Advancements in algorithms and neuromorphic hardware for spiking neural networks
Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …
various application domains, including autonomous driving and drone vision. Researchers …
[HTML][HTML] Brian 2, an intuitive and efficient neural simulator
Brian 2 allows scientists to simply and efficiently simulate spiking neural network models.
These models can feature novel dynamical equations, their interactions with the …
These models can feature novel dynamical equations, their interactions with the …
[HTML][HTML] Bindsnet: A machine learning-oriented spiking neural networks library in python
The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …
enabling the modeling of neural systems and the development of biologically inspired …
[HTML][HTML] Neuromorphic intermediate representation: a unified instruction set for interoperable brain-inspired computing
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal
dynamics are getting wide attention and are being applied to many relevant problems using …
dynamics are getting wide attention and are being applied to many relevant problems using …
[HTML][HTML] From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?
We examine the challenging “marriage” between computational efficiency and biological
plausibility—A crucial node in the domain of spiking neural networks at the intersection of …
plausibility—A crucial node in the domain of spiking neural networks at the intersection of …
BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming
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 …
brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose …
[HTML][HTML] Introducing the Dendrify framework for incorporating dendrites to spiking neural networks
Computational modeling has been indispensable for understanding how subcellular
neuronal features influence circuit processing. However, the role of dendritic computations …
neuronal features influence circuit processing. However, the role of dendritic computations …
Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim.
S Koravuna, U Rückert… - International Journal of …, 2023 - search.ebscohost.com
Abstract Spiking Neural Networks (SNNs) help achieve brain-like efficiency and functionality
by building neurons and synapses that mimic the human brain's transmission of electrical …
by building neurons and synapses that mimic the human brain's transmission of electrical …
[HTML][HTML] GPUs outperform current HPC and neuromorphic solutions in terms of speed and energy when simulating a highly-connected cortical model
While neuromorphic systems may be the ultimate platform for deploying spiking neural
networks (SNNs), their distributed nature and optimization for specific types of models …
networks (SNNs), their distributed nature and optimization for specific types of models …
[HTML][HTML] PyGeNN: a Python library for GPU-enhanced neural networks
More than half of the Top 10 supercomputing sites worldwide use GPU accelerators and
they are becoming ubiquitous in workstations and edge computing devices. GeNN is a C++ …
they are becoming ubiquitous in workstations and edge computing devices. GeNN is a C++ …