Automata complete computation with Hodgkin–Huxley neural networks composed of synfire rings. (June 2020)

• Record Type: Journal Article
• Authors: Cabessa, Jérémie; Tchaptchet, Aubin
• Is Part Of: Neural networks. Volume 126(2020)
• Page Start: 312
• Page End: 334
• Publication Date: 2020-06
• Subjects: Neural computation -- Recurrent neural networks -- Cell assemblies -- Synfire rings -- Hodgkin–Huxley equations -- Finite state automata; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2020.03.019
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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.