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Decoding Information and Exploring Deep Learning Parallelism
The speakers delve into the concept of non-triviality and its relation to decoding information from Kannick-Toms, while also exploring deep learning parallelism and optimizing circuits for motor sequences. They highlight the importance of correlating the connectome, behavior, and genome to determine genetic and learned aspects, stressing the need for more measurements for a comprehensive understanding.