Simulation of networks of spiking neurons: a review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We start by
reviewing the different types of simulation strategies and algorithms that are currently …
reviewing the different types of simulation strategies and algorithms that are currently …
Phenomenological models of synaptic plasticity based on spike timing
Synaptic plasticity is considered to be the biological substrate of learning and memory. In
this document we review phenomenological models of short-term and long-term synaptic …
this document we review phenomenological models of short-term and long-term synaptic …
[HTML][HTML] Nest (neural simulation tool)
MO Gewaltig, M Diesmann - Scholarpedia, 2007 - scholarpedia.org
The Neural Simulation Tool NEST is a computer program for simulating large
heterogeneous networks of point neurons or neurons with a small number of compartments …
heterogeneous networks of point neurons or neurons with a small number of compartments …
[HTML][HTML] Event-based backpropagation can compute exact gradients for spiking neural networks
TC Wunderlich, C Pehle - Scientific Reports, 2021 - nature.com
Spiking neural networks combine analog computation with event-based communication
using discrete spikes. While the impressive advances of deep learning are enabled by …
using discrete spikes. While the impressive advances of deep learning are enabled by …
Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model
The digital neuromorphic hardware SpiNNaker has been developed with the aim of
enabling large-scale neural network simulations in real time and with low power …
enabling large-scale neural network simulations in real time and with low power …
Decorrelation of neural-network activity by inhibitory feedback
Correlations in spike-train ensembles can seriously impair the encoding of information by
their spatio-temporal structure. An inevitable source of correlation in finite neural networks is …
their spatio-temporal structure. An inevitable source of correlation in finite neural networks is …
Equation-oriented specification of neural models for simulations
Simulating biological neuronal networks is a core method of research in computational
neuroscience. A full specification of such a network model includes a description of the …
neuroscience. A full specification of such a network model includes a description of the …
[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 …
sPyNNaker: a software package for running PyNN simulations on SpiNNaker
This work presents sPyNNaker 4.0. 0, the latest version of the software package for
simulating PyNN-defined spiking neural networks (SNNs) on the SpiNNaker neuromorphic …
simulating PyNN-defined spiking neural networks (SNNs) on the SpiNNaker neuromorphic …
The high-conductance state of cortical networks
We studied the dynamics of large networks of spiking neurons with conductance-based
(nonlinear) synapses and compared them to networks with current-based (linear) synapses …
(nonlinear) synapses and compared them to networks with current-based (linear) synapses …