Framework for benchmarking models of visual cortex function

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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.