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A scalable chip multiprocessor for large-scale neural simulation

用于大规模神经模拟的可扩展芯片多处理器

基本信息

批准号：
EP/D07908X/1
负责人：
Stephen Furber
金额：
$ 81.27万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2006
资助国家：
英国
起止时间：
2006 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FD07908X%2F1
关键词：
scalable chip multiprocessor large scale

项目摘要

Biological brains are highly complex systems whose underlying principles of operation are little understood. We know that they comprise very large numbers of nerve cells - neurons - that interact with each other principally through electrical impulses or spikes, and we have instruments that can show which areas of the brain are more or less active at any time, but we know little about the intermediate levels of brain function. How, for example, are all the details of a complex visual scene encoded in the patterns of neural spikes in the visual cortex? And how do we use those patterns to recognize our family and friends?One way to help understand complex systems is to develop hypotheses of how those systems might work and then to use computers to test those hypotheses. Modelling spiking neurons is computationally very intensive, so a modern PC is capable of modelling a few tens of thousands of neurons in real time using a rather simple model of each neuron. In this research we plan to build a new sort of computer designed specifically for modelling large numbers of neurons in real time. This computer will be based upon large numbers of fairly simple microprocessors that communicate with each other using spike events modelled closely on the way biological neurons communicate. We will use developments in semiconductor technology to enable many microprocessors to be put on a single silicon chip, thereby keeping the cost and power consumption of the computer as low as possible.Our brains keep working despite frequent failures of their component neurons, and this fault-tolerant characteristic is of great interest to engineers who wish to make computers more reliable. So this work has two complementary ultimate goals: to use the computer to understand better how the brain works at the level of spike patterns, and to see if biology can help us see how to build computer systems that continue functioning despite component failures.

生物大脑是一个高度复杂的系统，其基本的运作原理还不为人所知。我们知道它们由大量的神经细胞（神经元）组成，这些神经细胞主要通过电脉冲或尖峰信号相互作用，我们有仪器可以显示大脑的哪些区域在任何时候都比较活跃，但我们对大脑功能的中间层次知之甚少。例如，一个复杂的视觉场景的所有细节是如何编码在视觉皮层的神经尖峰模式中的？我们如何利用这些模式来识别我们的家人和朋友？帮助理解复杂系统的一种方法是提出这些系统如何工作的假设，然后使用计算机来测试这些假设。对尖峰神经元进行建模在计算上是非常密集的，因此现代PC能够使用每个神经元的相当简单的模型在真实的时间内对数万个神经元进行建模。在这项研究中，我们计划建造一种新型计算机，专门用于在真实的时间内模拟大量神经元。这台计算机将基于大量相当简单的微处理器，这些微处理器使用与生物神经元通信方式密切相关的尖峰事件相互通信。我们将利用半导体技术的发展，把许多微处理器放在一个硅芯片上，从而使计算机的成本和功耗尽可能低。尽管大脑的组成神经元经常出现故障，但我们的大脑仍在继续工作，这种容错特性对希望使计算机更可靠的工程师来说是非常有兴趣的。因此，这项工作有两个互补的最终目标：使用计算机更好地了解大脑在尖峰模式水平上的工作方式，并看看生物学是否可以帮助我们了解如何构建计算机系统，即使组件故障也能继续运行。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Advances in Neuro-Information Processing

神经信息处理的进展

DOI：
10.1007/978-3-642-03040-6_127
发表时间：
2009
期刊：
影响因子：
0
作者：
Brown A
通讯作者：
Brown A

Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System.

DOI：
10.3389/fnins.2018.00434
发表时间：
2018
期刊：
Frontiers in neuroscience
影响因子：
4.3
作者：
Bogdan PA;Rowley AGD;Rhodes O;Furber SB
通讯作者：
Furber SB

Towards a Bio-Inspired Real-Time Neuromorphic Cerebellum.

DOI：
10.3389/fncel.2021.622870
发表时间：
2021
期刊：
Frontiers in cellular neuroscience
影响因子：
5.3
作者：
Bogdan PA;Marcinnò B;Casellato C;Casali S;Rowley AGD;Hopkins M;Leporati F;D'Angelo E;Rhodes O
通讯作者：
Rhodes O

Event driven bio-inspired attentive system for the iCub humanoid robot on SpiNNaker