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37
Advancing the Boundaries of HighConnectivity Network Simulation with Distributed Computing
, 2005
"... The availability of efficient and reliable simulation tools is one of the missioncritical technologies in the fastmoving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of e ..."
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Cited by 63 (23 self)
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The availability of efficient and reliable simulation tools is one of the missioncritical technologies in the fastmoving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The
The Cat is Out of the Bag: Cortical Simulations with 10 9 Neurons, 10 13 Synapses
"... In the quest for cognitive computing, we have built a massively parallel cortical simulator, C2, that incorporates a number of innovations in computation, memory, and communication. Using C2 on LLNL’s Dawn Blue Gene/P supercomputer with 147, 456 CPUs and 144 TB of main memory, we report two cortical ..."
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Cited by 28 (4 self)
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In the quest for cognitive computing, we have built a massively parallel cortical simulator, C2, that incorporates a number of innovations in computation, memory, and communication. Using C2 on LLNL’s Dawn Blue Gene/P supercomputer with 147, 456 CPUs and 144 TB of main memory, we report two cortical simulations – at unprecedented scale – that effectively saturate the entire memory capacity and refresh it at least every simulated second. The first simulation consists of 1.6 billion neurons and 8.87 trillion synapses with experimentallymeasured gray matter thalamocortical connectivity. The second simulation has 900 million neurons and 9 trillion synapses with probabilistic connectivity. We demonstrate nearly perfect weak scaling and attractive strong scaling. The simulations, which incorporate phenomenological spiking neurons, individual learning synapses, axonal delays, and dynamic synaptic channels, exceed the scale of the cat cortex, marking the dawn of a new era in the scale of cortical simulations. 1.
Exact simulation of integrateandfire models with synaptic conductances
 Neural Comp
, 2006
"... Computational neuroscience relies heavily on the simulation of large networks of neuron models. There are essentially two simulation strategies: 1) using an approximation method (e.g. RungeKutta) with spike times binned to the time step; 2) calculating spike times exactly in an eventdriven fashion ..."
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Cited by 28 (1 self)
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Computational neuroscience relies heavily on the simulation of large networks of neuron models. There are essentially two simulation strategies: 1) using an approximation method (e.g. RungeKutta) with spike times binned to the time step; 2) calculating spike times exactly in an eventdriven fashion. In large networks, the computation time of the best algorithm for either strategy scales linearly with the number of synapses, but each strategy has its own assets and constraints: approximation methods can be applied to any model but are inexact; exact simulation avoids numerical artefacts but is limited to simple models. Previous work has focused on improving the accuracy of approximation methods. In this paper we extend the range of models that can be simulated exactly to a more realistic model, namely an integrateandfire model with exponential synaptic conductances.
Analytical IntegrateandFire Neuron Models with ConductanceBased Dynamics for EventDriven Simulation Strategies
, 2006
"... Eventdriven simulation strategies were proposed recently to simulate integrateandfire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating largescale networks of neurons; most important, such approaches are more precise than traditional cloc ..."
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Cited by 13 (1 self)
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Eventdriven simulation strategies were proposed recently to simulate integrateandfire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating largescale networks of neurons; most important, such approaches are more precise than traditional clockdriven numerical integration approaches because the timing of spikes is treated exactly. The drawback of such eventdriven methods is that in order to be efficient, the membrane equations must be solvable analytically, or at least provide simple analytic approximations for the state variables describing the system. This requirement prevents, in general, the use of conductancebased synaptic interactions within the framework of eventdriven simulations and, thus, the investigation of network paradigms where synaptic conductances are important. We propose here a number of extensions of the classical leaky IF neuron model involving approximations of the membrane equation with conductancebased synaptic current, which lead to simple analytic expressions for the
Anatomy of a Cortical Simulator
"... Insights into brain’s highlevel computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical ..."
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Cited by 12 (1 self)
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Insights into brain’s highlevel computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. 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 clockdriven and synapses in an eventdriven fashion; memory efficient representation of simulation state; and communication efficient message exchanges. Using phenomenological, singlecompartment models of spiking neurons and synapses with spiketiming dependent plasticity, we represented a ratscale cortical model (55 million neurons, 442 billion synapses) in 8TB memory of a 32,768processor BlueGene/L. With 1 millisecond resolution for neuronal dynamics and 120 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate. In summary, by combining stateoftheart hardware with innovative algorithms and software design, we simultaneously achieved unprecedented timetosolution on an unprecedented problem size. 1.
Realtime computing platform for spiking neurons (rtspike),” Neural Networks
 IEEE Transactions on
, 2006
"... Abstract—A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in ha ..."
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Cited by 11 (0 self)
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Abstract—A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as inputdriven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing softwarebased eventdriven approaches. As an alternative we have designed an efficient timebased computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the realtime control of robots operating within closed actionperception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important. Index Terms—Fieldprogrammable gate arrays, pipeline processing, real time system, spiking neural network hardware. I.
Scalable eventdriven native parallel processing: The spinnaker neuromimetic system
 in ACM International Conference on Computing Frontiers
, 2010
"... Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with model limitations, nor FPGA implementations, ..."
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Cited by 9 (7 self)
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Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with model limitations, nor FPGA implementations, with scalability limitations, offer a satisfactory solution even though they have improved simulation performance dramatically. SpiNNaker introduces a different approach, the “neuromimetic ” architecture, that maintains the neural optimisation of dedicated chips while offering FPGAlike universal configurability. Central to this parallel multiprocessor is an asynchronous eventdriven model that uses interruptgenerating dedicated hardware on the chip to support realtime neural simulation. While this architecture is particularly suitable for spiking models, it can also implement “classical ” neural models
Acceleration of Spiking Neural Networks in Emerging Multicore and GPU Architectures
"... Abstract—Recently, there has been strong interest in largescale simulations of biological spiking neural networks (SNN) to model the human brain mechanisms and capture its inference capabilities. Among various spiking neuron models, the HodgkinHuxley model is the oldest and most compute intensive, ..."
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Abstract—Recently, there has been strong interest in largescale simulations of biological spiking neural networks (SNN) to model the human brain mechanisms and capture its inference capabilities. Among various spiking neuron models, the HodgkinHuxley model is the oldest and most compute intensive, whereas the more recent Izhikevich model is very compute efficient. Some of the recent multicore processors and accelerators including Graphical Processing Units, IBM’s Cell Broadband Engine, AMD Opteron, and Intel Xeon can take advantage of task and thread level parallelism, making them good candidates for largescale SNN simulations. In this paper we implement and analyze two character recognition networks based on these spiking neuron models. We investigate the performance improvement and optimization techniques for SNNs on these accelerators over an equivalent software implementation on a 2.66 GHz Intel Core 2 Quad. We report significant speedups of the two SNNs on these architectures. It has been observed that given proper application of optimization techniques, the commodity X86 processors are viable options for those applications that require a nominal amount of flops/byte. But for applications with a significant number of flops/byte, specialized architectures such as GPUs and cell processors can provide better performance. Our results show that a proper match of architecture with algorithm complexity provides the best performance. I.
R.: Two Simulation Tools for Biologically Inspired Virtual Robotics
 In: Proceedings of the IEEE 5th Chapter Conference on Advances in Cybernetic Systems
, 2006
"... Abstract This paper describes two simulators that have been developed as part of Holland’s and Troscianko’s project to build a conscious robot. The first simulates a reconfigurable humanoid robot within a dynamic environment. This robot is more biologically realistic than traditional humanoid robot ..."
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Cited by 4 (2 self)
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Abstract This paper describes two simulators that have been developed as part of Holland’s and Troscianko’s project to build a conscious robot. The first simulates a reconfigurable humanoid robot within a dynamic environment. This robot is more biologically realistic than traditional humanoid robots because its movements are transmitted across its whole structure, which poses the same control problems that brains have to solve with biological systems. The second simulator in this paper simulates large numbers of spiking neurons with a flexibility and speed that makes it ideal for developing models of biologically structured neural networks. In combination these simulators will enable researchers to develop models of how the brain controls the human body and they will also be used to create and test controllers for the real CRONOS robot, on which the virtual robot is based. These tools are currently in their final stage of development and will soon be made freely available for noncommercial use.