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Found 9 papers, 2 papers with code
Observing hidden neuronal states in experiments
We construct systematically experimental steady-state bifurcation diagrams for entorhinal cortex neurons.
Synchronization in STDP-driven memristive neural networks with time-varying topology
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.
Invariants for neural automata
Our work could be of substantial importance for related regression studies of real-world measurements with neurosymbolic processors for avoiding confounding results that are dependant on a particular encoding and not intrinsic to the dynamics.
Are Brain-Computer Interfaces Feasible with Integrated Photonic Chips?
Here, we propose a novel skull implant BCI that uses UPE.
Stochastic facilitation in heteroclinic communication channels
Here we investigate the information transmission properties of heteroclinic networks, studying them as communication channels.
Spike-adding and reset-induced canard cycles in adaptive integrate and fire models
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
Why we should use Topological Data Analysis in Ageing: towards defining the "Topological shape of ageing"
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.
A modular architecture for transparent computation in Recurrent Neural Networks
We then show that the Goedelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space.
Turing Computation with Recurrent Artificial Neural Networks
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.
