A scalable chip multiprocessor for large-scale neural simulation

生物大脑是高度复杂的系统，其基本的工作原理鲜为人知。我们知道，它们由非常大量的神经细胞组成，这些神经细胞主要通过电脉冲或尖峰相互作用，我们有仪器可以随时显示大脑中哪些区域或多或少活跃，但我们对大脑功能的中间水平知之甚少。例如，一个复杂视觉场景的所有细节是如何在视觉皮层的神经尖峰模式中编码的？我们如何使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对这些系统如何工作提出假设，然后使用计算机来检验这些假设。对尖峰神经元进行建模的计算非常密集，因此现代PC能够使用每个神经元的一个相当简单的模型实时对数万个神经元进行建模。在这项研究中，我们计划建造一种专门为实时模拟大量神经元而设计的新型计算机。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进行相互通信。我们将利用半导体技术的发展，使许多微处理器能够放在一块硅芯片上，从而使计算机的成本和功耗尽可能低。我们的大脑尽管其组成神经元经常出现故障，但仍能继续工作，这种容错特性对希望使计算机更可靠的工程师非常感兴趣。因此，这项工作有两个相辅相成的终极目标：使用计算机更好地了解大脑在尖峰模式下是如何工作的，以及看看生物学是否可以帮助我们构建计算机系统，在组件故障的情况下仍能继续运行。

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.