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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

01:34:11

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

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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

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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.

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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

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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

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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

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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

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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

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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

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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

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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

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01:15:24

BI 054 Kanaka Rajan: How Do We Switch Behaviors?

Brain Inspired ·

2019/11/27

Support the Podcast Kanaka and I discuss a few different ways she uses recurrent neural networks to understand how brains give rise to behaviors. We talk about her work showing how neural circuits transition from active to passive coping behavior in zebrafish, and how RNNs could be used to understand how we switch tasks in general and how we multi-task. Plus the usual fun speculation, advice, and more. Kanaka’s google scholar profile. Follow her on twitter: @rajankdr. Papers we discuss:

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01:33:24

BI 053 Jon Brennan: Linguistics in Minds and Machines

Brain Inspired ·

2019/11/17

Support the Podcast Jon and I discuss understanding the syntax and semantics of language in our brains. He uses linguistic knowledge at the level of sentence and words, neuro-computational models, and neural data like EEG and fMRI to figure out how we process and understand language while listening to the natural language found in everyday conversations and stories. I also get his take on the current state of natural language processing and other AI advances, and how linguistics,

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01:25:48

BI 052 Andrew Saxe: Deep Learning Theory

Brain Inspired ·

2019/11/06

Support the Podcast Andrew and I discuss his work exploring how various facets of deep networks contribute to their function, i.e. deep network theory. We talk about what he’s learned by studying linear deep networks and asking how depth and initial weights affect learning dynamics, when replay is appropriate (and when it’s not), how semantics develop, and what it all might tell us about deep learning in brains. Show notes: Visit Andrew's website. The papers we discuss or mention: Are

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01:28:21

BI 051 Jess Hamrick: Mental Simulation and Construction

Brain Inspired ·

2019/10/27

Support the Podcast Jess and I discuss construction using graph neural networks. She makes AI agents that build structures to solve tasks in a simulated blocks and glue world using graph neural networks and deep reinforcement learning. We also discuss her work modeling mental simulation in humans and how it could be implemented in machines, and plenty more. Show notes: Jess’s website. Follow her on twitter: @jhamrick The papers we discuss or mention: Analogues of mental simulation and

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01:36:02

BI 050 Kyle Dunovan: Academia to Industry

Brain Inspired ·

2019/10/17

Kyle and I talk about his work modeling the basal ganglia and its circuitry to control whether we take an action and how we select among alternative actions. We also reflect on his experiences in academia, the larger picture of what it’s like in graduate school and after - at least in a computational neuroscience program - why he left, what he’s doing now, and how it all fits together. Show notes: Kyle’s website. Follow him on twitter: @dunovank Examples of his work on basal ganglia and

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01:24:45

BI 049 Phillip Alvelda: Trustworthy Brain Machines

Brain Inspired ·

2019/10/06

Support the Podcast Phillip and I discuss his company Brainworks, which uses the latest neuroscience to build AI into its products. We talk about their first product, Ambient Biometrics, that measures vital signs using your smartphone's camera. We also dive into entrepreneurship in the AI startup world, ethical issues in AI and social media companies, his early days using neural networks at NASA, where he thinks this is all headed, and more. Show notes: His company, Brainworks.Follow

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01:24:37

BI 048 Liz Spelke: What Makes Us Special?

Brain Inspired ·

2019/09/25

Support the Podcast Liz and I discuss her work on cognitive development, specially in infants, and what it can tell us about what makes human cognition different from other animals, what core cognitive abilities we’re born with, and how those abilities may form the foundation for much of our other cognitive abilities to develop. We also talk about natural language as the potential key faculty that synthesizes our early core abilities into the many higher cognitive functions that make us

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00:48:12

BI 047 David Poeppel: Wrong in Interesting Ways

Brain Inspired ·

2019/09/15

In this second part of our conversation, (listen to the first part) David and I discuss his thoughts about current language and speech techniques in AI, his thoughts about the prospects of artificial general intelligence, the challenge of mapping the parts of linguistics onto the parts of neuroscience, the state of graduate training, and more. Visit David's lab websiteat NYU. He’s also a director atMax Planck Institute for Empirical Aesthetics. Follow him on twitter: @davidpoeppel. Some of the

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