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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

01:29:17

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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01:37:12

BI 046 David Poeppel: From Sounds to Meanings

Brain Inspired ·

2019/09/08

Support the Podcast David and I talk about his work to understand how sound waves floating in the air get transformed into meaningful concepts in your mind. He studies speech processing and production, language, music, and everything in between, approaching his work with steadfast principles to help frame what it means to understand something scientifically. We discuss many of the hurdles to understanding how our brains work and making real progress in science, plus a ton more. Show

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01:16:52

BI 045 Raia Hadsell: Robotics and Deep RL

Brain Inspired ·

2019/08/28

Support the Podcast Show notes: Raia and I discuss her work at DeepMind figuring out how to build robots using deep reinforcement learning to do things like navigate cities and generalize intelligent behaviors across different tasks. We also talk about challenges specific for embodied AI (robots), how much of it takes inspiration from neuroscience, and lots more. Raia’s website. Follow her on Twitter:@RaiaHadsell Papers relevant to our discussion: Learning to Navigate in Cities without

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01:15:33

BI 044 Talia Konkle: Turning Vision On Its Side

Brain Inspired ·

2019/08/18

Talia and I discuss her work on how our visual system is organized topographically, and divides into three main categories: big inanimate things, small inanimate things, and animals. Her work is unique in that it focuses not on the classic hierarchical processing of vision (though she does that, too), but what kinds of things are represented along that hierarchy. She also uses deep networks to learn more about the visual system. We also talk about her keynote talk at the Cognitive Computational

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01:30:30

BI 043 Anna Schapiro: Learning in Hippocampus and Cortex

Brain Inspired ·

2019/08/07

How does knowledge in the world get into our brains and integrated with the rest of our knowledge and memories? Anna and I talk about the complementary learning systems theory introduced in 1995 that posits a fast episodic hippopcampal learning system and a slower statistical cortical learning system. We then discuss her work that advances and adds missing pieces to the CLS framework, and explores how sleep and sleep cycles contribute to the process. We also discuss how her work might contribute to

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00:59:35

BI 042 Brad Aimone: Brains at the Funeral of Moore’s Law

Brain Inspired ·

2019/07/28

This is part 2 of my conversation with Brad (listen to part 1 here). We discuss how Moore’s law is on its last legs, and his ideas for how neuroscience - in particular neural algorithms - may help computing continue to scale in a post-Moore’s law world. We also discuss neuromporphics in general, and more. Brad's homepage.Follow Brad on Twitter: @jbimaknee.The paper we discuss:Neural Algorithms and Computing Beyond Moore's Law.Check out the Neuro Inspired Computing Elements (NICE) workshop - lots

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