Brain Inspired

Damian Kelty-Stephen, an experimental psychologist at SUNY New Paltz, explores the boundaries of ecological psychology and neuroscience. He challenges the conventional computer metaphor of the brain and discusses fractal behaviors linking brain and body. The conversation delves into cascade dynamics and turbulence as key elements of intelligence. Damian advocates for a richer understanding of cognition through multifractal frameworks, emphasizing the complexity of self-organization in both natural and artificial systems.

Luis Favela, an Associate Professor at Indiana University Bloomington and expert in cognitive science and philosophy, dives into the complexities of the mind and environment. He introduces his NeuroEcological Nexus Theory, advocating for a fusion between ecological psychology and neuroscience. The discussion spans historical rifts, the conceptualization of intelligence, and the philosophical implications of perception. Favela also questions traditional views on AI, emphasizing the role of embodied cognition. His insights promise a refreshing perspective on brain-body-environment interactions.

Joshua Vogelstein discusses the world's largest connectome, focusing on a fruit fly's brain. He explains the concept of prospective learning in AI compared to retrospective learning, highlighting its potential. The podcast covers audio/video sync issues, data cleaning challenges, and the importance of anatomy in understanding brain function.

Jolande Fooken, a post-postdoctoral researcher, discusses hand-eye coordination and naturalistic tasks. Topics include various eye movements, levels of expertise, Yarbus' work, experimental paradigms for the brain, evolving views about the brain, and the intersection of coordination, robots, and AI.

Neuroscientists and AI experts discuss the relationship between neuroscience and AI at the COSYNE conference. They explore historical influences, evolving research approaches, and the need for interdisciplinary collaboration for progress. Topics include the shift in priorities from neuroscience to AI, the intersection of neuroscience and AI, and predictions for the future of neuro-AI in 2044.

Philosopher Mazviita Chirimuuta discusses simplification in neuroscience, highlighting the use of models, math, and analogies to understand the complex brain. She explores the intersection of neuroscience and philosophy, delves into simplification strategies in science, and emphasizes the interplay of technology and scientific understanding. The discussion touches on the challenges of interpreting scientific results, the limitations of reductionism, and the importance of maintaining a critical mindset in scientific pursuits.

Support the show to get full episodes, full archive, and join the Discord community. As some of you know, I recently got back into the research world, and in particular I work in Eric Yttris' lab at Carnegie Mellon University. Eric's lab studies the relationship between various kinds of behaviors and the neural activity in a few areas known to be involved in enacting and shaping those behaviors, namely the motor cortex and basal ganglia.  And study that, he uses tools like optogentics, neuronal recordings, and stimulations, while mice perform certain tasks, or, in my case, while they freely behave wandering around an enclosed space. We talk about how Eric got here, how and why the motor cortex and basal ganglia are still mysteries despite lots of theories and experimental work, Eric's work on trying to solve those mysteries using both trained tasks and more naturalistic behavior. We talk about the valid question, "What is a behavior?", and lots more. Yttri Lab Twitter: @YttriLab Related papers Opponent and bidirectional control of movement velocity in the basal ganglia. B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors. 0:00 - Intro 2:36 - Eric's background 14:47 - Different animal models 17:59 - ANNs as models for animal brains 24:34 - Main question 25:43 - How circuits produce appropriate behaviors 26:10 - Cerebellum 27:49 - What do motor cortex and basal ganglia do? 49:12 - Neuroethology 1:06:09 - What is a behavior? 1:11:18 - Categorize behavior (B-SOiD) 1:22:01 - Real behavior vs. ANNs 1:33:09 - Best era in neuroscience

The podcast discusses synthesizing neural principles for better AI, focusing on a 'sideways-in' approach for computational brains. It explores integrating diverse brain operations, the challenges in achieving general-purpose AI, advancements in robotics inspired by biological principles, and the complexities of spiking neural networks for artificial general intelligence.

Dan Goodman, co-founder of Neuromatch and creator of the Brian spiking neural network simulator, discusses the importance of spikes in intelligent systems and the curious choice of modern neural networks to disregard spiking. They delve into the intricacies of spiking neural networks, the transition from math to neuroscience, the creation of complex tasks for neural networks, and the challenges in training them. They also explore the impact of advanced technology on human intelligence.

Neuroscientist and author John Krakauer returns to discuss brain reorganization, plasticity, motor problems after strokes, and artificial general intelligence. They explore the misconception of brain reorganization, the challenges in studying behavioral outcomes after a stroke, and the need to critically analyze scientific papers and challenge established ideas.