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Hardware Implementation of a Bio-plausible Neuron Model for Evolution and Growth of Spiking Neural Networks on FPGA
2008
2008 NASA/ESA Conference on Adaptive Hardware and Systems
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We propose a digital neuron model suitable for evolving and growing heterogeneous spiking neural networks on FPGAs by introducing a novel flexible dendrite architecture and the new PLAQIF (Piecewise-Linear ...
Approximation of Quadratic Integrate and Fire) soma model. ...
FPGA-based POE Spiking Neural Networks The evolution of directly mapped recurrent spiking neural networks on FPGAs has been tackled by a few researchers (e.g. ...
doi:10.1109/ahs.2008.13
dblp:conf/ahs/ShayaniBT08
fatcat:zb33pcttgzgmfjklxpxexirzfi
Design Space Exploration of hardware spiking neurons for embedded Artificial Intelligence
2019
Neural Networks
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2019 pre-print arXiv:1910.01010v1 fatcat:u4cwobc3lrbxvawkn3sqla2jky
In this paper, we focus on Spiking Neural Networks (SNNs) with a comprehensive study of neural coding methods and hardware exploration. ...
Thus, we are able to reduce the number of spikes while keeping the same neuron's model, which results in an SNN with fewer events to process. ...
Acknowledgement We would like to thank Olivier Bichler and CEA LIST for providing us the N2D2 framework licence. ...
doi:10.1016/j.neunet.2019.09.024
pmid:31593842
fatcat:so2fuhlod5aldlofmpbgf5yagi
A Multi-cellular Developmental Representation for Evolution of Adaptive Spiking Neural Microcircuits in an FPGA
2009
2009 NASA/ESA Conference on Adaptive Hardware and Systems
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Here, a bio-inspired developmental genotype-phenotype mapping for evolution of spiking neural microcircuits in an FPGA is introduced, based on a digital neuron model and cortex structure suggested and ...
Suitability of the representation for evolution of useful architectures and its adaptability is shown through statistical analysis and examples of scalability, modularity and fault-tolerance. ...
This model is suitable for evolutionary development of heterogeneous spiking neural networks on FPGAs in different ways. ...
doi:10.1109/ahs.2009.39
dblp:conf/ahs/ShayaniBT09
fatcat:hy7bw7i6avevjjadkwtpyamgba
Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation
2016
Computational Intelligence and Neuroscience
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For the quadruped robot, the control is performed by the spiking neural network implemented on an Arduino board with 35% of resource usage. ...
Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. ...
At the same time, we applied the Christiansen Grammar Evolution to estimate the weights and synaptic connections of the spiking neural network to achieve an exact reproduction of the desired locomotion ...
doi:10.1155/2016/5615618
pmid:27436997
pmcid:PMC4942632
fatcat:xd2uhcsjbngpdb4bbhoohiqksa
Spiker: an FPGA-optimized Hardware acceleration for Spiking Neural Networks
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2201.06993v1 fatcat:lvmntslujbeihasompashrehwy
Spiking Neural Networks (SNNs) are an emerging type of neural network that presents many advantages compared to more classical Artificial Neural Networks (ANNs). ...
In particular, information between neurons is exchanged in the form of binary spikes, reducing the required resources and making the model more suitable for the creation o a hardware accelerator. ...
Spiking Neural Networks (SNNs) are an emerging class of ANNs in which information is exchanged between neurons in the form of binary spikes. ...
arXiv:2201.06993v2
fatcat:ohcoxbwhvzhefi535lhggmvctm
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
2016
Frontiers in Neuroscience
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NeuroFlow supports a number of commonly used 20 current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and 21 the spike-timing-dependent plasticity (STDP) rule for ...
Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core 24 processor, or 2.83 times the speed of GPU-based platforms. ...
Researchers have used FPGAs to develop neurocomputers for
137
spiking and non-spiking artificial neural networks (Maguire et al., 2007). ...
doi:10.3389/fnins.2015.00516
pmid:26834542
pmcid:PMC4712299
fatcat:r733lgxhgjcppcw7rwxuca7x6y
Does Soft Computing Classify Research in Spiking Neural Networks?
2010
International Journal of Computational Intelligence Systems
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This paper reviews research in spiking neural networks and attempts to determine if the term Soft Computing can be used to classify contributions in this area. ...
This can cause problems in disseminating and sharing results and potentially restricts research due to a lack of knowledge of the varied contributions. ...
Acknowledgements The author acknowledges the support of the Intelligent Systems Research Centre, University of Ulster and the ...
doi:10.2991/ijcis.2010.3.2.5
fatcat:5wqms6ymazg6zo43y6763td5ia
Does Soft Computing Classify Research in Spiking Neural Networks?
2010
International Journal of Computational Intelligence Systems
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This paper reviews research in spiking neural networks and attempts to determine if the term Soft Computing can be used to classify contributions in this area. ...
This can cause problems in disseminating and sharing results and potentially restricts research due to a lack of knowledge of the varied contributions. ...
Acknowledgements The author acknowledges the support of the Intelligent Systems Research Centre, University of Ulster and the ...
doi:10.1080/18756891.2010.9727688
fatcat:pvogtmgumfevbo4sjylqaswtcu
Digital Multiplierless Realization of Two Coupled Biological Morris-Lecar Neuron Model
2015
IEEE Transactions on Circuits and Systems Part 1: Regular Papers
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Index Terms-Field-programmable gate array (FPGA), Morris-Lecar (ML) neuron model, spiking neural networks (SNN). ...
Modeling and implementation of biological neural networks are significant objectives of the neuromorphic research field. ...
Spiking Neural Network (SNN) paradigms are significant for neuromorphic engineers and their research efforts in developing artificial neural networks have increased, recently [3] , [4] , [5] - [22] ...
doi:10.1109/tcsi.2015.2423794
fatcat:zfcn3v5nhzfhvfafj6rusolvqe
Modelling Neural Dynamics with Optics: A New Approach to Simulate Spiking Neurons through an Asynchronous Laser
2020
Electronics
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Spiking neurons are a new approach to emulate the neural processes that occur in the brain, known as the third generation of artificial neural networks. ...
In this paper, we propose a novel approach for implementing spiking neurons through an optical system. ...
We thank the University of Zurich and specially Prof. Indiveri for the support provided to carry out this research. ...
doi:10.3390/electronics9111853
fatcat:z5y6suyo7jfp3kxdvsyzdbvg4q
Spike pattern recognition using artificial neuron and spike-timing-dependent plasticity implemented on a multi-core embedded platform
2017
Artificial Life and Robotics
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The objective of this work is to use a multi-core embedded platform as computing architectures for neural applications relevant to neuromorphic engineering: e.g. robotics, artificial and spiking neural ...
networks. ...
" program (project number: 35966TL), implemented by the French Ministry of Foreign Affairs, the French Ministry of Higher Education and Research and the Japan Society for Promotion of Science. ...
doi:10.1007/s10015-017-0421-y
fatcat:i435ocl35jekfiogwpreyf524m
SNAVA—A real-time multi-FPGA multi-model spiking neural network simulation architecture
2018
Neural Networks
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Spiking Neural Networks (SNN) for Versatile Applications (SNAVA) simulation platform is a scalable and programmable parallel architecture that supports realtime, large-scale, multi-model SNN computation ...
This parallel architecture is implemented in modern Field-Programmable Gate Arrays (FPGAs) devices to provide high performance execution and flexibility to support large-scale SNN models. ...
FPGAs 590 provide a platform for designing highly parallel systems and high-speed serial links which are suitable for the simulation of SNN models. ...
doi:10.1016/j.neunet.2017.09.011
pmid:29054036
fatcat:qair4swatvh3rlndbbxh76ootm
Recent trends in neuromorphic engineering
2016
Big Data Analytics
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We hope that this review would serve as a handy reference to both beginners and experts, and provide a glimpse into the broad spectrum of applications of neuromorphic hardware and algorithms. ...
There is a diversity of work in the literature pertaining to neuromorphic systems, devices and circuits. ...
Models for large-scale spiking neural networks have been explored by Krichmar et al. [63] , Wu et. al. [64] and Wang et al. ...
doi:10.1186/s41044-016-0013-1
fatcat:oyjitbviy5cdpesqwo6fp5tpgu
PAX: A mixed hardware/software simulation platform for spiking neural networks
2010
Neural Networks
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In this paper we present a mixed hardware-software platform, specifically designed for the simulation of spiking neural networks, using conductance-based models of neurons and synaptic connections with ...
max 300 words) Many hardware-based solutions now exist for the simulation of bio-like neural networks. ...
Mixed analog-digital systems are an emerging solution for the emulation of spiking neural networks, as shown in section 2, in which various models and implementation techniques for hardware-based platforms ...
doi:10.1016/j.neunet.2010.02.006
pmid:20434309
fatcat:2dtewzdc3zh3tatompvknggmmq
A Hardware Implementation of a Network of Functional Spiking Neurons with Hebbian Learning
[chapter]
2004
Lecture Notes in Computer Science
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In this paper we present a functional model of a spiking neuron intended for hardware implementation. ...
As an example application we present a frequency discriminator to verify the computing capabilities of a generic network of our neuron model. ...
As for any model of neuron, adaptivity is required for a network of these neural models. ...
doi:10.1007/978-3-540-27835-1_18
fatcat:muxvxv75pzhqhiqfzz5muzjbvi
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