Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.
Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.
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DOI：
10.3389/fncel.2021.622870
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发表时间：
2021
影响因子：
5.3
通讯作者：
Rhodes O
中科院分区：
文献类型：
--
作者：
Bogdan PA;Marcinnò B;Casellato C;Casali S;Rowley AGD;Hopkins M;Leporati F;D'Angelo E;Rhodes O
这项工作提出了第一个模拟的大规模，生物物理约束小脑模型上进行神经形态硬件。在SpiNNaker神经形态系统上模拟了一个包含97，000个神经元和420万个突触的模型。结果进行了验证，对基线模拟相同的模型执行NEST，流行的尖峰神经网络模拟器使用通用的计算资源和双精度浮点运算。单个细胞和网络水平的尖峰活动的平均尖峰率，相对领先或滞后的尖峰时间，和膜电位动态的个别神经元，和SpiNNaker被证明产生的结果与NEST协议。一旦验证，该模型将用于研究如何加快SpiNNaker系统上网络的模拟速度，未来的目标是创建一个实时的神经形态小脑。通过详细的通信分析，峰值网络活动被确定为仿真加速的主要挑战之一。通过网络的尖峰活动的传播进行测量，并将告知未来的发展，小脑模型上的神经形态硬件的加速执行策略。模型中颗粒细胞与其他细胞类型的比例很大，导致高水平的活性聚集到少数细胞上，这些细胞与处理通讯相关的时间成本相对较大。根据空间位置在SpiNNaker上组织细胞可以减少41%的峰值通信负载。希望这些见解，加上替代并行化策略，将铺平道路，实时执行大规模的，生物物理约束的小脑模型SpiNNaker。这反过来将使探索小脑启发的控制器的神经机器人应用，并在时间尺度上执行延长的持续时间模拟，目前将禁止使用传统的计算平台。
This work presents the first simulation of a large-scale, bio-physically constrained cerebellum model performed on neuromorphic hardware. A model containing 97,000 neurons and 4.2 million synapses is simulated on the SpiNNaker neuromorphic system. Results are validated against a baseline simulation of the same model executed with NEST, a popular spiking neural network simulator using generic computational resources and double precision floating point arithmetic. Individual cell and network-level spiking activity is validated in terms of average spike rates, relative lead or lag of spike times, and membrane potential dynamics of individual neurons, and SpiNNaker is shown to produce results in agreement with NEST. Once validated, the model is used to investigate how to accelerate the simulation speed of the network on the SpiNNaker system, with the future goal of creating a real-time neuromorphic cerebellum. Through detailed communication profiling, peak network activity is identified as one of the main challenges for simulation speed-up. Propagation of spiking activity through the network is measured, and will inform the future development of accelerated execution strategies for cerebellum models on neuromorphic hardware. The large ratio of granule cells to other cell types in the model results in high levels of activity converging onto few cells, with those cells having relatively larger time costs associated with the processing of communication. Organizing cells on SpiNNaker in accordance with their spatial position is shown to reduce the peak communication load by 41%. It is hoped that these insights, together with alternative parallelization strategies, will pave the way for real-time execution of large-scale, bio-physically constrained cerebellum models on SpiNNaker. This in turn will enable exploration of cerebellum-inspired controllers for neurorobotic applications, and execution of extended duration simulations over timescales that would currently be prohibitive using conventional computational platforms.
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影响因子：
3.2
作者：
Casellato, Claudia;Antonietti, Alberto;Pedrocchi, Alessandra
通讯作者：
Pedrocchi, Alessandra
影响因子：
20.6
作者：
Chicca, Elisabetta;Stefanini, Fabio;Indiveri, Giacomo
通讯作者：
Indiveri, Giacomo
影响因子：
--
作者：
Antonietti, Alberto;Martina, Dario;Pedrocchi, Alessandra
通讯作者：
Pedrocchi, Alessandra
DOI：
10.1109/tnnls.2016.2598190
发表时间：
2017-11-01
影响因子：
10.4
作者：
Antonietti, Alberto;Casellato, Claudia;Pedrocchi, Alessandra
通讯作者：
Pedrocchi, Alessandra
影响因子：
3.8
作者：
Florimbi, Giordana;Torti, Emanuele;Leporati, Francesco
通讯作者：
Leporati, Francesco