Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

How does the brain work? After a century of research, we still lack a coherent view of how
neurons process signals and control our activities. But as the field of computational …

We present a novel method for predicting long-term target states based on mean-reverting
stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised …

Neuronal Noise combines experimental, theoretical and computational results to show how
noise is inherent to neuronal activity, and how noise can be important for neuronal …

Recent advances in bioelectronics and neural engineering allowed the development of
brain machine interfaces and neuroprostheses, capable of facilitating or recovering …

Neurological diseases can be studied by performing bio-hybrid experiments using a real-
time biomimetic Spiking Neural Network (SNN) platform. The Hodgkin-Huxley model offers a …

Neurons in the brain express intrinsic dynamic behavior which is known to be stochastic in
nature. A crucial question in building models of neuronal excitability is how to be able to …

The stochastic Ornstein-Uhlenbeck neuronal model is studied, and estimators of the model
input parameters, depending on the firing regime of the process, are derived. Closed …

Synaptic noise due to intense network activity can have a significant impact on the
electrophysiological properties of individual neurons. This is the case for the cerebral cortex …

Background New, silicon-based multielectrodes comprising hundreds or more electrode
contacts offer the possibility to record spike trains from thousands of neurons …