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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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

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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.

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

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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.

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

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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.

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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Johannes Jaeger is Associate Faculty at the Complexity Science Hub in Vienna. He's also a freelance researcher, a philosopher, and an educator. He's here today to educate us about some of the fundamental differences between living organisms and machines, like AI, and why we should care about those differences. We discuss his paper The Cyborg Myth, an argument for why we can't seamlessly replace ourselves with machine parts over time. We talk about judgment and relevance realization as a fundamental difference between AI and living organisms -the ability to judge what is a relevant problem to solve in the first place, assuming intelligence is about problem solving. We also discuss what agency is in living systems, and why AI agents are something completely different. I think you get the recurring theme here. Yogi is writing a book called Beyond the Age of Machines, a work in progress and you can read it as he writes it on his expanding possibilities website.

Untethered in the Platonic Realm (Yogi's website)

Expanding Possibilities

Book in progress: Beyond the Age of Machines

Mastadon: @yoginho

Related

The Cyborg Myth.(talk version here)

Naturalizing relevance realization: why agency and cognition are fundamentally not computational.

Artificial intelligence is algorithmic mimicry: why artificial "agents" are not (and won't be) proper agents.

Read the transcript.

0:00 - Intro
7:11 - The cyborg myth
15:16 - Judgment
24:22 - Consciousness
28:56 - Agency
36:40 - Relevance realization and energy efficiency
46:44 - Metabolism as a metaphor
1:00:39 - Robert Rosen
1:06:20 - Conceptual engineering
1:12:55 - Dynamics and computation
1:23:07 - Agency book