Edge computing has gained significant traction in recent years, promising enhanced
efficiency by integrating artificial intelligence capabilities at the edge. While the focus has …

Spiking neural network simulations are a central tool in Computational Neuroscience,
Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators …

Spiking neural network (SNN) trained by spike-timing-dependent plasticity (STDP) is a
promising computing paradigm for energy-efficient artificial intelligence systems. During the …

This paper designs a multi-compartment soma-dendrite-spine model having nonlinear
dynamics of an asynchronous cellular automaton. The model can exhibit various …

Echo state networks are computationally lightweight reservoir models inspired by the
random projections observed in cortical circuitry. As interest in reservoir computing has …

Abstract The Liquid State Machine (LSM) is a recurrent spiking neural network designed for
efficient processing of spatio-temporal streams of information. LSMs have several inbuilt …

Spiking recurrent neural networks are gaining traction in solving complex temporal tasks. In
general, spiking neural networks are resilient and computationally powerful. These intrinsic …

A novel membrane potential model whose nonlinear dynamics is described by an ergodic
discrete difference equation is presented. It is shown that the model can exhibit various …

Compact online learning architectures can be used to enhance internet of things devices,
allowing them to learn directly on received data instead of sending data to a remote server …

A synaptic circuit according to an embodiment includes: a weight current circuit that applies
a weight current corresponding to a weight value; an input switch that switches whether or …