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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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.

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

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.

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

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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.

Cristopher Moore is a professor at the Santa Fe Institute in New Mexico, and he is a computation and computational complexity expert. He recently joined a us in my complexity discussion group, and answered a bunch of our questions, but I wasn't done with him regarding what, if anything, computational complexity has to do understanding how brains and minds work. So that's why he's here today, and we discuss a wide variety of topics related to AI, computation, computational complexity, and cognition.

Cris's Homepage

Book:

The Nature of Computation

Related papers

What Is a Macrostate? Subjective Observations and Objective Dynamics

0:00 - Intro
4:24 - The Nature of Computation
9:14 - Computational complexity
28:22 - Real mathematics
35:08 - Current state of AI
39:04 - Computational complexity in the AI world
47:53 - Cognition, creation, problems
56:16 - Rugged landscapes and generalization
1:13:52 - What is computation?
1:32:31 - How would you study the brain?

Support the show to get full episodes, full archive, and join the Discord community.

he 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.

Check out this story: Beyond the algorithmic oracle: Rethinking machine learning in behavioral neuroscience

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.

Neuroscience studies in part the relation between brain activity and behaviors. But, what is a behavior? It's a simple question, but there's no simple answer. For example, you're behaving right now, whatever you're doing, even if you're not doing much. When you cross the street, how many behaviors do you use? When you sleep, what behaviors do you do? Hopefully these simple examples make you think about how difficult it can be call some single movement a behavior.

Nedah Nemati is a philosopher of neuroscience at Columbia University. I met Nedah at a workshop a few months ago, where we chatted about the growing trend in neuroscience toward what is sometimes being called "naturalistic neuroscience," which really means varying levels of allowing organisms to behave more freely, less constrained, than traditional neuroscience experiments that seek to minimize unrelated to the behavior or cognition you want to isolate to study and explain. In more extreme cases, researches will try in the lab to emulate as much as possible the ecological world a particular organism has evolved to exist in, or even perform the experiments out of the lab, in the wild, so to speak. So a good part of our discussion revolves around this trend, and what counts as a "naturalistic" behavior, and how the tools we use to perform experiments shape the experiments and the scientific questions themselves.

Nedah has a neuroscience background, but in her philosophical work she has embedded herself into various neuroscience labs to better understand how the experiences of the researchers themselves, called their lived experiences, shape the assumptions and questions in their science. As an example, we discuss her work looking into the neuroscience of sleep from over a 100 years ago to today. When a modern neuroscientist studies sleep, are they studying the same thing a scientist claimed to be studying 100 years ago, even though they claimed to be studying sleep back then as well?

Nedah's website.

Transmitter piece:

Beyond the algorithmic oracle: Rethinking machine learning in behavioral neuroscience

Related papers

Rethinking Neuroscientific Methodology: Lived Experience in Behavioral Studies

What is ‘Natural’ about Naturalistic Neuroscience?

0:00 - Intro
5:00 - Philosopher in a lab
20:21 - Sleep as behavior
22:22 - How the study of "sleep" has changed
27:24 - How tools and methods shape definitions
46:07 - Naturalistic neuroscience
1:00:47 - Naturalistic vs experimental
1:14:32 - How tools change theory
1:16:57 - Lived experience
1:26:28 - Lived experience vs. bias
1:37:09 - AI and engineering in neuroscience
1:45:29 - Should a lab hire a philosopher?