Spiking neural networks and their applications: A review
The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …
domains. However, deep neural networks are very resource-intensive in terms of energy …
Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience
Neuron modeling may be said to have originated with the Hodgkin and Huxley action
potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over …
potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over …
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 …
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 …
A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based
computation. However, supervised training of SNNs remains a challenge as spike activities …
computation. However, supervised training of SNNs remains a challenge as spike activities …
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 …
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 …
Mapping spiking neural networks to neuromorphic hardware
Neuromorphic hardware implements biological neurons and synapses to execute a spiking
neural network (SNN)-based machine learning. We present SpiNeMap, a design …
neural network (SNN)-based machine learning. We present SpiNeMap, a design …
CARLsim 4: An open source library for large scale, biologically detailed spiking neural network simulation using heterogeneous clusters
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 …
the memory and computation required to iteratively process the large set of neural state …
Bottom-up and top-down neural processing systems design: Neuromorphic intelligence as the convergence of natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …