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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

01:19:13

BI 094 Alison Gopnik: Child-Inspired AI

Brain Inspired ·

2021/01/08

Alison and I discuss her work to accelerate learning and thus improve AI by studying how children learn, as Alan Turing suggested in his famous 1950 paper. The ways children learn are via imitation, by learning abstract causal models, and active learning by implementing a high exploration/exploitation ratio. We also discuss child consciousness, psychedelics, the concept of life history, the role of grandparents and elders, and lots more. Alison's Website.Cognitive Development and Learning

en-US

01:06:31

BI 093 Dileep George: Inference in Brain Microcircuits

Brain Inspired ·

2020/12/29

Dileep and I discuss his theoretical account of how the thalamus and cortex work together to implement visual inference. We talked previously about his Recursive Cortical Network (RCN) approach to visual inference, which is a probabilistic graph model that can solve hard problems like CAPTCHAs, and more recently we talked about using his RCNs with cloned units to account for cognitive maps related to the hippocampus. On this episode, we walk through how RCNs can map onto thalamo-cortical circuits so

en-US

01:42:12

BI 092 Russ Poldrack: Cognitive Ontologies

Brain Inspired ·

2020/12/15

Russ and I discuss cognitive ontologies - the "parts" of the mind and their relations - as an ongoing dilemma of how to map onto each other what we know about brains and what we know about minds. We talk about whether we have the right ontology now, how he uses both top-down and data-driven approaches to analyze and refine current ontologies, and how all this has affected his own thinking about minds. We also discuss some of the current meta-science issues and challenges in neuroscience and AI, and

en-US

01:28:19

BI 091 Carsen Stringer: Understanding 40,000 Neurons

Brain Inspired ·

2020/12/04

Carsen and I discuss how she uses 2-photon calcium imaging data from over 10,000 neurons to understand the information processing of such large neural population activity. We talk about the tools she makes and uses to analyze the data, and the type of high-dimensional neural activity structure they found, which seems to allow efficient and robust information processing. We also talk about how these findings may help build better deep learning networks, and Carsen's thoughts on how to improve the

en-US

01:38:57

BI 090 Chris Eliasmith: Building the Human Brain

Brain Inspired ·

2020/11/23

Chris and I discuss his Spaun large scale model of the human brain (Semantic Pointer Architecture Unified Network), as detailed in his book How to Build a Brain. We talk about his philosophical approach, how Spaun compares to Randy O'Reilly's Leabra networks, the Applied Brain Research Chris co-founded, and I have guest questions from Brad Aimone, Steve Potter, and Randy O'Reilly. Chris's website.Applied Brain Research.The book: How to Build a Brain.Nengo (you can run Spaun).Paper summary of

en-US

01:26:52

BI 089 Matt Smith: Drifting Cognition

Brain Inspired ·

2020/11/12

Matt and I discuss how cognition and behavior drifts over the course of minutes and hours, and how global brain activity drifts with it. How does the brain continue to produce steady perception and action in the midst of such drift? We also talk about how to think about variability in neural activity. How much of it is noise and how much of it is hidden important activity? Finally, we discuss the effect of recording more and more neurons simultaneously, collecting bigger and bigger datasets, plus

en-US

01:39:08

BI 088 Randy O’Reilly: Simulating the Human Brain

Brain Inspired ·

2020/11/02

Randy and I discuss his LEABRA cognitive architecture that aims to simulate the human brain, plus his current theory about how a loop between cortical regions and the thalamus could implement predictive learning and thus solve how we learn with so few examples. We also discuss what Randy thinks is the next big thing neuroscience can contribute to AI (thanks to a guest question from Anna Schapiro), and much more. Computational Cognitive Neuroscience Laboratory.The papers we discuss or mention:The

en-US

01:23:00

BI 087 Dileep George: Cloning for Cognitive Maps

Brain Inspired ·

2020/10/23

When a waiter hands me the bill, how do I know whether to pay it myself or let my date pay? On this episode, I get a progress update from Dileep on his company, Vicarious, since Dileep's last episode. We also talk broadly about his experience running Vicarious to develop AGI and robotics. Then we turn to his latest brain-inspired AI efforts using cloned structured probabilistic graph models to develop an account of how the hippocampus makes a model of the world and represents our cognitive maps in

en-US

01:35:43

BI 086 Ken Stanley: Open-Endedness

Brain Inspired ·

2020/10/12

Ken and I discuss open-endedness, the pursuit of ambitious goals by seeking novelty and interesting products instead of advancing directly toward defined objectives. We talk about evolution as a prime example of an open-ended system that has produced astounding organisms, Ken relates how open-endedness could help advance artificial intelligence and neuroscience, and we discuss a range of topics related to the general concept of open-endedness, and Ken takes a couple questions from Stefan Leijnen and

en-US

01:43:41

BI 085 Ida Momennejad: Learning Representations

Brain Inspired ·

2020/09/30

Ida and I discuss the current landscape of reinforcement learning in both natural and artificial intelligence, and how the old story of two RL systems in brains - model-free and model-based - is giving way to a more nuanced story of these two systems constantly interacting and additional RL strategies between model-free and model-based to drive the vast repertoire of our habits and goal-directed behaviors. We discuss Ida’s work on one of those “in-between” strategies, the successor representation RL

en-US

01:56:01

BI 084 György Buzsáki and David Poeppel

Brain Inspired ·

2020/09/15

David, Gyuri, and I discuss the issues they argue for in their back and forth commentaries about the importance of neuroscience and psychology, or implementation-level and computational-level, to advance our understanding of brains and minds - and the names we give to the things we study. Gyuri believes it’s time we use what we know and discover about brain mechanisms to better describe the psychological concepts we refer to as explanations for minds; David believes the psychological concepts are

en-US

01:13:16

BI 083 Jane Wang: Evolving Altruism in AI

Brain Inspired ·

2020/09/05

Jane and I discuss the relationship between AI and neuroscience (cognitive science, etc), from her perspective at Deepmind after a career researching natural intelligence. We also talk about her meta-reinforcement learning work that connects deep reinforcement learning with known brain circuitry and processes, and finally we talk about her recent work using evolutionary strategies to develop altruism and cooperation among the agents in a multi-agent reinforcement learning

en-US

02:15:38

BI 082 Steve Grossberg: Adaptive Resonance Theory

Brain Inspired ·

2020/08/26

Steve and I discuss his long and productive career as a theoretical neuroscientist. We cover his tried and true method of taking a large body of psychological behavioral findings, determining how they fit together and what’s paradoxical about them, developing design principles, theories, and models from that body of data, and using experimental neuroscience to inform and confirm his model predictions. We talk about his Adaptive Resonance Theory (ART) to describe how our brains are self-organizing,

en-US

01:22:05

BI 081 Pieter Roelfsema: Brain-propagation

Brain Inspired ·

2020/08/16

Pieter and I discuss his ongoing quest to figure out how the brain implements learning that solves the credit assignment problem, like backpropagation does for neural networks. We also talk about his work to understand how we perceive individual objects in a crowded scene, his neurophysiological recordings in support of the global neuronal workspace hypothesis of consciousness, and the visual prosthetic device he’s developing to cure blindness by directly stimulating early visual

en-US

01:31:09

BI 080 Daeyeol Lee: Birth of Intelligence

Brain Inspired ·

2020/08/06

Daeyeol and I discuss his bookBirth of Intelligence: From RNA to Artificial Intelligence, which argues intelligence is a function of and inseparable from life, bound by self-replication and evolution. The book covers a ton of neuroscience related to decision making and learning, though we focused on a few theoretical frameworks and ideas like division of labor and principal-agent relationships to understand how our brains and minds are related to our genes, how AI is related to humans (for now),

en-US

01:19:04

BI 079 Romain Brette: The Coding Brain Metaphor

Brain Inspired ·

2020/07/27

Romain and I discuss his theoretical/philosophical work examining how neuroscientists rampantly misuse the word "code" when making claims about information processing in brains. We talk about the coding metaphor, various notions of information, the different roles and facets of mental representation, perceptual invariance, subjective physics, process versus substance metaphysics, and the experience of writing a Behavior and Brain Sciences article (spoiler: it's a demanding yet rewarding

en-US

01:14:37

BI 078 David and John Krakauer: Part 2

Brain Inspired ·

2020/07/17

In this second part of our conversation David, John, and I continue to discuss the role of complexity science in the study of intelligence, brains, and minds. We also get into functionalism and multiple realizability, dynamical systems explanations, the role of time in thinking, and more. Be sure to listen to the first part, which lays the foundation for what we discuss in this episode. Notes: David’s page at the Santa Fe Institute.John’s BLAM lab website.Follow SFI on twitter:

en-US

01:33:04

BI 077 David and John Krakauer: Part 1

Brain Inspired ·

2020/07/14

David, John, and I discuss the role of complexity science in the study of intelligence. In this first part, we talk about complexity itself, its role in neuroscience, emergence and levels of explanation, understanding, epistemology and ontology, and really quite a bit more. Notes: David’s page at the Santa Fe Institute.John’s BLAM lab website.Follow SFI on twitter: @sfiscience.BLAM on Twitter: @blamlabRelated Krakauer stuff:At the limits of thought. An Aeon article by DavidComplex Time:

en-US

01:16:03

BI 075 Jim DiCarlo: Reverse Engineering Vision

Brain Inspired ·

2020/06/24

Jim and I discuss his reverse engineering approach to visual intelligence, using deep models optimized to perform object recognition tasks. We talk about the history of his work developing models to match the neural activity in the ventral visual stream, how deep learning connects with those models, and some of his recent work: adding recurrence to the models to account for more difficult object recognition, using unsupervised learning to account for plasticity in the visual stream, and controlling

en-US

234 results

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