Framework for benchmarking models of visual cortex function

视觉皮层功能基准模型框架

• 批准号: RGPIN-2019-05855
• 负责人: Tripp, Bryan
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 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=712624
• 关键词: Framework; benchmarking; models; visual; cortex

Neurons communicate with each other using brief voltage spikes that cause the release of neurotransmitters. The brain encodes information about a person's surroundings, memories, and plans, as coordinated patterns of spikes that involve many neurons. Spikes have been recorded from the brain for over 50 years, and we know many details about how they correlate with stimulus properties, etc.; however, we have only a rough idea of how these details interact to produce complex behaviour. Computational models are essential for understanding such complex systems. Models have been used in neuroscience for over 100 years, but it has never been possible to develop models that produce sophisticated behaviour. However, with the advent of deep networks, it is now possible to produce increasingly sophisticated behaviour; deep networks appear to be good starting points for producing large-scale models of complex brain function. This research program seeks to develop such models, by beginning with deep networks, iteratively incorporating models of biological mechanisms, and testing whether each mechanism makes the networks' internal representations and behaviour more biologically realistic. The research will focus particularly on the most fundamental step in this direction, which is developing a rigorous suite of benchmark tests to compare sophisticated models to the brains of living creatures. This will be done with the mouse brain, because detailed information about the mouse brain is publicly available, and because the small size of the mouse brain will facilitate rapid iteration through different model variations, to clarify which neural mechanisms contribute most to neural information processing. The specific objectives are to 1) develop a data-driven network architecture that allows very specific comparisons between parts of deep networks and the mouse brain; 2) develop a realistic virtual environment to evaluate neural mechanisms in terms of their roles in the life and survival of the mouse; and 3) develop metrics to compare models with complex public datasets of mouse brain activity. This research will allow a systematic and thorough assessment of sophisticated brain models, leading to identification of the neural circuit properties that most strongly account for spike patterns and behaviour. The results of this work in the mouse will provide important hints about the function of the human brain, which can then be tested and refined in more complex models. The research will contribute to a more precise understanding of human cognition, which may enable new quantitative approaches to a wide range of problems in psychology, neurology, and education. The research will also systematically identify useful mechanisms for advanced artificial intelligence. HQP trained as part of this research program will be uniquely positioned for future careers in neuroscience and advanced artificial intelligence.

神经元使用短暂的电压尖峰相互通信，从而导致神经递质的释放。大脑编码有关一个人的周围环境，记忆和计划的信息，作为涉及许多神经元的尖峰模式。峰值已从大脑中记录了50多年，我们知道许多有关它们与刺激特性的相关性等的细节。但是，我们对这些细节如何相互作用以产生复杂行为有一个粗略的想法。 计算模型对于理解这种复杂系统至关重要。模型已在神经科学中使用了100多年，但是从未有可能开发产生复杂行为的模型。但是，随着深网的出现，现在有可能产生越来越复杂的行为。深层网络似乎是产生复杂大脑功能的大型模型的好起点。该研究计划旨在从深层网络开始，迭代地结合生物学机制模型，并测试每种机制是否使网络的内部表示和行为更现实。 这项研究将特别关注该方向上最基本的步骤，该方向正在开发严格的基准测试套件，以将复杂的模型与生物的大脑进行比较。这将用小鼠大脑来完成，因为有关小鼠大脑的详细信息是公开可用的，并且由于小鼠大脑的尺寸较小将通过不同的模型变化来促进快速迭代，以阐明哪些神经机制对神经信息处理有最大的作用。 特定目标是1）开发一个数据驱动的网络体系结构，该体系结构可以在深网和鼠标大脑的部分之间进行非常具体的比较； 2）开发一个现实的虚拟环境，以根据其在小鼠的生命和生存中的作用来评估神经机制； 3）开发指标将模型与小鼠大脑活动的复杂公共数据集进行比较。这项研究将允许对复杂的大脑模型进行系统的全面评估，从而确定最强烈解释尖峰模式和行为的神经回路特性。 这项工作在小鼠中的结果将提供有关人脑功能的重要提示，然后可以在更复杂的模型中对其进行测试和完善。这项研究将有助于对人类认知的更精确理解，这可能使人们能够解决心理学，神经病学和教育方面的广泛问题。该研究还将系统地确定高级人工智能的有用机制。作为该研究计划的一部分，受过培训的HQP将成为神经科学和先进人工智能的未来职业的独特位置。