High-level information processing in the mammalian cortex requires both segregated
processing in specialized circuits and integration across multiple circuits. One possible way …

A novel approach to simulate simple protein–ligand systems at large time and length scales
is to couple Markov state models (MSMs) of molecular kinetics with particle-based reaction …

Individual-based models (IBM) enable modellers to avoid far-reaching abstractions and
strong simplifications by allowing for a state-based representation of individuals. The fact …

Learning-in-memory (LIM) is a recently proposed paradigm to overcome fundamental
memory bottlenecks in training machine learning systems. While compute-in-memory (CIM) …

We present the existence and uniqueness of random periodic path for stochastic dynamical
systems generated by random switching stochastic differential equations (SDEs). These …

We prove a Large Deviation Principle for Piecewise Deterministic Markov Processes
(PDMPs). This is an asymptotic estimate for the probability of a trajectory in the large size …

A key for understanding any information process depends on the nature of the signal
propagation. This article introduces a one-dimensional model that describes the passage of …

The calcium (Ca $^{2+} $) signalling system is important for many cellular processes within
the human body. Signals are transmitted within the cell by releasing Ca $^{2+} $ from the …

The canonical approach to modeling stochasticity considers a dynamical system driven by
Gaussian white noise. Here, we propose an alternative hybrid formulation, in which …

Neurons can detect weak signals in noisy cellular environments and complex backgrounds.
Channel blocks have a great impact on the initiation and propagation of action potentials for …