained activities in a robust manner. We propose a cell assembly based paradigm for abstract neural computation centered on the concept of synfire rings. More precisely, we empirically show that Hodgkin–Huxley neural networks modularly composed of synfire rings are automata complete. We provide an algorithmic construction which, starting from any given finite state automaton, builds a corresponding Hodgkin–Huxley neural network modularly composed of synfire rings and capable of simulating it. We illustrate the correctness of the construction on two specific examples. We further analyze the stability and robustness of the construction as a function of changes in the ring topologies as well as with respect to cell death and synaptic failure mechanisms, respectively. These results establish the possibility of achieving abstract computation with bio-inspired neural networks. They might constitute a theoretical ground for the realization of biological neural computers.
Trvalý link: http://hdl.handle.net/11104/0309005