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FPGA Implementation of Simplified Spiking Neural Network [article]

Shikhar Gupta, Arpan Vyas, Gaurav Trivedi
2020 arXiv   pre-print
In this paper, a simpler and computationally efficient SNN model using FPGA architecture is described.  ...  The proposed model is validated on a Xilinx Virtex 6 FPGA and analyzes a fully connected network which consists of 800 neurons and 12,544 synapses in real-time.  ...  large scale simulations rather than for low power embedded applications.  ... 
arXiv:2010.01200v1 fatcat:ou2bvu3q75gphiemrioemjgvhy

Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator

Joshua C. Crone, Manuel M. Vindiola, Alfred B. Yu, David L. Boothe, David Beeman, Kelvin S. Oie, Piotr J. Franaszczuk
2019 Frontiers in Neuroinformatics  
In this paper, we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks.  ...  While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity  ...  We are focused on the simulation of large-scale models with higher neuronal complexity for two related reasons.  ... 
doi:10.3389/fninf.2019.00069 pmid:31803040 pmcid:PMC6873326 fatcat:jiy4gfscpreu7dnnwmugx3t5ai

Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors

Maell Cullen, KongFatt Wong-Lin
2015 IET Systems Biology  
They then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons.  ...  , approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale.  ...  This poses a significant problem for developing computationally efficient multiscale models of DA system from molecular to neuronal circuit levels.  ... 
doi:10.1049/iet-syb.2015.0018 pmid:26577159 pmcid:PMC8687313 fatcat:kydysaqxe5glda7gczytw7c2pa

Brain Modeling ToolKit: an Open Source Software Suite for Multiscale Modeling of Brain Circuits [article]

Kael Dai, Sergey Gratiy, Yazan N Billeh, Richard Xu, Binghuang Cai, Nicholas Cain, Atle E Rimehaug, Alexander J Stasik, Gaute T Einevoll, Stefan Mihalas, Christof Koch, Anton Arkhipov
2020 biorxiv/medrxiv   pre-print
We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.  ...  Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to  ...  Acknowledgments 3-D visualizations were generated using RTNeuron with the support of the Blue Brain Project. We are grateful to Michael Hines for many helpful discussions and suggestions.  ... 
doi:10.1101/2020.05.08.084947 fatcat:mrje22zdrjgozcvnkoywgnzd3y

Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits

Kael Dai, Sergey L. Gratiy, Yazan N. Billeh, Richard Xu, Binghuang Cai, Nicholas Cain, Atle E. Rimehaug, Alexander J. Stasik, Gaute T. Einevoll, Stefan Mihalas, Christof Koch, Anton Arkhipov (+1 others)
2020 PLoS Computational Biology  
We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.  ...  Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to  ...  Acknowledgments 3-D visualizations were generated using RTNeuron with the support of the Blue Brain Project. We are grateful to Michael Hines for many helpful discussions and suggestions.  ... 
doi:10.1371/journal.pcbi.1008386 pmid:33253147 fatcat:p2r7xq2ic5em7bjywglbcjyjta

Modeling the network dynamics of pulse-coupled neurons

Sarthak Chandra, David Hathcock, Kimberly Crain, Thomas M. Antonsen, Michelle Girvan, Edward Ott
2017 Chaos  
We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network.  ...  We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions, as well as degree  ...  In modelling the dynamics of such networks, simulating the microscopic behavior at each node can be a computationally intensive task, especially when the network is extremely large. 5] [16] has recently  ... 
doi:10.1063/1.4977514 pmid:28364765 fatcat:n5ao72i6qzbbdlejwowhunor44

The Scientific Case for Brain Simulations

Gaute T. Einevoll, Alain Destexhe, Markus Diesmann, Sonja Grün, Viktor Jirsa, Marc de Kamps, Michele Migliore, Torbjørn V. Ness, Hans E. Plesser, Felix Schürmann
2019 Neuron  
A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons.  ...  Here, we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and why a set of brain simulators based on neuron models at different  ...  A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons.  ... 
doi:10.1016/j.neuron.2019.03.027 pmid:31121126 fatcat:gzq5cfm47za27kdgiecuwfrcvm

Affordable emerging computer hardware for neuromorphic computing applications

Morgan Bishop, Michael J. Moore, Daniel J. Burns, Robinson E. Pino, Richard Linderman
2010 The 2010 International Joint Conference on Neural Networks (IJCNN)  
ABSTRACT We are pursuing an investigation of neuromorphic computational models and architectures in order to leverage present understanding of how the estimated 10 11 neurons and 10 15 neuron connections  ...  Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to and to the Office of Management and Budget, Paperwork  ...  ACKNOWLEDGMENT This work was supported in part by the United States Air Force Office of Scientific Research (AFOSR), LRIR# 061F02COR.  ... 
doi:10.1109/ijcnn.2010.5596576 dblp:conf/ijcnn/BishopMBPL10 fatcat:n7potz66pncdhecfdzsspvmjc4

Software for Brain Network Simulations: A Comparative Study

Ruben A. Tikidji-Hamburyan, Vikram Narayana, Zeki Bozkus, Tarek A. El-Ghazawi
2017 Frontiers in Neuroinformatics  
Furthermore, as expected, NEST mostly favors large network models, while NEURON is better suited for detailed models.  ...  However, all simulators tend to expand the supported range of models by providing a universal environment for the computational study of individual neurons and brain networks.  ...  of large scale networks.  ... 
doi:10.3389/fninf.2017.00046 pmid:28775687 pmcid:PMC5517781 fatcat:a4dp346hizeflim4dulfeoe74q

Anatomy of a cortical simulator

Rajagopal Ananthanarayanan, Dharmendra S. Modha
2007 Proceedings of the 2007 ACM/IEEE conference on Supercomputing - SC '07  
With 1 millisecond resolution for neuronal dynamics and 1-20 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate.  ...  We have built a cortical simulator, C2, incorporating several algorithmic enhancements to optimize the simulation scale and time, through: computationally efficient simulation of neurons in a clock-driven  ...  Large-scale cortical simulations provide one avenue for computationally exploring hypotheses about how does the cortex work, what does it compute, and how we may, eventually, mechanize it.  ... 
doi:10.1145/1362622.1362627 dblp:conf/sc/AnanthanarayananM07 fatcat:nlf2avp4evfz5kqx3gpyjz4jxi

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

Xinyu Wu, Vishal Saxena, Kehan Zhu
2015 IEEE Journal on Emerging and Selected Topics in Circuits and Systems  
As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive  ...  Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns.  ...  John Chiasson for his comments on the manuscript.  ... 
doi:10.1109/jetcas.2015.2433552 fatcat:jd6l2uhpmjhindanx7boqkdkgq

A brain basis of dynamical intelligence for AI and computational neuroscience [article]

Joseph D. Monaco, Kanaka Rajan, Grace M. Hwang
2021 arXiv   pre-print
While scaling to larger models has delivered performance improvements for current applications, more brain-like capacities may demand new theories, models, and methods for designing artificial learning  ...  A convergence of AI/CN theories and objectives will reveal dynamical principles of intelligence for brains and engineered learning systems.  ...  Riding this bicycle will require new theories and models of joint temporal-attractor dynamics in biological and artificial systems.  ... 
arXiv:2105.07284v2 fatcat:ble5h45pk5fczn72dwco2m3rkm

Hebbian learning with winner take all for spiking neural networks

Ankur Gupta, Lyle N. Long
2009 2009 International Joint Conference on Neural Networks  
We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification.  ...  These bell-shapes curves are similar to those experimentally observed in the V1 and MT/V5 area of the mammalian brain.  ...  NEURON MODEL AND LEARNING A. Neuron Model There are many different models one could use both to model the individual spiking neurons, and also the nonlinear dynamics of the system.  ... 
doi:10.1109/ijcnn.2009.5178751 dblp:conf/ijcnn/GuptaL09 fatcat:aggxdkaj3zbqrfgm46if777vbu

Design of a Power Efficient Artificial Neuron Using Superconducting Nanowires

Emily Toomey, Ken Segall, Karl K. Berggren
2019 Frontiers in Neuroscience  
Here, we propose to use superconducting nanowires as a platform for the development of an artificial neuron.  ...  With the rising societal demand for more information-processing capacity with lower power consumption, alternative architectures inspired by the parallelism and robustness of the human brain have recently  ...  Donnie Keithley for edits, and the entire Quantum Nanostructures and Nanofabrication group.  ... 
doi:10.3389/fnins.2019.00933 pmid:31551691 pmcid:PMC6738026 fatcat:bvtl62ry5zaq7apoijicqpr3ua

Generative Models of Brain Dynamics – A review [article]

Mahta Ramezanian Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti, Irina Rish, Guillaume Dumas
2021 arXiv   pre-print
We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling.  ...  The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century.  ...  The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  ... 
arXiv:2112.12147v2 fatcat:gg2njt2ks5gudk7ewxype2zvni
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