The Neural Marketplace

现代计算机的功能比许多人想象的要强大得多。因此，今天的计算机仍然不能做多少事情是值得注意的。奇怪的是，计算机的一些最困难的任务对人类来说毫不费力。视觉、自然语言理解和行走控制等问题无疑需要大量的计算能力。但真实的困难是我们无法写下计算机执行这些任务时可以遵循的规则集。唯一的解决方案可能是开发计算机系统，像我们一样，通过示例和试错来学习，而不需要明确的指令。大脑包含的神经元数量与现代超级计算机中的晶体管数量大致相当。这些细胞在计算上比以前认为的更复杂。但最令人惊讶的是它们能够组织成大型的、功能连贯的网络，不断学习和适应动物不断变化的环境。这种情况在没有控制中心点的情况下发生，这表明神经元的某些东西使它们自动组装成信息处理系统。这个奖学金的提议是基于一个新的假设，来自神经生物学研究，这种自组织如何通过类似于市场经济的竞争过程发生。大脑皮层中的一个典型神经元接收大约10，000个输入，它将这些输入整合成一个单一的输出，依次广播到大约10，000个目标。我们的新假设是一种机制，通过这种机制，神经元从其目标接收反馈，并发出信号，表明它所携带的信息对网络的其他部分有多有用。一些证据表明，在大脑中，称为神经营养因子的分子可以充当这种反馈信号的载体。根据这一假设，整个大脑的神经元不断地尝试新的信息处理策略。在大多数情况下，神经元的目标不需要新的信息，不会收到反馈，神经元将返回到其先前的状态。然而，一些神经元会在对更大的网络有用的信息上发生，并将接收反馈，从而保留最近的变化。在市场经济中，这样的相互作用允许自治代理人（人和公司）组织成网络。汽车制造商从供应商那里购买零部件，供应商从自己的供应商那里购买零部件，如此循环，在供应链的每一个阶段，多家公司都在竞争生产最好的产品，尝试新的设计，如果成功，将增加市场份额。因此，制造一辆好车所需的决策分布在大量的代理人身上。没有一个人必须了解制造过程的每一个部分;相反，多个个体代理人做出的决策会使系统自行组织起来。改进和适应是通过在各级试验新的方法来实现的。把这幅图放大，你会看到一个包含数十亿人的全球经济。类似的相互作用能否将大脑中数十亿个细胞组织成一个单一的连贯系统？它们能让我们构建可扩展的学习机器来解决目前难以解决的计算问题吗？目前的提案将通过构建一系列越来越大的基于市场的神经网络系统来回答这些问题，以解决语音识别和机器人控制等一系列越来越具有挑战性的任务。这项研究的影响将远远超出这些领域，为视觉和医疗诊断等各种应用的学习系统的构建提供信息，以及需要多个计算设备的可扩展自组织的互联网路由等领域。通过计算机验证这一假设也将为我们理解大脑如何处理信息提供一个步骤性的变化，可能会产生新的方法来研究大脑组织的紊乱。

Modern computers are more powerful than many ever dared expect. So it is remarkable how much today's computers still can't do. Strangely, some of the hardest tasks for computers are effortless to humans. Problems like vision, natural language comprehension, and walking control will undoubtedly require massive computing power. But the real difficulty is our inability to write down sets of rules that a computer can follow to perform these tasks. The only solution may be to develop computer systems that, like us, learn by example and by trial and error, without needing explicit instructions. The brain contains roughly as many neurons as there are transistors in a modern supercomputer. These cells are computationally more sophisticated than was once believed. But what is most amazing is their ability to organize into large, functionally coherent networks, that constantly learn and adapt to an animal's changing circumstances. This happens with no central point of control, suggesting that something about neurons causes them to automatically assemble into information-processing systems. This fellowship proposal is based on a new hypothesis, derived from neurobiological research, for how this self-organization occurs through competitive processes analogous to those of a market economy. A typical neuron in the cerebral cortex receives about 10,000 inputs, which it integrates to produce a single output, broadcast in turn to about 10,000 targets. Our new hypothesis is for a mechanism by which a neuron receives feedback from its targets, signalling how useful the information it carries is to the rest of the network. Several lines of evidence suggest that in the brain, molecules called neurotrophins can act as carriers of this feedback signal. According to the hypothesis, neurons throughout the brain constantly experiment with new information processing strategies. In most cases, the new information will not be required by the neuron's targets, no feedback will be received, and the neuron will return to its prior state. A few neurons, however, will happen upon information that is useful to the larger network, and will receive feedback causing the recent changes to be retained. In a market economy, interactions like this allow autonomous agents (people and firms) to organize into networks. A firm that makes cars buys parts from suppliers, who buy components from their own suppliers, and so on. At each stage of the supply chain, multiple firms compete to produce the best products, experimenting with new designs that, if successful, will increase market share. The decisions required to build a good car are thus distributed over a large number of agents. No one person has to understand every part of the manufacturing process; instead, decisions made by multiple individual agents cause the system to organize itself. Improvements and adaptations occur by experiments with new approaches at all levels. Scale this picture up, and you have a global economy encompassing billions of individuals. Could similar interactions organize the billions of cells in the brain into a single coherent system? And could they allow us to build scalable learning machines to solve currently intractable problems in computing?The current proposal will answer these questions by constructing a series of increasingly large market-based neural network systems, to solve a series of increasingly challenging tasks from speech recognition and robot control. This research will have impact far beyond these domains, informing the construction of learning systems for applications as diverse as vision and medical diagnosis, as well as to domains such as internet routing that require scalable self-organization of multiple computing devices. Confirming the computational validation of the hypothesis would also provide a step-change in our understanding of how the brain processes information, potentially yielding new approaches to disorders of brain organization.