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.

神经元通过短暂的电压尖峰相互交流，从而释放神经递质。大脑将一个人的周围环境、记忆和计划等信息编码为涉及许多神经元的协调的尖峰模式。50多年来，我们一直在记录大脑的尖峰，我们知道很多关于它们如何与刺激特性相关的细节；然而，对于这些细节如何相互作用产生复杂的行为，我们只有一个粗略的概念。