Deep neural networks with rate-based neurons have exhibited tremendous progress in the
last decade. However, the same level of progress has not been observed in research on …

Biological brains have a natural capacity for resolving certain classification tasks. Studies on
biologically plausible spiking neurons, architectures and mechanisms of artificial neural …

Some neuroscientific studies have demonstrated that the human brain is a complex
integrated spatiotemporal system. The human brain supports a wide range of models …

Spiking neural networks (SNNs) transmit information through discrete spikes that perform
well in processing spatial–temporal information. Owing to their nondifferentiable …

In recent years, classification problems have been widely studied as one of the critical
research directions in artificial intelligence. Efficiently simulating the human brain's ability to …

Spiking neural networks (SNNs) as the third generation of artificial neural networks are
closer to their biological counterparts than their predecessors. SNNs have a higher …

Spiking neural networks (SNNs) process information with temporal coding schemes to
transmit feature values into spiking time series. Population Coding (PC) is the most popular …

This paper develops a new approach to estimate predicted class probabilities in deep
Spiking Neural Networks (SNN) that encourages faster classification. The proposed …

Thanks to the fast evolution of electroencephalography (EEG)-based braincomputer
interfaces (BCIs) and computing technologies, as well as the availability of large EEG …

Interaksi mesin-komputer merupakan suatu keniscayaan dan akan menjadi bagian yang
tidak terpisahkan dari kehidupan dalam waktu dekat, terutama di bidang rekayasa …