161 episodes
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Andrea Gambarotto is a postdoctoral philosopher and researcher at the University of Luxembourg. He is an expert in the philosophy of Georg Wilhelm Friedrich Hegel, most commonly known as Hegel. More recently he has been studying the relation between some of Hegel's ideas and those of modern theoretical biology regarding questions of autonomy and agency. Andrea argues that, where Immanuel Kant believed we should explain biological stuff and inanimate stuff the same way- via mechanistic explanations, Hegel believed to explain the biological stuff, we should leverage the fact the biological organisms have intrinsic purpose… agency. And, Hegel's approach is in line with what's called the enactive approach in cognitive science, which has a long history and continues to thrive. Andrea explains all of that during our discussion. One reason I invited Andrea on is because these issues get the heart of what some of us care about, which is, what are the differences and similarities between our natural intelligence and engineered artificial intelligence? Why should we care about those differences? A large language model isn't alive, but does it have a mind? Should we call what it does cognition? What are the relations between life, mind, cognition, intelligence, consciousness? Those kinds of questions. We even discuss why the famed octopus might be really intelligent but not conscious.
Andrea Gambarotto
Gambarotto papers
Enactivism and the Hegelian stance on intrinsic purposiveness
Body plan organization and the evolution of conscious agency
Blog: Dialectical Systems
Papers also mentioned
Weber & Varela 2002: Life after Kant: Natural purposes and the autopoietic foundations of biological individuality.
Mossio & Bich 2014: What makes biological organisation teleological?
Bechtel & Bich & 2021: Grounding cognition: heterarchical control mechanisms in biology.
Pessoa 2026: Beyond networks: Toward adaptive models of biological complexity.
Levins 1998: Dialectics and Systems Theory.
Barandiaran & Moreno 2006: On What Makes Certain Dynamical Systems Cognitive: A Minimally Cognitive Organization Program.
Books mentioned
Linguistic Bodies: The Continuity between Life and Language
Radical Embodied Cognitive Science
An Evolutionary Story of Agency
0:00 - Intro
7:51 - Intrinsic and extrinsic purposiveness
14:39 - Hegel, Kant, and autonomy
28:07 - Constraint closure and enactivism
35:13 - How Hegel and Enactivitsm agree
43:58 - Dialectics
57:44 - Brain activity and enactivism
1:20:02 - Heidegger and cognitive science
1:26:59 - Artificial intelligence and Hegel
1:29:15 - Mind agency decoupling
1:43:38 - Evolution of conscious agency
1:52:42 - Heterarchy via McCulloch - Support the show to get full episodes, full archive, and join the Discord community.
The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.
Read more about our partnership.
Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Daniel Levenstein started his NeuroAI and Dynamics Lab at Yale University about a year ago. We briefly discuss what it's like to transition from a postdoc to a principle investigator, i.e. head of the lab. But most mostly we discuss his work and ideas. Dan studies spontaneous neural activity during sleep, specially in brain areas like hippocampus and cortex, and how this internally generated spontaneous activity is related to learning and memory and navigation. Really, he used to study those processes directly through experimental brain recording datasets. These days he builds and studies models of those processes, using AI models and seeing how their dynamics and functions match what we see in brains.
Levenstein Lab
Social: @dlevenstein.bsky.social
Related papers
On the Role of Theory and Modeling in Neuroscience
The problem-ladenness of theory
Sequential predictive learning is a unifying theory for hippocampal representation and replay
0:00 - Intro
9:12 - Neuro-AI
18:23 - Experiment vs theory
20:36 - Ground vs active state neuron activity
25:38 - Beginning a lab
31:34 - Sleep and Internally generated activity
40:02 - Spiking neural networks
52:13 - Naturalistic neuro-AI
59:52 - Cognitive maps, world models
1:04:16 - Weasel words and motifs
1:08:17 - Transformers and brains
1:18:53 - AI vs biology
1:24:32 - Neuroscience theory - Support the show to get full episodes, full archive, and join the Discord community.
The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.
Read more about our partnership.
Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Marco Facchin is a postdoctoral philosopher of neuroscience and cognitive sciences more broadly at the University of Antwerp. He and his colleague Farid Zahnoun recently hosted a workshop called Beyond Neuro-computationalism with themselves and a handful of speakers, almost all of whom have been on Brain Inspired. In that workshop, they discussed many topics around this sort of forever ongoing reassessment in neuroscience and philosophy about how best to think about cognition, the role of brains, embodied, enactive, embedded, extended - known together as 4E cognition - how much biological detail matters for a good explanation, and so on. The talks from that workshop are online, and I'll link to them in the show notes. So today Marco and I discuss how that all went, and many of the topics and themes I just mentioned, plus his own work and ideas along those lines.
Marco Facchin
Social: @marcofacchin.bsky.social
Beyond neuro-computationalism talks.
Why can’t we say what cognition is (at least for the time being)
Predictive processing and anti-representationalism
Defusing the Representation-Hungry Challenge
Structural representations do not meet the job description challenge
Structure and function in the predictive brain
Read the transcript.
0:00 - Intro
3:30 - Beyond neuro-computationalism
14:02 - Vicente Raja motifs
17:58 - 4E cognition
32:36 - Philosophy and neuroscience
42:18 - The problem with predictive processing
48:56 - Role of AI in understanding brains and minds
54:05 - Metabolic constraints
1:07:58 - A philosopher's view of neuroscience
1:13:27 - A-lieving and AI
1:25:47 - AI consciousness - Support the show to get full episodes, full archive, and join the Discord community.
The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.
Read more about our partnership.
Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Alison Barth runs the Barth Lab at Carnegie Mellon University, where they use learning experiments in mice to try to figure out how the cortex works. As you may know, the brain in general but also the cortex itself is made up of a large variety neuron cell types, with different activity properties. Alison has the gritty job of identifying those different cell types in sensory cortex, and seeing how they change when animals learn to associate rewards with sensory stimulation. So unlike many of the guests, who take a much more zoomed out view and look at how populations of neurons carry out some function, Alison is happiest down at the cellular level. So we talk about her work, why she prefers to work at that scale, and a variety of related topics.
Barth Lab.
Related papers
Barth lab publications.
Learning, prediction accuracy, and neural plasticity in sensory cortex.
Read the transcript.
0:00 - Intro
4:24 - Alison's trajectory to learning and memory
21:02 - Automated mouse learning experiments
25:34 - What is success in this line of work?
32:34 - How many cell types do we need to explain?
34:33 - Current experiments
38:11 - How does cortex work?
45:19 - Predictive processing
1:02:01 - Obstacles
1:10:41 - Role of AI
1:33:38 - Moving forward - Support the show to get full episodes, full archive, and join the Discord community.
The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists.
Read more about our partnership.
Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released.
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Kathryn Nave is a Leverhulme Trust Early Career Fellow at the University of Edinburgh, and the author of the book A Drive to Survive: The Free Energy Principle and the Meaning of Life. In the book, Kate dives deep into the free energy principle and active inference, which are popular approaches to studying brains, minds, and organisms in general, and which are being used in artificial intelligence. Ultimately, Kate finds these approaches come up short as explanatory frameworks for life, and autonomy, and intelligence. Instead, Kate and many others advocate a framework that Kate calls constraint closure or closure of constraints, but also goes by the name organizational closure. This is a concept from philosophy and theoretical biology that people like Alvaro Moreno and Matteo Mossio have put forth in their 2015 book Biological Autonomy. The core ideas are also found in various forms from people like Robert Rosen, Stuart Kauffman, Alicia Juarrero, Terrence Deacon, and others. We discuss what constraint closure is, why Kate thinks it's a solid foundation to build on, and what if anything it means for cognitive science and brain sciences to embrace this constraint closure view. I highly recommend the book even if you're looking for a primer on the free energy principle and active inference. As we discuss, Kate's journalism experience has helped her become a wonderful communicator of these notoriously difficult concepts.
Kathryn Nave
@kathrynnave; @kathrynnave.eurosky.social.
A Drive to Survive: The Free Energy Principle and the Meaning of Life
Related episode:
BI 241 Johannes Jaeger: Agency and the Cyborg Myth
Mentioned in the episode:
We Need To Rewild The Internet
Beyond Control: Finding the Purpose of Enactive Cognitive Science
Read the transcript.
0:00 - Intro
5:39 - Journalism back to philosophy
15:56 - How Kate got into predictive processing etc.
21:30 - Predictive processing and phenomenology
30:45 - Organizational closure
37:37 - Constraint closure beyond the single cell
45:04 - Brain as metabolic
50:12 - Basal cognition
52:13 - Degeneracy
55:08 - Neutral networks
1:00:33 - AI and autonomy
1:08:12 - Meaning and mind
1:10:02 - Why do we need brains?
1:17:33 - Reframe neuroscience?
1:23:51 - Reifying models
1:27:43 - Free energy principle and active inference
1:37:16 - Tolerating as much variability as possible
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About Brain Inspired
Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.
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