Brain Inspired

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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.

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Maxim Raginsky is a professor at the University of Illinois at Urbana-Champaign. Max describes himself as interested in probability and stochastic processes, deterministic and stochastic control, machine learning, optimization, and information theory. Today we mostly lean on his control theory expertise, although you'll here his knowledge is vast in many other domains, even some neuroscience. I wanted his control theory perspective on neuroscience, AI, and biological autonomy, so we dance around a lot of topics related to those. Max also writes a substack called The Art of the Realizable, from which I drew during parts of our conversation.

Maxim Raginsky

Substack: The Art of the Realizable. 

Related papers

Biological Autonomy

Control-related episodes

BI 143 Rodolphe Sepulchre: Mixed Feedback Control

BI 205 Dmitri Chklovskii: Neurons Are Smarter Than You Think

0:00 - Intro
3:07 - Low energy lifestyle
4:27 - Engineering and philosophy?
13:52 - Brains vs AI
19:45 - Inferring the inside from behavior
30:57 - Analog vs digital
41:32 - Is the brain a control system?
46:50 - Willems control
1:02:12 - Control vs cybernetics
1:13:26 - A control perspective on AI vs brains
1:17:46 - AGI
1:21:48 - Turing 1950
1:29:07 - Perceptual control theory and active inference
1:40:09 - Passive control in the brain?
1:41:48 - Computation
1:43:36 - Counting spikes

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.

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

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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