Brain Inspired

Panel discussion on using neuroscience technologies to decode memory from connectomes, featuring a group of experts including Kenneth Hayworth. Topics include advancements in connectomics, decoding memory and connectomes, analyzing connectome complexity, the role of molecules, deep learning parallelism, studying connectome data with cultured neurons, understanding neuronal interactions, and the rules of connectome interpretation.

Laura Gradowski, a philosopher of science at the University of Pittsburgh, discusses the importance of scientific pluralism and the inclusion of fringe theories in science. She cites historical examples, including the Garcia effect, that challenge mainstream theories and highlight the need for tolerance and diversity in scientific research. The podcast explores various topics such as the transition of fringe ideas to mainstream acceptance, the validation of traditional ecological knowledge, and the role of constraints in generating movement and thoughts. It also delves into the concept of the 'no end principle' and the continuous exploration of new ideas in science.

Eric Shea-Brown, a theoretical neuroscientist, discusses dynamics and dimensionality in neural networks, exploring how they change during tasks. He highlights research findings on structural connection motifs and dimensionalities related to different modes of learning. The podcast also covers the impact of model architectures on neural dynamics, the complexity of the biological brain, and the concept of rich brain vs lazy brain. The chapter on paths and motifs in neural networks showcases a student's prediction abilities. Finally, the guest expresses desires for advancements in neuroscience and support for the podcast.

Support the show to get full episodes, full archive, and join the Discord community. I was recently invited to moderate a panel at the Annual Bernstein conference - this one was in Berlin Germany. The panel I moderated was at a satellite workshop at the conference called How can machine learning be used to generate insights and theories in neuroscience? Below are the panelists. I hope you enjoy the discussion! Program: How can machine learning be used to generate insights and theories in neuroscience? Panelists: Katrin Franke Lab website. Twitter: @kfrankelab. Ralf Haefner Haefner lab. Twitter: @haefnerlab. Martin Hebart Hebart Lab. Twitter: @martin_hebart. Johannes Jaeger Yogi's website. Twitter: @yoginho. Fred Wolf Fred's university webpage. Organizers: Alexander Ecker | University of Göttingen, Germany Fabian Sinz | University of Göttingen, Germany Mohammad Bashiri, Pavithra Elumalai, Michaela Vystrcilová | University of Göttingen, Germany

David Poeppel, researcher studying auditory cognition, speech perception, language, and music at NYU, returns to discuss the mysteries of memory storage, the language of thought hypothesis, and the pace of scientific progress in understanding the brain. They explore the challenges of studying memory, the implementation requirements for language processing, and the potential combination of symbolic computation and dynamics in the brain. They also delve into the downside of unprincipled data mining and the re-emergence of the language of thought hypothesis in cognitive organization.

Kevin Mitchell, Professor of genetics at Trinity College Dublin, discusses his new book 'Free Agents: How Evolution Gave Us Free Will'. Topics include the origin of agency, complexity of free will, indeterminacy in the universe, harnessing brain's randomness, creativity, and artificial free will.

Philosopher Alicia Juarrero discusses the importance of constraints in understanding complex systems like the brain. They explore the concept of context and its impact on human behavior. The conversation touches on emergent properties, culture, and the role of top-down constraints. They also delve into embodied cognition, brain activity, dimensionality, the difference between mind and brain, and the concept of wisdom. Exploring hope, protein folding, and higher levels of organization and constraints conclude the podcast.

In this podcast, Justin Wood discusses his work comparing the visual cognition of newborn chicks and AI models. He uses controlled-rearing techniques to understand visual intelligence and build systems that emulate biological organisms. They explore topics like object recognition, reverse engineering, collective behavior, and the potential of transformers in cognitive science.

Support the show to get full episodes, full archive, and join the Discord community. David runs his lab at UCLA where he's also a distinguished professor.  David used to believe what is currently the mainstream view, that our memories are stored in our synapses, those connections between our neurons.  So as we learn, the synaptic connections strengthen and weaken until their just right, and that serves to preserve the memory. That's been the dominant view in neuroscience for decades, and is the fundamental principle that underlies basically all of deep learning in AI. But because of his own and others experiments, which he describes in this episode, David has come to the conclusion that memory must be stored not at the synapse, but in the nucleus of neurons, likely by some epigenetic mechanism mediated by RNA molecules. If this sounds familiar, I had Randy Gallistel on the the podcast on episode 126 to discuss similar ideas, and David discusses where he and Randy differ in their thoughts. This episode starts out pretty technical as David describes the series of experiments that changed his mind, but after that we broaden our discussion to a lot of the surrounding issues regarding whether and if his story about memory is true. And we discuss meta-issues like how old discarded ideas in science often find their way back, what it's like studying non-mainstream topic, including challenges trying to get funded for it, and so on. David's Faculty Page. Related papers The central importance of nuclear mechanisms in the storage of memory. David mentions Arc and virus-like transmission: The Neuronal Gene Arc Encodes a Repurposed Retrotransposon Gag Protein that Mediates Intercellular RNA Transfer. Structure of an Arc-ane virus-like capsid. David mentions many of the ideas from the Pushing the Boundaries: Neuroscience, Cognition, and Life  Symposium. Related episodes: BI 126 Randy Gallistel: Where Is the Engram? BI 127 Tomás Ryan: Memory, Instinct, and Forgetting

Support the show to get full episodes, full archive, and join the Discord community. Check out my free video series about what's missing in AI and Neuroscience My guest is Michael C. Frank, better known as Mike Frank, who runs the Language and Cognition lab at Stanford. Mike's main interests center on how children learn language - in particular he focuses a lot on early word learning, and what that tells us about our other cognitive functions, like concept formation and social cognition. We discuss that, his love for developing open data sets that anyone can use, The dance he dances between bottom-up data-driven approaches in this big data era, traditional experimental approaches, and top-down theory-driven approaches How early language learning in children differs from LLM learning Mike's rational speech act model of language use, which considers the intentions or pragmatics of speakers and listeners in dialogue. Language & Cognition Lab Twitter: @mcxfrank. I mentioned Mike's tweet thread about saying LLMs "have" cognitive functions: Related papers: Pragmatic language interpretation as probabilistic inference. Toward a “Standard Model” of Early Language Learning. The pervasive role of pragmatics in early language. The Structure of Developmental Variation in Early Childhood. Relational reasoning and generalization using non-symbolic neural networks. Unsupervised neural network models of the ventral visual stream.