Real-time million-synapse simulation of rat barrel cortex

Sharp, T. G.; Petersen, Rasmus S.; Furber, Steve

doi:10.3389/fnins.2014.00131

Cited by 14 publications

(14 citation statements)

References 44 publications

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“…As synapses outnumber neurons by a factor of 10 3 − 10 5 , these constitute the main constraint on network size. Computational capacity ranges from a few tens of millions of synapses on laptop or desktop computers, or on dedicated hardware when fully exploited [ 4 , 5 ], to 10 12 − 10 13 synapses on supercomputers [ 6 ]. This upper limit is still about two orders of magnitude below the full human brain, underlining the need for downscaling in computational modeling.…”

Section: Introductionmentioning

confidence: 99%

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

2015

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Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.

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Section: Introductionmentioning

confidence: 99%

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

2015

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“…Recently there is growing interest in building large-scale models using spiking neural networks (SNNs), which can achieve higher biological accuracy and more comprehensive functionality than smaller scale models (Izhikevich and Edelman, 2008 ; Eliasmith et al, 2012 ; Reimann et al, 2013 ). As a result, a number of computing platforms targeting SNNs such as SpiNNaker (Furber et al, 2013 ; Sharp et al, 2014 ), FACETS (Schemmel et al, 2010 ), Neurogrid (Silver et al, 2007 ), and TrueNorth (Merolla et al, 2014 ) have been developed to make large-scale network simulation faster, more energy efficient and more accessible. Neural simulation platforms generally make use of processors such as multi-core processors, Application-Specific Integrated Circuit (ASIC) chips, or Graphics Processing Units (GPUs), and offer various degrees of programmability, from programmable parameters to complete instruction level control.…”

Section: Introductionmentioning

confidence: 99%

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

2016

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NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

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“…The design of SpiNNaker was based on the assumption that each ARM processing core would be responsible for simulating 1000 spiking neurons (Jin et al, 2008 ). Each of these neurons was expected to have around 1000 synaptic inputs each receiving spikes at an average rate of 10 Hz and, within these constraints, large-scale cortical models with up to 50 × 10 6 neurons have already been successfully simulated on SpiNNaker (Sharp et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture

Knight

Furber

2016

Front. Neurosci.

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While the adult human brain has approximately 8.8 × 1010 neurons, this number is dwarfed by its 1 × 1015 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows 4× more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.

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Real-time million-synapse simulation of rat barrel cortex

Cited by 14 publications

References 44 publications

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

Synapse-Centric Mapping of Cortical Models to the SpiNNaker Neuromorphic Architecture

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