Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand i...
BI 200 Grace Hwang and Joe Monaco: The Future of NeuroAI
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Joe Monaco and Grace Hwang co-organized a recent workshop I participated in, the 2024 BRAIN NeuroAI Workshop. You may have heard of the BRAIN Initiative, but in case not, BRAIN is is huge funding effort across many agencies, one of which is the National Institutes of Health, where this recent workshop was held. The BRAIN Initiative began in 2013 under the Obama administration, with the goal to support developing technologies to help understand the human brain, so we can cure brain based diseases.
BRAIN Initiative just became a decade old, with many successes like recent whole brain connectomes, and discovering the vast array of cell types. Now the question is how to move forward, and one area they are curious about, that perhaps has a lot of potential to support their mission, is the recent convergence of neuroscience and AI... or NeuroAI. The workshop was designed to explore how NeuroAI might contribute moving forward, and to hear from NeuroAI folks how they envision the field moving forward. You'll hear more about that in a moment.
That's one reason I invited Grace and Joe on. Another reason is because they co-wrote a position paper a while back that is impressive as a synthesis of lots of cognitive sciences concepts, but also proposes a specific level of abstraction and scale in brain processes that may serve as a base layer for computation. The paper is called Neurodynamical Computing at the Information Boundaries, of Intelligent Systems, and you'll learn more about that in this episode.
Joe's NIH page.
Grace's NIH page.
Twitter:
Related papers
Neurodynamical Computing at the Information Boundaries of Intelligent Systems.
Cognitive swarming in complex environments with attractor dynamics and oscillatory computing.
Spatial synchronization codes from coupled rate-phase neurons.
Oscillators that sync and swarm.
Mentioned
A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications.
Recalling Lashley and reconsolidating Hebb.
BRAIN NeuroAI Workshop (Nov 12–13)
NIH BRAIN NeuroAI Workshop Program Book
NIH VideoCast – Day 1 Recording – BRAIN NeuroAI Workshop
NIH VideoCast – Day 2 Recording – BRAIN NeuroAI Workshop
Neuromorphic Principles in Biomedicine and Healthcare Workshop (Oct 21–22)
NPBH 2024
BRAIN Investigators Meeting 2020 Symposium & Perspective Paper
BRAIN 2020 Symposium on Dynamical Systems Neuroscience and Machine Learning (YouTube)
Neurodynamical Computing at the Information Boundaries of Intelligent Systems | Cognitive Computation
NSF/CIRC
Community Infrastructure for Research in Computer and Information Science and Engineering (CIRC) | NSF - National Science Foundation
THOR Neuromorphic Commons - Matrix: The UTSA AI Consortium for Human Well-Being
0:00 - Intro
25:45 - NeuroAI Workshop - neuromorphics
33:31 - Neuromorphics and theory
49:19 - Reflections on the workshop
54:22 - Neurodynamical computing and information boundaries
1:01:04 - Perceptual control theory
1:08:56 - Digital twins and neural foundation models
1:14:02 - Base layer of computation
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1:37:11
BI 199 Hessam Akhlaghpour: Natural Universal Computation
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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 the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Hessam Akhlaghpour is a postdoctoral researcher at Rockefeller University in the Maimon lab. His experimental work is in fly neuroscience mostly studying spatial memories in fruit flies. However, we are going to be talking about a different (although somewhat related) side of his postdoctoral research. This aspect of his work involves theoretical explorations of molecular computation, which are deeply inspired by Randy Gallistel and Adam King's book Memory and the Computational Brain. Randy has been on the podcast before to discuss his ideas that memory needs to be stored in something more stable than the synapses between neurons, and how that something could be genetic material like RNA. When Hessam read this book, he was re-inspired to think of the brain the way he used to think of it before experimental neuroscience challenged his views. It re-inspired him to think of the brain as a computational system. But it also led to what we discuss today, the idea that RNA has the capacity for universal computation, and Hessam's development of how that might happen. So we discuss that background and story, why universal computation has been discovered in organisms yet since surely evolution has stumbled upon it, and how RNA might and combinatory logic could implement universal computation in nature.
Hessam's website.
Maimon Lab.
Twitter: @theHessam.
Related papers
An RNA-based theory of natural universal computation.
The molecular memory code and synaptic plasticity: a synthesis.
Lifelong persistence of nuclear RNAs in the mouse brain.
Cris Moore's conjecture #5 in this 1998 paper.
(The Gallistel book): Memory and the Computational Brain: Why Cognitive Science Will Transform Neuroscience.
Related episodes
BI 126 Randy Gallistel: Where Is the Engram?
BI 172 David Glanzman: Memory All The Way Down
Read the transcript.
0:00 - Intro
4:44 - Hessam's background
11:50 - Randy Gallistel's book
14:43 - Information in the brain
17:51 - Hessam's turn to universal computation
35:30 - AI and universal computation
40:09 - Universal computation to solve intelligence
44:22 - Connecting sub and super molecular
50:10 - Junk DNA
56:42 - Genetic material for coding
1:06:37 - RNA and combinatory logic
1:35:14 - Outlook
1:42:11 - Reflecting on the molecular world
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1:49:07
BI 198 Tony Zador: Neuroscience Principles to Improve AI
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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 the “Brain Inspired” email alerts to be notified every time a new “Brain Inspired” episode is released: https://www.thetransmitter.org/newsletters/
To explore more neuroscience news and perspectives, visit thetransmitter.org.
Tony Zador runs the Zador lab at Cold Spring Harbor Laboratory. You've heard him on Brain Inspired a few times in the past, most recently in a panel discussion I moderated at this past COSYNE conference - a conference Tony co-founded 20 years ago. As you'll hear, Tony's current and past interests and research endeavors are of a wide variety, but today we focus mostly on his thoughts on NeuroAI.
We're in a huge AI hype cycle right now, for good reason, and there's a lot of talk in the neuroscience world about whether neuroscience has anything of value to provide AI engineers - and how much value, if any, neuroscience has provided in the past.
Tony is team neuroscience. You'll hear him discuss why in this episode, especially when it comes to ways in which development and evolution might inspire better data efficiency, looking to animals in general to understand how they coordinate numerous objective functions to achieve their intelligent behaviors - something Tony calls alignment - and using spikes in AI models to increase energy efficiency.
Zador Lab
Twitter: @TonyZador
Previous episodes:
BI 187: COSYNE 2024 Neuro-AI Panel.
BI 125 Doris Tsao, Tony Zador, Blake Richards: NAISys
BI 034 Tony Zador: How DNA and Evolution Can Inform AI
Related papers
Catalyzing next-generation Artificial Intelligence through NeuroAI.
Encoding innate ability through a genomic bottleneck.
Essays
NeuroAI: A field born from the symbiosis between neuroscience, AI.
What the brain can teach artificial neural networks.
Read the transcript.
0:00 - Intro
3:28 - "Neuro-AI"
12:48 - Visual cognition history
18:24 - Information theory in neuroscience
20:47 - Necessary steps for progress
24:34 - Neuro-AI models and cognition
35:47 - Animals for inspiring AI
41:48 - What we want AI to do
46:01 - Development and AI
59:03 - Robots
1:25:10 - Catalyzing the next generation of AI
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1:35:04
BI 197 Karen Adolph: How Babies Learn to Move and Think
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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 the “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.
Karen Adolph runs the Infant Action Lab at NYU, where she studies how our motor behaviors develop from infancy onward. We discuss how observing babies at different stages of development illuminates how movement and cognition develop in humans, how variability and embodiment are key to that development, and the importance of studying behavior in real-world settings as opposed to restricted laboratory settings. We also explore how these principles and simulations can inspire advances in intelligent robots. Karen has a long-standing interest in ecological psychology, and she shares some stories of her time studying under Eleanor Gibson and other mentors.
Finally, we get a surprise visit from her partner Mark Blumberg, with whom she co-authored an opinion piece arguing that "motor cortex" doesn't start off with a motor function, oddly enough, but instead processes sensory information during the first period of animals' lives.
Infant Action Lab (Karen Adolph's lab)
Sleep and Behavioral Development Lab (Mark Blumberg's lab)
Related papers
Motor Development: Embodied, Embedded, Enculturated, and Enabling
An Ecological Approach to Learning in (Not and) Development
An update of the development of motor behavior
Protracted development of motor cortex constrains rich interpretations of infant cognition
Read the transcript.
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1:29:31
BI 196 Cristina Savin and Tim Vogels with Gaute Einevoll and Mikkel Lepperød
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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.
This is the second conversation I had while teamed up with Gaute Einevoll at a workshop on NeuroAI in Norway. In this episode, Gaute and I are joined by Cristina Savin and Tim Vogels. Cristina shares how her lab uses recurrent neural networks to study learning, while Tim talks about his long-standing research on synaptic plasticity and how AI tools are now helping to explore the vast space of possible plasticity rules.
We touch on how deep learning has changed the landscape, enhancing our research but also creating challenges with the "fashion-driven" nature of science today. We also reflect on how these new tools have changed the way we think about brain function without fundamentally altering the structure of our questions.
Be sure to check out Gaute's Theoretical Neuroscience podcast as well!
Mikkel Lepperød
Cristina Savin
Tim Vogels
Twitter: @TPVogels
Gaute Einevoll
Twitter: @GauteEinevoll
Gaute's Theoretical Neuroscience podcast.
Validating models: How would success in NeuroAI look like?
Read the transcript, provided by The Transmitter.
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.