no code implementations • 29 Aug 2023 • Dmitry Amakhin, Anton Chizhov, Guillaume Girier, Mathieu Desroches, Jan Sieber, Serafim Rodrigues
We construct systematically experimental steady-state bifurcation diagrams for entorhinal cortex neurons.
no code implementations • 17 Apr 2023 • Marius E. Yamakou, Mathieu Desroches, Serafim Rodrigues
In particular, we found that decreasing $P$ (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing $F$ (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay $\tau_c$, the average degree $\langle k \rangle$, and the rewiring probability $\beta$ have some appropriate values.
no code implementations • 29 Jan 2021 • Mathieu Desroches, Piotr Kowalczyk, Serafim Rodrigues
We study a class of planar integrate and fire (IF) models called adaptive integrate and fire (AIF) models, which possesses an adaptation variable on top of membrane potential, and whose subthreshold dynamics is piecewise linear (PWL).
Dynamical Systems Neurons and Cognition
no code implementations • 14 Aug 2020 • Tamàs Fülöp, Mathieu Desroches, Fernando Antônio Nóbrega Santos, Serafim Rodrigues
Living systems are subject to the arrow of time; from birth, they undergo complex transformations (self-organization) in a constant battle for survival, but inevitably ageing and disease trap them to death.
1 code implementation • 7 Sep 2016 • Giovanni Sirio Carmantini, Peter beim Graben, Mathieu Desroches, Serafim Rodrigues
We then show that the Goedelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space.
no code implementations • 4 Nov 2015 • Giovanni S Carmantini, Peter beim Graben, Mathieu Desroches, Serafim Rodrigues
We improve the results by Siegelmann & Sontag (1995) by providing a novel and parsimonious constructive mapping between Turing Machines and Recurrent Artificial Neural Networks, based on recent developments of Nonlinear Dynamical Automata.