A mean field method for pulse-coupled oscillators with delays used a self-connected oscillator to represent a synchronous cluster of - 1 oscillators and a single oscillator assumed to be perturbed from the cluster. A periodic train of biexponential ...
IEEE transactions on neural networks and learning systems
Jun 1, 2025
The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processe...
IEEE transactions on neural networks and learning systems
Jun 1, 2025
Spiking neural networks (SNNs) are biologically plausible models known for their computational efficiency. A significant advantage of SNNs lies in the binary information transmission through spike trains, eliminating the need for multiplication opera...
Brain computer interfaces (BCIs) require substantial cognitive flexibility to optimize control performance. Remarkably, learning this control is rapid, suggesting it might be mediated by neuroplasticity mechanisms operating on very short time scales....
The human brain is a dynamic system that is constantly learning. It employs a combination of various learning strategies to facilitate complex learning processes. However, implementing biological learning mechanisms into Spiking Neural Networks (SNNs...
Achieving general artificial intelligence (AGI) has long been a grand challenge in the field of AI, and brain-inspired computing is widely acknowledged as one of the most promising approaches to realize this goal. This paper introduces a novel brain-...
Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each encoding me...
The study of brain activity spans diverse scales and levels of description and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks p...
Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robustly, but it...
Traditional models of reading lack a realistic simulation of the early visual processing stages, taking input in the form of letter banks and predefined line segments, making them unsuitable for modeling early brain responses. We used variations of t...
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