Romain and I discuss his theoretical/philosophical work examining how neuroscientists rampantly misuse the word “code” when making claims about information processing in brains. We talk about the coding metaphor, various notions of information, the different roles and facets of mental representation, perceptual invariance, subjective physics, process versus substance metaphysics, and the process of writing a Behavior and Brain Sciences article (spoiler: it’s a demanding yet rewarding experience).
In this second part of our conversation David, John, and I continue to discuss the role of complexity science in the study of intelligence, brains, and minds. Be sure to listen to the first part, which lays the foundation for what we discuss in this episode.
David, John, and I discuss the role of complexity science in the study of intelligence. In this first part, we talk about complexity itself, its role in neuroscience, emergence and levels of explanation, understanding, and really quite a bit more.
Olaf and I discuss the explosion of network neuroscience, which uses network science tools to map the structure (connectome) and activity of the brain at various spatial and temporal scales. We talk about the possibility of bridging physical and functional maps via communication dynamics, and about the relation between network science and artificial neural networks and plenty more.
Jim and I discuss his reverse engineering approach to visual intelligence, using deep models optimized to perform object recognition tasks. We talk about the history of his work developing models to match the neural activity in the ventral visual stream, how deep learning connects with those models, and some of his recent work: adding recurrence to the models to account for more difficult object recognition, using unsupervised learning to account for plasticity in the visual stream, and controlling neural activity by creating specific images for subjects to view.