Abstract From Deep Blue to AlphaGo, artificial intelligence and machine learning are
booming, and neural networks have become the hot research direction. However, due to the …

Most current Artificial Neural Network (ANN) models are based on highly simplified brain
dynamics. They have been used as powerful computational tools to solve complex pattern …

Ferroelectric tunnel junctions (FTJ) based on hafnium zirconium oxide (Hf1–x Zr x O2; HZO)
are a promising candidate for future applications, such as low-power memories and …

For a network of spiking neurons that encodes information in the timing of individual spike
times, we derive a supervised learning rule, SpikeProp, akin to traditional error …

The concept that neural information is encoded in the firing rate of neurons has been the
dominant paradigm in neurobiology for many years. This paradigm has also been adopted …

A new Multi-Spiking Neural Network (MuSpiNN) model is presented in which information
from one neuron is transmitted to the next in the form of multiple spikes via multiple …

Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of
neural networks. Highly inspired from natural computing in the brain and recent advances in …

The goal of this research is to develop an efficient SNN model for epilepsy and epileptic
seizure detection using electroencephalograms (EEGs), a complicated pattern recognition …

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 …

Based on the authors' groundbreaking research, Automated EEG-Based Diagnosis of
Neurological Disorders: Inventing the Future of Neurology presents a research ideology, a …