Electric and magnetic stimulation of the human brain can be used to excite or inhibit
neurons. Numerous methods have been designed over the years for this purpose with …

Brain-inspired computer vision aims to learn from biological systems to develop advanced
image processing techniques. However, its progress so far is not impressing. We recognize …

This paper presents a bioinspired digital liquid-state machine (LSM) for low-power very-
large-scale-integration (VLSI)-based machine learning applications. To the best of the …

Artificial neural networks built with optoelectronic synaptic devices have been proven to
process visual information effectively. However, it takes great latency time and energy …

Electrolyte‐gated transistors (EGTs) have been extensively studied as a next‐generation
neuromorphic device mimicking the biological ionic flux in synapses. However, its long‐term …

The granular retrosplenial cortex (RSG) is critical for both spatial and non-spatial behaviors,
but the underlying neural codes remain poorly understood. Here, we use optogenetic circuit …

We investigated a mathematical model composed of a spiking neural network (SNN)
interacting with astrocytes. We analysed how information content in the form of two …

Diverse artificial synapse structures and materials are widely proposed for neuromorphic
hardware systems beyond von Neumann architecture owing to their capability to mimic …

Growing experimental evidence shows that both homeostatic and Hebbian synaptic
plasticity can be expressed presynaptically as well as postsynaptically. In this review, we …

To understand how the human brain works, neuroscientists heavily rely on brain simulations
which incorporate the concept of time to their operating model. In the simulations, neurons …