Credit assignment in the neocortex

新皮质的信用分配

• 批准号: RGPIN-2020-05105
• 负责人: Richards, Blake
• 金额: $ 4.53万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717528
• 关键词: Credit; assignment; neocortex

When we learn something new our brain changes. Specifically, the connections between the cells in our brains change. These connections, called synapses, determine how information flows through our brains, so changing them alters how our nervous system processes information. What is notable about learning, though, is that we don't just change when we learn, we get better. Though neuroscientists now have some understanding about synaptic changes in the brain, the mechanisms that ensure that we actually get better at something when we learn remain very mysterious. This mystery is known as the credit assignment problem. Somehow, our brains are capable of “assigning credit” for any errors or successes to the synapses that underlie a behaviour. Put another way, our brain somehow knows which synapses need to change in order to reduce our errors and increase our successes. Interestingly, simulated brains, known as artificial neural networks, have allowed researchers in the field of artificial intelligence (AI) to build systems that can recognise images, translate languages, drive cars, and control robotic arms. As with real brains, artificial neural networks change their synaptic connections when they learn. This works because computer scientists have developed algorithms for assigning credit in artificial neural networks. However, artificial neural networks still cannot learn as well as human beings. For example, a human can learn to drive a car in a matter of hours, whereas current artificial neural networks take millions of hours. Therefore, though the credit assignment algorithms used in artificial neural networks have been important for AI, they do not tell us exactly how our own brains work. The question we want to answer in this research program is: how does the mammalian brain solve the credit assignment problem, and can we build artificial neural networks that mimic the brain's solution? We will utilise a combination of AI and neuroscience to address this question. This research has significant potential to benefit society. If we could understand how the brain solves the credit assignment problem, then we could simultaneously make two significant leaps forward. First, we would understand how the brain rewires itself when learning. This could help us to develop novel brain-computer interfaces, prosthetic devices, etc. Second, if we understood credit assignment in the brain, we could potentially develop new algorithms for artificial neural networks that would give them the ability to learn more like humans. This could make them faster at learning. Canada is a world-leader in neuroscience inspired AI, and this research will help to train the next generation of researchers, software engineers, and entrepreneurs to help consolidate our position in this field and generate economic benefits for Canadian society.

当我们学习新东西时，我们的大脑会发生变化。具体来说，我们大脑中细胞之间的联系发生了变化。这些被称为突触的连接决定了信息如何在我们的大脑中流动，所以改变它们会改变我们的神经系统处理信息的方式。然而，值得注意的是，当我们学习时，我们不仅会改变，还会变得更好。尽管神经科学家现在对大脑中的突触变化有了一些了解，但确保我们在学习时确实在某件事上做得更好的机制仍然非常神秘。这个谜团被称为信用分配问题。不知何故，我们的大脑能够将任何错误或成功“归功于”构成行为基础的突触。换句话说，我们的大脑知道哪些突触需要改变，以减少我们的错误，增加我们的成功。