Simulation of large neuronal networks with biophysically accurate models on graphics processors

Wang, Mingchao; Yan, Boyuan; Hu, Jingzhen

doi:10.1109/ijcnn.2011.6033643

Cited by 24 publications

(12 citation statements)

References 11 publications

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“…The large latency makes them difficult to be used in real-time brain-machine interfaces (BMI). GPUs, on the other hand, are capable of parallel computing but are constrained by memory and communication bandwidth issues [13]. Models can be implemented directly onto CMOS, [14], [15] but a single implementation can be time consuming.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

Luo

Coapes

Mak

et al. 2016

IEEE Trans. Biomed. Circuits Syst.

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The cerebellum plays a critical role for sensorimotor control and learning. However, dysmetria or delays in movements' onsets consequent to damages in cerebellum cannot be cured completely at the moment. Neuroprosthesis is an emerging technology that can potentially substitute such motor control module in the brain. A pre-requisite for this to become practical is the capability to simulate the cerebellum model in real-time, with low timing distortion for proper interfacing with the biological system. In this paper, we present a frame-based network-on-chip (NoC) hardware architecture for implementing a bio-realistic cerebellum model with ∼ 100 000 neurons, which has been used for studying timing control or passage-of-time (POT) encoding mediated by the cerebellum. The simulation results verify that our implementation reproduces the POT representation by the cerebellum properly. Furthermore, our field-programmable gate array (FPGA)-based system demonstrates excellent computational speed that it can complete 1sec real world activities within 25.6 ms. It is also highly scalable such that it can maintain approximately the same computational speed even if the neuron number increases by one order of magnitude. Our design is shown to outperform three alternative approaches previously used for implementing spiking neural network model. Finally, we show a hardware electronic setup and illustrate how the silicon cerebellum can be adapted as a potential neuroprosthetic platform for future biological or clinical application.

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Section: Introductionmentioning

confidence: 99%

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

Luo

Coapes

Mak

et al. 2016

IEEE Trans. Biomed. Circuits Syst.

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“…By imitating such structures, neuromorphic computing systems are anticipated to be superior to the conventional computer systems in tasks such as image recognition and natural language understanding. As the most resourceconsuming part in neuromorphic algorithms [2], matrix operations are normally processed by hardware accelerators like CPU/GPU/FPGA [3] or VLSI circuits [4]. The straightforward hardware realization of neural networks, however, commonly consumes a large volume of memory and computing resources, incurring high design complexity and hardware cost.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired computing with resistive memories — models, architectures and applications

Liu

Chen

et al. 2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

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The traditional Von Neumann architecture has constrained the potential for applying massively parallel architecture to embedded high performance computing where we must optimize the size, weight and power of the system. Inspired by highly parallel biological systems, such as the human brain, the neuromorphic architecture offers a promising novel computing paradigm for compact and energy efficient platforms. The discovery of memristor devices provided the element we need with unprecedented efficiency in realizing such a computing architecture. There are still many challenges left to meet our goal of a fully functional bio-inspired computer. Here we will discuss our research in memristor crossbar based architecture, adaptation of this architecture for cogent confabulation models, and potential applications of the bio-inspired computer.

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“…These enhancements, in combination with parallel computing (Bower and Beeman, 1998; Migliore et al, 2006), have become a necessity to cope with the higher computational and the communication demands of neuroapplications. Recently, a number of developers have investigated the possibility of simulating spiking neural networks on a single Graphical Processing Unit (GPU) (Bernhard and Keriven, 2005; Fernandez et al, 2008; Fidjeland et al, 2009; Nageswaran et al, 2009a,b; Tiesel and Maida, 2009; Bhuiyan et al, 2010; Fidjeland and Shanahan, 2010; Han and Taha, 2010a,b; Hoffmann et al, 2010; Mutch et al, 2010; Scorciono, 2010; Yudanov et al, 2010; Nowotny, 2011; Ahmadi and Soleimani, 2011; Igarashi et al, 2011; Thibeault et al, 2011; Wang et al, 2011) or on multiple Graphics Processing Units (GPUs) (Brette and Goodman, 2012b). All these current simulators have shown significant improvements over their CPU only counterparts by integrating the utilization of GPUs.…”

Section: Introductionmentioning

confidence: 99%

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling

Hoang¹,

Tanna²,

Bray³

et al. 2013

Front. Neuroinform.

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Computational Neuroscience is an emerging field that provides unique opportunities to study complex brain structures through realistic neural simulations. However, as biological details are added to models, the execution time for the simulation becomes longer. Graphics Processing Units (GPUs) are now being utilized to accelerate simulations due to their ability to perform computations in parallel. As such, they have shown significant improvement in execution time compared to Central Processing Units (CPUs). Most neural simulators utilize either multiple CPUs or a single GPU for better performance, but still show limitations in execution time when biological details are not sacrificed. Therefore, we present a novel CPU/GPU simulation environment for large-scale biological networks, the NeoCortical Simulator version 6 (NCS6). NCS6 is a free, open-source, parallelizable, and scalable simulator, designed to run on clusters of multiple machines, potentially with high performance computing devices in each of them. It has built-in leaky-integrate-and-fire (LIF) and Izhikevich (IZH) neuron models, but users also have the capability to design their own plug-in interface for different neuron types as desired. NCS6 is currently able to simulate one million cells and 100 million synapses in quasi real time by distributing data across eight machines with each having two video cards.

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Simulation of large neuronal networks with biophysically accurate models on graphics processors

Cited by 24 publications

References 11 publications

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

Bio-inspired computing with resistive memories — models, architectures and applications

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling

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Simulation of large neuronal networks with biophysically accurate models on graphics processors

Cited by 24 publications

References 11 publications

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

Bio-inspired computing with resistive memories &#x2014; models, architectures and applications

A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling

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Product

Resources

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Bio-inspired computing with resistive memories — models, architectures and applications