Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware.
Large-Scale Simulations of Plastic Neural Networks on Neuromorphic Hardware.
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DOI：
10.3389/fnana.2016.00037
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发表时间：
2016
影响因子：
2.9
通讯作者：
Furber SB
中科院分区：
文献类型：
--
作者：
Knight JC;Tully PJ;Kaplan BA;Lansner A;Furber SB
SpiNNaker是一种数字神经形态架构，旨在以接近生物实时的速度模拟大规模尖峰神经网络。SpiNNaker系统的基本计算单元不是使用定制的模拟或数字硬件，而是一个通用的ARM处理器，允许对其进行编程以模拟各种神经元和突触模型。这种灵活性在生物可塑性现象的研究中特别有价值。最近提出的学习规则的基础上贝叶斯置信传播神经网络（BCPNN）的范例提供了一个通用的框架，不同的可塑性机制的相互作用，使用尖峰神经元建模。然而，使用BCPNN学习来模拟大型网络在计算上可能是昂贵的，因为它需要每个突触的多个状态变量，每个状态变量都需要在每个模拟时间步更新。我们讨论的效率和准确性的权衡，在开发一个基于事件的BCPNN实现SpiNNaker的基础上的BCPNN方程的解析解，并详细介绍了采取的步骤，以适应这在有限的计算和内存资源的SpiNNaker架构。我们通过在一个循环吸引子网络中学习神经活动的时间序列来证明这种学习规则，我们在高达2.0 × 104个神经元和5.1 × 107个可塑性突触的规模上进行了模拟：这是有史以来在神经形态硬件上模拟的最大的可塑性神经网络。我们还在Cray XC-30超级计算机系统上运行了一个类似的模拟，发现如果要匹配我们的SpiNNaker模拟的运行时间，超级计算机系统使用的功率大约是45倍。这表明更便宜，更节能的神经形态系统正在成为大规模大脑模型可塑性研究中有用的发现工具。
SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Rather than using bespoke analog or digital hardware, the basic computational unit of a SpiNNaker system is a general-purpose ARM processor, allowing it to be programmed to simulate a wide variety of neuron and synapse models. This flexibility is particularly valuable in the study of biological plasticity phenomena. A recently proposed learning rule based on the Bayesian Confidence Propagation Neural Network (BCPNN) paradigm offers a generic framework for modeling the interaction of different plasticity mechanisms using spiking neurons. However, it can be computationally expensive to simulate large networks with BCPNN learning since it requires multiple state variables for each synapse, each of which needs to be updated every simulation time-step. We discuss the trade-offs in efficiency and accuracy involved in developing an event-based BCPNN implementation for SpiNNaker based on an analytical solution to the BCPNN equations, and detail the steps taken to fit this within the limited computational and memory resources of the SpiNNaker architecture. We demonstrate this learning rule by learning temporal sequences of neural activity within a recurrent attractor network which we simulate at scales of up to 2.0 × 104 neurons and 5.1 × 107 plastic synapses: the largest plastic neural network ever to be simulated on neuromorphic hardware. We also run a comparable simulation on a Cray XC-30 supercomputer system and find that, if it is to match the run-time of our SpiNNaker simulation, the super computer system uses approximately 45× more power. This suggests that cheaper, more power efficient neuromorphic systems are becoming useful discovery tools in the study of plasticity in large-scale brain models.