Brain Inspired

Support the show to get full episodes, full archive, and join the Discord community. Dean Buonomano runs the Buonomano lab at UCLA. Dean was a guest on Brain Inspired way back on episode 18, where we talked about his book Your Brain is a Time Machine: The Neuroscience and Physics of Time, which details much of his thought and research about how centrally important time is for virtually everything we do, different conceptions of time in philosophy, and how how brains might tell time. That was almost 7 years ago, and his work on time and dynamics in computational neuroscience continues. One thing we discuss today, later in the episode, is his recent work using organotypic brain slices to test the idea that cortical circuits implement timing as a computational primitive it's something they do by they're very nature. Organotypic brain slices are between what I think of as traditional brain slices and full on organoids. Brain slices are extracted from an organism, and maintained in a brain-like fluid while you perform experiments on them. Organoids start with a small amount of cells that you the culture, and let them divide and grow and specialize, until you have a mass of cells that have grown into an organ of some sort, to then perform experiments on. Organotypic brain slices are extracted from an organism, like brain slices, but then also cultured for some time to let them settle back into some sort of near-homeostatic point - to them as close as you can to what they're like in the intact brain... then perform experiments on them. Dean and his colleagues use optigenetics to train their brain slices to predict the timing of the stimuli, and they find the populations of neurons do indeed learn to predict the timing of the stimuli, and that they exhibit replaying of those sequences similar to the replay seen in brain areas like the hippocampus. But, we begin our conversation talking about Dean's recent piece in The Transmitter, that I'll point to in the show notes, called The brain holds no exclusive rights on how to create intelligence. There he argues that modern AI is likely to continue its recent successes despite the ongoing divergence between AI and neuroscience. This is in contrast to what folks in NeuroAI believe. We then talk about his recent chapter with physicist Carlo Rovelli, titled Bridging the neuroscience and physics of time, in which Dean and Carlo examine where neuroscience and physics disagree and where they agree about the nature of time. Finally, we discuss Dean's thoughts on the integrated information theory of consciousness, or IIT. IIT has see a little controversy lately. Over 100 scientists, a large part of that group calling themselves IIT-Concerned, have expressed concern that IIT is actually unscientific. This has cause backlash and anti-backlash, and all sorts of fun expression from many interested people. Dean explains his own views about why he thinks IIT is not in the purview of science - namely that it doesn't play well with the existing ontology of what physics says about science. What I just said doesn't do justice to his arguments, which he articulates much better. Buonomano lab. Twitter: @DeanBuono. Related papers The brain holds no exclusive rights on how to create intelligence. What makes a theory of consciousness unscientific? Ex vivo cortical circuits learn to predict and spontaneously replay temporal patterns. Bridging the neuroscience and physics of time. BI 204 David Robbe: Your Brain Doesn’t Measure Time Read the transcript. 0:00 - Intro 8:49 - AI doesn't need biology 17:52 - Time in physics and in neuroscience 34:04 - Integrated information theory 1:01:34 - Global neuronal workspace theory 1:07:46 - Organotypic slices and predictive processing 1:26:07 - Do brains actually measure time? David Robbe

Aran Nayebi, an Assistant Professor at Carnegie Mellon, specializes in merging AI and biological systems. He discusses the evolution of AI architectures and their relation to cognitive sciences. The conversation dives into the challenges of creating intelligent agents inspired by human cognition, highlighting the evolving Turing Test in NeuroAI. Nayebi also reveals insights on the significance of understanding brain processes for AI development and reflects on cultural perspectives, including the impact of 'The Matrix' on perceptions of intelligence.

Gabriele Scheler, a computational neuroscientist and co-founder of the Carl Correns Foundation for Mathematical Biology, delves into groundbreaking neuron models and their applications in AI. She argues that traditional neuron models have stagnated, advocating for approaches that account for internal cellular computations, which could revolutionize AI development. Gabriele also explores the interplay between language and thought, critiques the limitations of current AI, and reflects on how our internal verbal dialogues shape cognition, emphasizing the need for a nuanced understanding in neuroscience.

In this discussion with Alison Preston, a neuroscientist at the University of Texas at Austin, schemas take center stage in understanding cognition. She unpacks how these mental frameworks evolve from childhood to adulthood, influencing memory and decision-making. The conversation touches on the fascinating links between neural alignments and creativity, especially during adolescence. They also explore the implications of AI on human memory and the importance of precise terminology in psychology, offering a glimpse into the complex tapestry of our cognitive processes.

Listeners are invited to join a collaborative journal club dedicated to the fascinating field of complexity science. The discussion will center around foundational papers, fostering insights and connections to participants' own research. The speaker also introduces an exclusive complexity group, teasing significant themes from a notable book containing 20 essential papers. Additionally, there are perks for Patreon subscribers, hinting at exciting upcoming engagements.

Ciara Greene, an Associate Professor at University College Dublin, dives into the fascinating world of human memory. Her research reveals that memories aren't perfect records but flexible constructs helping us navigate life. They evolve over time, influenced by emotion and context. Discussing her book, Greene highlights the benefits of forgetting and the dangers of misinformation, especially in legal scenarios. Listeners will gain insights into how memories shape our identities and the ethical implications of memory research.

In a captivating discussion, Dmitri Chklovskii, head of the Neural Circuits and Algorithms lab at the Flatiron Institute, reveals how neurons are far more than just passive processors. He argues that these 'smart' cells actively influence their inputs, reshaping our understanding of brain function. The conversation dives into the complexities of the C. elegans connectome, uncovering its significance for neural communication. Chklovskii also challenges traditional neuroscience by exploring how feedback mechanisms and spike timing shape learning and behavior.

David Robbe, a neuroscientist and leader of the Cortical-Basal Ganglia Circuits and Behavior Lab, dives into how we perceive time beyond traditional measurements. He challenges the notion of a 'clock' in our brains, suggesting that time estimations come from our interactions with the environment. Robbe explores the intriguing link between behavior and neural timing, highlights Bergson's philosophy on time and memory, and contrasts human cognition with AI's mechanical processing, raising questions about free will and survival instincts.

David Krakauer, president of the Santa Fe Institute, is a prominent expert in complexity science and author of "The Complex World." In this intriguing discussion, he explores the four pillars of complexity—entropy, evolution, dynamics, and computation. Topics include the historical context of complexity science, the interplay of agency and emergent properties, and how time scales affect our understanding of systems. Krakauer also addresses the challenges of integrating various disciplines and the significance of information theory in neuroscience.

In this engaging discussion, Eli Sennesh, a postdoctoral researcher at Vanderbilt University, sheds light on predictive coding and its implications for understanding brain functions. He navigates the intriguing concept of 'divide-and-conquer predictive coding' and its experimental applications. The conversation also touches on the relationship between neuroscience and AI, emphasizing the need for biologically plausible computational models. They explore the complexities of decision-making, consciousness, and the humor in our perceptions of task difficulty, offering a delightful blend of research insights and personal anecdotes.