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PODCAST

Brain Inspired

Paul Middlebrooks

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

science natural sciences

All Episodes

01:33:18

BI 122 Kohitij Kar: Visual Intelligence

Brain Inspired ·

2021/12/12

Support the show to get full episodes and join the Discord community. Ko and I discuss a range of topics around his work to understand our visual intelligence. Ko was a postdoc in James Dicarlo's lab, where he helped develop the convolutional neural network models that have become the standard for explaining core object recognition. He is starting his own lab at York University, where he will continue to expand and refine the models, adding important biological details and incorporating

en-US

01:43:12

BI 121 Mac Shine: Systems Neurobiology

Brain Inspired ·

2021/12/02

Support the show to get full episodes and join the Discord community. Mac and I discuss his systems level approach to understanding brains, and his theoretical work suggesting important roles for the thalamus, basal ganglia, and cerebellum, shifting the dynamical landscape of brain function within varying behavioral contexts. We also discuss his recent interest in the ascending arousal system and neuromodulators. Mac thinks the neocortex has been the sole focus of too much neuroscience

en-US

01:40:02

BI 120 James Fitzgerald, Andrew Saxe, Weinan Sun:...

Brain Inspired ·

2021/11/21

Support the show to get full episodes and join the Discord community. James, Andrew, and Weinan discuss their recent theory about how the brain might use complementary learning systems to optimize our memories. The idea is that our hippocampus creates our episodic memories for individual events, full of particular details. And through a complementary process, slowly consolidates those memories within our neocortex through mechanisms like hippocampal replay. The new idea in their work

en-US

01:06:36

BI 119 Henry Yin: The Crisis in Neuroscience

Brain Inspired ·

2021/11/11

Support the show to get full episodes and join the Discord community. Henry and I discuss why he thinks neuroscience is in a crisis (in the Thomas Kuhn sense of scientific paradigms, crises, and revolutions). Henry thinks our current concept of the brain as an input-output device, with cognition in the middle, is mistaken. He points to the failure of neuroscience to successfully explain behavior despite decades of research. Instead, Henry proposes the brain is one big hierarchical set of

en-US

01:36:08

BI 118 Johannes Jäger: Beyond Networks

Brain Inspired ·

2021/11/01

Support the show to get full episodes and join the Discord community. Johannes (Yogi) is a freelance philosopher, researcher & educator. We discuss many of the topics in his online course, Beyond Networks: The Evolution of Living Systems. The course is focused on the role of agency in evolution, but it covers a vast range of topics: process vs. substance metaphysics, causality, mechanistic dynamic explanation, teleology, the important role of development mediating genotypes, phenotypes,

en-US

01:32:09

BI 117 Anil Seth: Being You

Brain Inspired ·

2021/10/19

Support the show to get full episodes and join the Discord community. Anil and I discuss a range of topics from his book, BEING YOU A New Science of Consciousness. Anil lays out his framework for explaining consciousness, which is embedded in what he calls the "real problem" of consciousness. You know the "hard problem", which was David Chalmers term for our eternal difficulties to explain why we have subjective awareness at all instead of being unfeeling, unexperiencing machine-like

en-US

01:31:20

BI 116 Michael W. Cole: Empirical Neural Networks

Brain Inspired ·

2021/10/12

Support the show to get full episodes and join the Discord community. Mike and I discuss his modeling approach to study cognition. Many people I have on the podcast use deep neural networks to study brains, where the idea is to train or optimize the model to perform a task, then compare the model properties with brain properties. Mike's approach is different in at least two ways. One, he builds the architecture of his models using connectivity data from fMRI recordings. Two, he doesn't train

en-US

01:23:41

BI 115 Steve Grossberg: Conscious Mind, Resonant Brain

Brain Inspired ·

2021/10/02

Support the show to get full episodes and join the Discord community. Steve and I discuss his book Conscious Mind, Resonant Brain: How Each Brain Makes a Mind. The book is a huge collection of his models and their predictions and explanations for a wide array of cognitive brain functions. Many of the models spring from his Adaptive Resonance Theory (ART) framework, which explains how networks of neurons deal with changing environments while maintaining self-organization and retaining

en-US

01:38:07

BI 114 Mark Sprevak and Mazviita Chirimuuta:...

Brain Inspired ·

2021/09/22

Support the show to get full episodes and join the Discord community. Mark and Mazviita discuss the philosophy and science of mind, and how to think about computations with respect to understanding minds. Current approaches to explaining brain function are dominated by computational models and the computer metaphor for brain and mind. But there are alternative ways to think about the relation between computations and brain function, which we explore in the discussion. We also talk about the

en-US

01:30:38

BI 113 David Barack and John Krakauer: Two Views On...

Brain Inspired ·

2021/09/12

Support the show to get full episodes and join the Discord community. David and John discuss some of the concepts from their recent paper Two Views on the Cognitive Brain, in which they argue the recent population-based dynamical systems approach is a promising route to understanding brain activity underpinning higher cognition. We discuss mental representations, the kinds of dynamical objects being used for explanation, and much more, including David's perspectives as a practicing

en-US

00:57:25

BI ViDA Panel Discussion: Deep RL and Dopamine

Brain Inspired ·

2021/09/02

en-US

01:13:56

BI 112 Ali Mohebi and Ben Engelhard: The Many Faces...

Brain Inspired ·

2021/08/26

BI 112: Ali Mohebi and Ben Engelhard The Many Faces of Dopamine Announcement: Ben has started his new lab and is recruiting grad students. Check out his lab here and apply! Engelhard Lab Ali and Ben discuss the ever-expanding discoveries about the roles dopamine plays for our cognition. Dopamine is known to play a role in learning – dopamine (DA) neurons fire

en-US

01:20:32

BI NMA 06: Advancing Neuro Deep Learning Panel

Brain Inspired ·

2021/08/19

en-US

01:23:50

BI NMA 05: NLP and Generative Models Panel

Brain Inspired ·

2021/08/13

BI NMA 05: NLP and Generative Models Panel This is the 5th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the 2nd of 3 in the deep learning series. In this episode, the panelists discuss their experiences “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative

en-US

00:59:21

BI NMA 04: Deep Learning Basics Panel

Brain Inspired ·

2021/08/06

BI NMA 04: Deep Learning Basics Panel This is the 4th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the first of 3 in the deep learning series. In this episode, the panelists discuss their experiences with some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons,

en-US

01:38:04

BI 111 Kevin Mitchell and Erik Hoel: Agency,...

Brain Inspired ·

2021/07/28

Erik, Kevin, and I discuss... well a lot of things. Erik's recent novel The Revelations is a story about a group of neuroscientists trying to develop a good theory of consciousness (with a murder mystery plot). Kevin's book Innate - How the Wiring of Our Brains Shapes Who We Are describes the messy process of getting from DNA, traversing epigenetics and development, to our personalities. We talk about both books, then dive deeper into topics like whether brains evolved for moving our

en-US

01:00:48

BI NMA 03: Stochastic Processes Panel

Brain Inspired ·

2021/07/22

Panelists: Yael Niv.@yael_nivKonrad Kording@KordingLab.Previous BI episodes:BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.BI 014 Konrad Kording: Regulators, Mount Up!Sam Gershman.@gershbrain.Previous BI episodes:BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?BI 028 Sam Gershman: Free Energy Principle & Human Machines.Tim Behrens.@behrenstim.Previous BI episodes:BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.BI 024 Tim

en-US

01:15:28

BI NMA 02: Dynamical Systems Panel

Brain Inspired ·

2021/07/15

Panelists: Adrienne Fairhall.@alfairhall.Bing Brunton.@bingbrunton.Kanaka Rajan.@rajankdr.BI 054 Kanaka Rajan: How Do We Switch Behaviors? This is the second in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with linear systems, real neurons, and dynamic networks. Other panels: First panel, about model fitting, GLMs/machine learning, dimensionality

en-US

01:27:12

BI NMA 01: Machine Learning Panel

Brain Inspired ·

2021/07/12

Panelists: Athena Akrami: @AthenaAkrami.Demba Ba.Gunnar Blohm: @GunnarBlohm.Kunlin Wei. This is the first in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with model fitting, GLMs/machine learning, dimensionality reduction, and deep learning. Other panels: Second panel, about linear systems, real neurons, and dynamic networks.Third panel, about

en-US

01:25:02

BI 110 Catherine Stinson and Jessica Thompson:...

Brain Inspired ·

2021/07/06

Catherine, Jess, and I use some of the ideas from their recent papers to discuss how different types of explanations in neuroscience and AI could be unified into explanations of intelligence, natural or artificial. Catherine has written about how models are related to the target system they are built to explain. She suggests both the model and the target system should be considered as instantiations of a specific kind of phenomenon, and explanation is a product of relating the model and the target

en-US

233 results

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