Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Supported by recent computational studies, there is increasing evidence that a wide range
of neuronal responses can be understood as an emergent property of nonnegative sparse …

The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …

Elucidating the intricate neural mechanisms underlying brain functions requires integrative
brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose …

Loihi is Intel's novel, manycore neuromorphic processor and is the first of its kind to feature a
microcode-programmable learning engine that enables on-chip training of spiking neural …

Abstract Spiking Neural Network (SNN) is a type of biologically-inspired neural networks that
perform information processing based on discrete-time spikes, different from traditional …

Large-scale spiking neural network (SNN) simulations are challenging to implement, due to
the memory and computation required to iteratively process the large set of neural state …

Biological neural networks continue to inspire new developments in algorithms and
microelectronic hardware to solve challenging data processing and classification problems …

Mature simulation systems for Spiking Neural Networks (SNNs) become more relevant than
ever for understanding the brain and supporting neuromorphic computing. The CARL-sim …

We present a system comprising a hybridization of self-organized map (SOM) properties
with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are …