This tutorial illustrates the use of data assimilation algorithms to estimate unobserved
variables and unknown parameters of conductance-based neuronal models. Modern data …

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw
nervous stimuli and respond identically to biological neurons. However, designing such …

Model optimization in neuroscience has focused on inferring intracellular parameters from
time series observations of the membrane voltage and calcium concentrations. These …

Designing hardware accelerators for machine learning (ML) applications is a well-
researched problem. This article presents a tutorial regarding new computing architectures …

Artificial neural networks inspired by brain operations can improve the possibilities of solving
complex problems more efficiently. Today's computing hardware, on the other hand, is …

The estimation of parameters controlling the electrical properties of biological neurons is
essential to determine their complement of ion channels and to understand the function of …

Biological neurons exhibit rich and complex nonlinear dynamics, which are computationally
expensive and area/power hungry for hardware implementation. This paper presents a …

Objective: Although biological synapses express a large variety of receptors in neuronal
membranes, the current hardware implementation of neuromorphic synapses often rely on …

Neuromorphic engineering aims at designing and building electronic systems that emulate
the function and organization of nervous systems in very large-scale integration (VLSI) …

Noise-activated transitions between coexisting attractors are investigated in a chaotic
spiking network. At low noise level, attractor hopping consists of discrete bifurcation events …