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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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.
BI 221 Ann Kennedy: Theory Beneath the Cortical Surface
Brain Inspired
1 hour 43 minutes 37 seconds
1 week ago
BI 221 Ann Kennedy: Theory Beneath the Cortical Surface
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Ann Kennedy is Associate Professor at Scripps Research Institute and runs the Laboratory for Theoretical Neuroscience and Behavior.
Among other things, Ann has been studying how processes important in life, like survival, threat response, motivation, and pain, are mediated through subcortical brain areas like the hypothalamus. She also pays attention to the time course those life processes require, which has led her to consider how the expression of things like proteins help shape neural processes throughout the brain, so we can behave appropriately in those different contexts.
You'll hear us talk about how this is still a pretty open field in theoretical neuroscience, unlike the historically heavy use of theory in popular brain areas throughout the cortex, and the historically narrow focus on spikes or action potentials as the only game in town when it comes to neural computation. We discuss that and I link in the show notes to a commentary piece Ann wrote, in which she argues for both top-down and bottom-up theoretical approaches.
I also link to her papers about the early evolution of nervous systems, how heterogeneity or diversity of neurons is an advantage for neural computations, and we discuss a kaggle competition she developed to benchmark automated behavioral labels of behaving organisms, so that despite different researchers using different recording systems and setups, analyzing those data will produce consistent labels to better compare across labs and aggregated bigger and better data sets.
Laboratory for Theoretical Neuroscience and Behavior.
Social:
@antihebbiann.bsky.social
@Antihebbiann
The Kaggle competition Ann developed to generalize behavior categorization.
Related papersDynamics of neural activity in early nervous system evolution.Theoretical neuroscience has room to grow.
Neural heterogeneity controls computations in spiking neural networks.
A parabrachial hub for the prioritization of survival behavior.
An approximate line attractor in the hypothalamus encodes an aggressive state.
0:00 - Intro
3:36 - Why study subcortical areas?
13:30 - Evolution
15:06 - Dynamical systems and time scales
21:32 - NeuroAI
28:37 - Before there were brains
33:11 - Endogenous spontaneous activity
40:09 - Natural vs artificial
43:09 - Different is more - heterogeneity
45:32 - Neuromodulators and neuropeptide functions
55:47 - Heterogeneity: manifolds, subspaces, and gain
1:02:43 - Control knobs
1:09:45 - Theoretical neuroscience has room to grow
1:19:59 - Hypothalamus
1:20:57 - Subcortical vs "higher" cognition
1:24:53 - 4E cognition
1:26:56 - Behavior benchmarking
1:37:26 - Current challenges
1:39:46 - Advice to young researchers
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.
