Five parameters of one of the most common neuronal models, the diffusion leaky integrate-and-fire
model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on …

Artificial neural networks overwrite previously learned tasks when trained sequentially, a
phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and …

The dialogue between cortex and hippocampus is known to be crucial for sleep-dependent
memory consolidation. During slow wave sleep, memory replay depends on slow oscillation (…

Olfactory processing takes place across multiple layers of neurons from the transduction of
odorants in the periphery, to odor quality processing, learning, and decision making in higher …

The human brain represents a complex computational system, the function and structure of
which may be measured using various neuroimaging techniques focusing on separate …

This article presents a stochastic model of binaural hearing in the medial superior olive (MSO)
circuit. This model is a variant of the slope encoding models. First, a general framework is …

Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in
many brain systems. Despite difference in the anatomy or functions of neuronal circuits …

Neural networks with a single plastic layer employing reward modulated spike time dependent
plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate …

Introduction Intracranial EEG (iEEG) data is a powerful way to map brain function,
characterized by high temporal and spatial resolution, allowing the study of interactions among …

The Ornstein–Uhlenbeck neuronal model is specified by two types of parameters. One type
corresponds to the properties of the neuronal membrane, whereas the second type (local …