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Reconfigurable platforms and the challenges for large-scale implementations of spiking neural networks
2008
2008 International Conference on Field Programmable Logic and Applications
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FPGA devices have witnessed popularity in their use for the rapid prototyping of biological Spiking Neural Network (SNNs) applications, as they offer the key requirement of reconfigurability. ...
The paper presents a novel Field Programmable Neural Network (FPNN) architecture incorporating low power analogue synapse and a network on chip architecture for SNN routing and configuration. ...
The aim of this strategy is to use the router of each tile to communicate spike events to and from its group of n synapses. ...
doi:10.1109/fpl.2008.4629989
dblp:conf/fpl/HarkinMHDDM08
fatcat:eevaoiwaafc2dj7h2p3jt7gtya
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
[article]
2021
arXiv
pre-print
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Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). ...
NeuroXplorer's optimization engine can incorporate hardware-oriented metrics such as energy, throughput, and latency, as well as SNN-oriented metrics such as inter-spike interval distortion and spike disorder ...
NeuroXplorer can work with both Artificial Neural Networks (ANNs) and biology-inspired Spiking Neural Networks (SNNs). ...
arXiv:2105.01795v1
fatcat:yztiegjepvho5ecztv2akaj4vy
On-chip communication for neuro-glia networks
2018
IET Computers & Digital Techniques
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This supports local communication for self-repair. The networks-on-chip (NoC) was implemented for local communication between the astrocytes and neurons. ...
Therefore, mapping neuro-glia networks to hardware provides a strategy for achieving self-repair in hardware. The internal interconnecting of these networks in hardware is a challenge. ...
The astrocyte network can be viewed as a high-level network, working in parallel with the neural network, responsible for regulating synaptic plasticity through neural networks. ...
doi:10.1049/iet-cdt.2017.0187
fatcat:xjx44h24inc3nhkg64r2guert4
Self-organization of multi-layer spiking neural networks
[article]
2020
arXiv
pre-print
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Inspired by this strategy, we attempt to efficiently self-organize large neural networks with an arbitrary number of layers into a wide variety of architectures. ...
The dynamical system encapsulates the dynamics of spiking units, their inter/intra layer interactions as well as the plasticity rules that control the flow of information between layers. ...
In this paper, we develop strategies to self-organize large spatially-connected, multi-layer spiking neural networks (SNN), inspired by the wiring rules and mechanisms adopted by the mammalian visual system ...
arXiv:2006.06902v1
fatcat:3527vglixfbxnjv3odmklh7mle
What does gamma coherence tell us about inter-regional neural communication?
2015
Nature Neuroscience
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Here we discuss how measurements of inter-regional gamma coherence can be prone to misinterpretation and suggest strategies for deciphering the roles that synchronized oscillations across brain networks ...
communication. ...
One possibility for observing neural transmission from network A to network B is to record the output spikes in the somatic layer of network A (electrode A SOMA ) and monitor the transmembrane currents ...
doi:10.1038/nn.3952
pmid:25706474
pmcid:PMC4803441
fatcat:6dgoxha35bbxto2t5qu5euh4je
Distributed simulation of polychronous and plastic spiking neural networks: strong and weak scaling of a representative mini-application benchmark executed on a small-scale commodity cluster
[article]
2014
arXiv
pre-print
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Here, we present the strong and weak scaling and the profiling of the computational/communication components of the DPSNN-STDP benchmark (Distributed Simulation of Polychronous Spiking Neural Network with ...
We introduce a natively distributed mini-application benchmark representative of plastic spiking neural network simulators. ...
: inter-process multicast: Spikes dim
(0.77±0.10)%
Message passing
Communication: inter-process multicast: Spikes payload
(0.82±0.20)%
Message passing
Axonal to synaptic spikes: intra-process multicast ...
arXiv:1310.8478v2
fatcat:6wac3pcpzvdjhawi7rtrtlnzwu
An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation
2013
Frontiers in Neuroscience
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We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. ...
Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. ...
This is a feature typical of a polychronous spiking neural network. The more clock cycles a spike lasts, the more robust the communication will be. ...
doi:10.3389/fnins.2013.00014
pmid:23408739
pmcid:PMC3570898
fatcat:25x2rbkrm5fgtpyithgr4vgk2a
Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections
[article]
2019
arXiv
pre-print
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Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which ...
We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying ...
Neural Activity Analysis The simulation generates the spikes produced by each neuron in the network. ...
arXiv:1902.08410v1
fatcat:k2ekn5k4o5hwth7tp6z3545s6y
Hardware event-driven simulation engine for spiking neural networks
2007
International journal of electronics (Print)
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We have adopted a strategy that handles efficiently disordered event lists, which is a completely novel approach in the framework of event-driven spiking neural network simulation. ...
Here, we need to handle inter-spike risks. ...
doi:10.1080/00207210701308625
fatcat:wwuxn25ij5cona2dgulfqwcqga
Scaling of a Large-Scale Simulation of Synchronous Slow-Wave and Asynchronous Awake-Like Activity of a Cortical Model With Long-Range Interconnections
2019
Frontiers in Systems Neuroscience
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Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which ...
We explored networks up to 192 × 192 modules, each composed of 1,250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying ...
For instance, we expect that the delivery of spiking messages will be a key element to be further optimized (e.g., using a hierarchical communication strategy). ...
doi:10.3389/fnsys.2019.00033
pmid:31396058
pmcid:PMC6664086
fatcat:nqhg25ez5zc2tl4nb524jf5l5y
Gaussian and Exponential Lateral Connectivity on Distributed Spiking Neural Network Simulation
2018
2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
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We measured the impact of long-range exponentially decaying intra-areal lateral connectivity on the scaling and memory occupation of a distributed spiking neural network simulator compared to that of short-range ...
Two-dimensional grids of cortical columns composed by up to 11 M point-like spiking neurons with spike frequency adaption were connected by up to 30 G synapses using short- and long-range connectivity ...
Orange blocks are used for the inter-processes communication tasks. ...
doi:10.1109/pdp2018.2018.00110
dblp:conf/pdp/PastorelliPSBCC18
fatcat:3gory6grabcodktarzoxko7ap4
SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor
2008
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
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SpiNNaker is a novel chip -based on the ARM processor -which is designed to support large scale spiking neural networks simulations. ...
Our eventual goal is to be able to simulate neural networks consisting of 10 9 neurons running in 'real time', by which we mean that a similarly sized collection of biological neurons would run at the ...
If a system for spiking neural networks is to be biologically plausible, therefore, it must display at least reasonable scalability, so that the same mapping and routing strategies that are effective for ...
doi:10.1109/ijcnn.2008.4634199
dblp:conf/ijcnn/KhanLPRJPF08
fatcat:w6jacfvycrajxead7t5vedmjue
Mapping of local and global synapses on spiking neuromorphic hardware
2018
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)
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2019 pre-print arXiv:1908.08024v1 fatcat:b23c6xo225e2djqf73nmehxjeu
Our framework is implemented in Python, interfacing CARLsim, a GPU-accelerated application-level spiking neural network simulator with an extended version of Noxim, for simulating time-multiplexed interconnects ...
Spiking Neural Networks (SNNs) are widely deployed to solve complex pattern recognition, function approximation and image classification tasks. ...
First, PACMAN is primarily targeted for architectures supported on SpiNNaker such as Deep Belief Networks [9] and Convolution Neural Networks [10] . ...
doi:10.23919/date.2018.8342201
dblp:conf/date/0001WHDCS18
fatcat:66xqcf5qtng7dckw4tgpr2rwmy
Parkinson's Disease Classification using Various Advanced Neural Network Classifiers
2019
International journal of recent technology and engineering
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Online Meta-neuron based Learning Algorithm (OMLA) is a newly evolved network applied for Parkinson's disease classification. ...
Network (PBL-McRBFN) for Parkinson's disease classification. ...
Online Meta-neuron based Learning Algorithm (OMLA) is a special class of artificial neural network (ANN), where neuron models communicate with spikes. ...
doi:10.35940/ijrte.d7924.118419
fatcat:hxfecatwsjemrbgmcyavt3hsk4
Simulation Tool for Asynchronous Cortical Streams (STACS): Interfacing with Spiking Neural Networks
2015
Procedia Computer Science
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We present a Simulation Tool for Asynchronous Cortical Streams (STACS) for studying spiking neural networks exhibiting adaptation in a closed-loop system. ...
In particular, STACS facilitates the development of network level metrics of spiking activity. ...
Event-based communication Much of the computation that occurs in a spiking neural network rests on top of events, namely the spiking of a neuron. ...
doi:10.1016/j.procs.2015.09.149
fatcat:kps6x4rp6ff47h5ftrbfqbfatq
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