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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

01:22:35

BI 074 Ginger Campbell: Are You Sure?

Brain Inspired ·

2020/06/16

Ginger and I discuss her book Are You Sure? The Unconscious Origins of Certainty, which summarizes Richard Burton's work exploring the experience and phenomenal origin of feeling confident, and how the vast majority of our brain processing occurs outside our conscious awareness. Are You Sure? The Unconscious Origins of Certainty.Brain Science Podcast.

en-US

01:25:10

BI 073 Megan Peters: Consciousness and Metacognition

Brain Inspired ·

2020/06/10

Megan and I discuss her work using metacognition as a way to study subjective awareness, or confidence. We talk about using computational and neural network models to probe how decisions are related to our confidence, the current state of the science of consciousness, and her newest project using fMRI decoded neurofeedback to induce particular brain states in subjects so we can learn about conscious and unconscious brain processing. Notes: Visit Megan's cognitive & neural computation

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01:18:53

BI 072 Mazviita Chirimuuta: Understanding,...

Brain Inspired ·

2020/06/01

Mazviita and I discuss the growing divide between prediction and understanding as neuroscience models and deep learning networks become bigger and more complex. She describes her non-factive account of understanding, which among other things suggests that the best predictive models may deliver less understanding. We also discuss the brain as a computer metaphor, and whether it's really possible to ignore all the traditionally "non-computational" parts of the brain like metabolism and other life

en-US

01:10:57

BI 071 J. Patrick Mayo: The Path To Faculty

Brain Inspired ·

2020/05/25

Patrick and I mostly discuss his path from a technician in the then nascent Jim DiCarlo lab, through his graduate school and two postdoc experiences, and finally landing a faculty position, plus the culture and issues in academia in general. We also cover plenty of science, like the role of eye movements in the study of vision, the neuroscience (and concept) of attention, what Patrick thinks of the deep learning hype, and more. But, this is a special episode, less about the science and more

en-US

01:47:07

BI 070 Bradley Love: How We Learn Concepts

Brain Inspired ·

2020/05/15

Brad and I discuss his battle-tested, age-defying cognitive model for how we learn and store concepts by forming and rearranging clusters, how the model maps onto brain areas, and how he's using deep learning models to explore how attention and sensory information interact with concept formation. We also discuss the cognitive modeling approach, Marr's levels of analysis, the term "biological plausibility", emergence and reduction, and plenty more. Notes: Visit Brad’s website.Follow Brad on

en-US

01:26:35

BI 069 David Ferrucci: Machines To Understand Stories

Brain Inspired ·

2020/05/05

David and I discuss the latest efforts he and his Elemental Cognition team have made to create machines that can understand stories the way humans can and do. The long term vision is to create what David calls "thought partners", which are virtual assistants that can learn and synthesize a massive amount of information for us when we need that information for whatever project we're working on. We also discuss the nature of understanding, language, the role of the biological sciences for AI, and

en-US

01:34:44

BI 068 Rodrigo Quian Quiroga: NeuroScience Fiction

Brain Inspired ·

2020/04/24

Rodrigo and I discuss concept cells and his latest book, NeuroScience Fiction. The book is a whirlwind of many of the big questions in neuroscience, each one framed by of one of Rodrigo’s favorite science fiction films and buttressed by tons of history, literature, and philosophy. We discuss a few of the topics in the book, like AI, identity, free will, consciousness, and immortality, and we keep returning to concept cells and the role of abstraction in human cognition. Notes: Rodrigo's lab

en-US

00:49:00

BI 067 Paul Cisek: Backward Through The Brain

Brain Inspired ·

2020/04/18

In this second part of my conversion with Paul (listen to the first part), we continue our discussion about how to understand brains as feedback control mechanisms - controlling our internal state and extending that control into the world - and how Paul thinks the key to understanding intelligence is to trace our evolutionary past through phylogenetic refinement. Paul's lab website.(A few of) his papers we discuss or mention:Resynthesizing behavior through phylogenetic refinement.Navigating

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01:34:11

BI 066 Paul Cisek: Forward Through Evolution

Brain Inspired ·

2020/04/15

In this first part of our conversation, Paul and I discuss his approach to understanding how the brain (and intelligence) works. Namely, he believes we are fundamentally action and movement oriented - all of our behavior and cognition is based on controlling ourselves and our environment through feedback control mechanisms, and basically all neural activity should be understood through that lens. This contrasts with the view that we serially perceive the environment, make internal representations of

en-US

01:40:13

BI 065 Thomas Serre: How Recurrence Helps Vision

Brain Inspired ·

2020/04/05

Thomas and I discuss the role of recurrence in visual cognition: how brains somehow excel with so few “layers” compared to deep nets, how feedback recurrence can underlie visual reasoning, how LSTM gate-like processing could explain the function of canonical cortical microcircuits, the current limitations of deep learning networks like adversarial examples, and a bit of history in modeling our hierarchical visual system, including his work with the HMAX model and interacting with the deep learning

en-US

01:28:25

BI 064 Galit Shmueli: Explanation vs. Prediction

Brain Inspired ·

2020/03/28

Galit and I discuss the independent roles of prediction and explanation in scientific models, their history and eventual separation in the philosophy of science, how they can inform each other, and how statisticians like Galit view the current deep learning explosion. Galit's website. Follow her on twitter: @gshmueli. The papers we discuss or mention: To Explain or To Predict?Predictive Analytics in Information Systems Research.

en-US

01:32:28

BI 063 Uri Hasson: The Way Evolution Does It

Brain Inspired ·

2020/03/15

Uri and I discuss his recent perspective that conceives of brains as super-over-parameterized models that try to fit everything as exactly as possible rather than trying to abstract the world into usable models. He was inspired by the way artificial neural networks overfit data when they can, and how evolution works the same way on a much slower timescale. Show notes: Uri's lab website. Follow his lab on twitter: @HassonLab.The paper we discuss: Direct Fit to Nature: An EvolutionaryPerspective

en-US

01:57:16

BI 062 Stefan Leijnen: Creativity and Constraint

Brain Inspired ·

2020/03/04

Stefan and I discuss creativity and constraint in artificial and biological intelligence. We talk about his Asimov Institute and its goal of artificial creativity and constraint, different types and functions of creativity, the neuroscience of creativity and its relation to intelligence, how constraint is an essential factor in all creative processes, and how computational accounts of intelligence may need to be discarded to account for our unique creative abilities. Show notes: The Asimov

en-US

01:29:17

BI 061 Jörn Diedrichsen and Niko Kriegeskorte: Brain...

Brain Inspired ·

2020/02/21

Jörn, Niko and I continue the discussion of mental representation from last episode with Michael Rescorla, then we discuss their review paper, Peeling The Onion of Brain Representations, about different ways to extract and understand what information is represented in measured brain activity patterns. Show notes: Jörn's lab website. Niko'slab website. Jörnon twitter: DiedrichsenLab. Niko on twitter:KriegeskorteLab.The papers we discuss or mention: Peeling the Onion of Brain

en-US

01:36:03

BI 060 Michael Rescorla: Mind as Representation Machine

Brain Inspired ·

2020/02/11

Michael and I discuss the philosophy and a bit of history of mental representation including the computational theory of mind and the language of thought hypothesis, how science and philosophy interact, how representation relates to computation in brains and machines, levels of computational explanation, and we discuss some examples of representational approaches to mental processes like bayesian modeling. Show notes: Michael's website (with links to a ton of his publications). Science and

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01:00:06

BI 059 Wolfgang Maass: How Do Brains Compute?

Brain Inspired ·

2020/01/22

In this second part of my discussion with Wolfgang (check out the first part), we talk about spiking neural networks in general, principles of brain computation he finds promising for implementing better network models, and we quickly overview some of his recent work on using these principles to build models with biologically plausible learning mechanisms, a spiking network analog of the well-known LSTM recurrent network, and meta-learning using reservoir computing. Wolfgang'swebsite.Advice To

en-US

00:55:10

BI 058 Wolfgang Maass: Computing Brains and Spiking Nets

Brain Inspired ·

2020/01/15

In this first part of our conversation (here's the second part), Wolfgang and I discuss the state of theoretical and computational neuroscience, and how experimental results in neuroscience should guide theories and models to understand and explain how brains compute. We also discuss brain-machine interfaces, neuromorphics, and more. In the next part (here), we discuss principles of brain processing to inform and constrain theories of computations, and we briefly talk about some of his most recent

en-US

01:21:12

BI 057 Nicole Rust: Visual Memory and Novelty

Brain Inspired ·

2020/01/03

Nicole and I discuss how a signature for visual memory can be coded among the same population of neurons known to encode object identity, how the same coding scheme arises in convolutional neural networks trained to identify objects, and how neuroscience and machine learning (reinforcement learning) can join forces to understand how curiosity and novelty drive efficient learning. Check out Nicole’s Visual Memory Laboratory website. Follow her on twitter:@VisualMemoryLab The papers we discuss or

en-US

01:27:37

BI 056 Tom Griffiths: The Limits of Cognition

Brain Inspired ·

2019/12/22

I speak with Tom Griffiths about his “resource-rational framework”, inspired by Herb Simon's bounded rationality and Stuart Russel’s bounded optimality concepts. The resource-rational framework illuminates how the constraints of optimizing our available cognition can help us understand what algorithms our brains use to get things done, and can serve as a bridge between Marr’s computational, algorithmic, and implementation levels of understanding. We also talk cognitive prostheses, artificial general

en-US

01:26:18

BI 055 Thomas Naselaris: Seeing Versus Imagining

Brain Inspired ·

2019/12/09

Thomas and I talk about what happens in the brain’s visual system when you see something versus imagine it. He uses generative encoding and decoding models and brain signals like fMRI and EEG to test the nature of mental imagery. We also discuss the huge fMRI dataset of natural images he’s collected to infer models of the entire visual system, how we’ve still not tapped the potential of fMRI, and more. Thomas's lab website. Papers we discuss or mention: Resolving Ambiguities of MVPA Using

en-US

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