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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

01:06:33

BI 041 Brad Aimone: Neurogenesis and Spiking in Deep...

Brain Inspired ·

2019/07/19

In this first part of our discussion, Brad and I discuss the state of neuromorphics and its relation to neuroscience and artificial intelligence. He describes his work adding new neurons to deep learning networks during training, called neurogenesis deep learning, inspired by how neurogenesis in the dentate gyrus of the hippocampus helps learn new things while keeping previous memories intact. We also talk about his method to transform deep learning networks into spiking neural networks so they can

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01:02:19

BI 040 Nando de Freitas: Enlightenment, Compassion,...

Brain Inspired ·

2019/07/09

Show Notes: Nando’s CIFAR page.Follow Nando on Twitter: @NandoDF He's giving a keynote address at Cognitive Computational Neuroscience Meeting 2020.Check out his famous machine learning lectures on Youtube. Papers we (more allude to than) discuss: Neural Programmer-Interpreters. Learning to learn by gradient descent by gradient descent. Dueling Network Architectures for Deep Reinforcement Learning.Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions. One-Shot

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01:19:09

BI 039 Anne Churchland: Decisions, Lapses, and Fidgets

Brain Inspired ·

2019/06/29

Show notes: Check out Anne's lab website.Follow her on twitter: @anne_churchlandAnne's List, the list of female systems neuroscientists to invite as speakers.The papers we discuss: Single-trial neural dynamics are dominated by richly varied movements.Lapses in perceptual judgments reflect exploration.Complexity vs Stimulus-Response Compatibility vs Stimulus-response ethological validity.Perceptual Decision-Making: A Field in the Midst of a Transformation.

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01:18:37

BI 038 Máté Lengyel: Probabilistic Perception and...

Brain Inspired ·

2019/06/19

Show notes: Máté's Cambridge website.He's part of the Computational Learning and Memory Group there.Here's his webpage at Central European University.A review to introduce his subsequent work:Statistically optimal perception and learning: from behavior to neural representations.Related recent talks:Bayesian models of perception, cognition and learning - CCCN 2017. Sampling: coding, dynamics, and computation in the cortex (Cosyne 2018).

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01:29:52

BI 037 Nathaniel Daw: Thinking the Right Thoughts

Brain Inspired ·

2019/06/09

Show notes: Nathaniel will deliver a keynote address at the upcoming CCN conference.Check out his lab website.Follow him on Twitter: @nathanieldaw.The paper we discuss:Prioritized memory access explains planning and hippocampal replayOr see a related talk: Rational planning using prioritized experience replay.

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01:11:08

BI 036 Roshan Cools: Cognitive Control and Dopamine

Brain Inspired ·

2019/05/30

Show notes: Roshan will deliver a keynote address at the upcoming CCN conference.Roshan's Motivational and Cognitive Control lab.Follow her on Twitter: @CoolsControl.Her TED Talk on Trusting Science.Papers related to the research we discuss:The costs and benefits of brain dopamine for cognitive control.Or see her variety of related works.

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01:11:03

BI 035 Tim Behrens: Abstracting & Generalizing...

Brain Inspired ·

2019/05/20

Show notes: This is the first in a series of episodes where I interview keynote speakers at the upcoming Cognitive Computational Neuroscience conference in September in Berlin. Thomas Naseralis summarizes the origins and vision of the CCN. Tim’s Neuroscience homepage: The papers we discuss: Generalisation of structural knowledge in the hippocampal-entorhinal system (referred to in the podcast at "The Tolman Eichenbaum Machine”) Human replay spontaneously reorganizes experience. (In press at Cell

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01:18:36

BI 034 Tony Zador: How DNA and Evolution Can Inform AI

Brain Inspired ·

2019/05/09

Show notes: Tony’s lab site, where there are links to his auditory decision making work and connectome work we discuss. Here are a few talks online about that: Corticostriatal circuits underlying auditory decisions. Can we upload our mind to the cloud?. Follow Tony on Twitter: @TonyZador The paper we discuss: A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains. Conferences we talk about: COSYNE conference. Neural Information and Coding workshops. Neural

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01:06:05

BI 033 Federico Turkheimer: Weak Versus Strong Emergence

Brain Inspired ·

2019/04/29

Show Notes: Federico's website.Federico’s papers we discuss: Conflicting emergences. Weak vs. strong emergence for the modelling of brain functionFrom homeostasis to behavior: balanced activity in an exploration of embodied dynamic environmental-neural interactionFree Energy Principle. Integrated Information Theory. The Tononi paper about Integrated Information Theory and its relation to emergence: Quantifying causal emergence shows that macro can beat micro Default mode as large scale

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01:15:44

BI 032 Rafal Bogacz: Back-Propagation in Brains

Brain Inspired ·

2019/04/19

Show notes: Visit Rafal’s Lab Website. Rafal's papers we discuss: Theories of Error Back-Propagation in the Brain. An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.A tutorial on the free-energy framework for modelling perception and learning.Check out Episode 9 with Blake Richards about how apical dendrites could do back-prop. The Randall O’Reilly early paper describing biologically plausible back propagation: O'Reilly,

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01:44:47

BI 031 Francisco de Sousa Webber: Natural Language...

Brain Inspired ·

2019/04/12

Cortical.ioThe white paper we discuss:Semantic Folding Theory And its Application in Semantic Fingerprinting. A nice talk Francisco gave:Semantic fingerprinting: Democratising natural language processing Francisco was influenced by Jeff Hawkins’ work and book On Intelligence. See episode 017 to learn more about Jeff Hawkins’ approach to modeling cortex. Douglas Hofstadter’s Analogy as the Core of Cognition.

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01:04:40

BI 030 Jay McClelland: Mathematical Reasoning and PDP

Brain Inspired ·

2019/03/27

Jay's homepage at Stanford.Implementing mathematical reasoning in machines:The video lecture.The paper.Parallel Distributed Processing by Rumelhart and McClelland.Complimentary Learning Systems Theory and Its Recent Update.Episode 28 with Sam Gershman about building machines that learn and think like humans.Check out my interview on Ginger Campbell's Brain Science podcast.

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01:44:23

BI 029 Paul Humphreys & Zac Irving: Emergence & Mind...

Brain Inspired ·

2019/03/06

Show notes: Paul Humphreys' website.Zac Irving's website.Emergence: Emergence: A Philosophical Account. (book by Paul)The Oxford Handbook of Philosophy of Science. Mind Wandering: Mind-Wandering is Unguided Attention.The Philosophy of Mind-Wandering.The Neuroscience of Spontaneous Thought.

en-US

01:14:09

BI 028 Sam Gershman: Free Energy Principle & Human...

Brain Inspired ·

2019/02/19

Show notes: Sam's Computational Cognitive Neuroscience Lab.Follow Sam on Twitter: @gershbrain.The papers we discuss: What does the free energy principle tell us about the brain?Building machines that learn and think like people. A video summarizing that work. The book Sam recommended: What Is Thought by Eric Baum.

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01:20:04

BI 027 Ioana Marinescu & Konrad Kording: Causality in...

Brain Inspired ·

2019/02/12

Show notes: Websites:Ioana Marinescu, Konrad KordingTwitter: Twitter: @mioana; @KordingLabThe paper we discuss: Quasi-experimental causality in neuroscience and behavioral research.A Pre-printversion. Judea Pearl's The Book of Why.Judea Pearl's online lecture about causality: The Art and Science of Cause and Effect.Ioana's review of Universal Basic Income: No Strings Attached.The post on hedge drift by Stefan Schubert: Hedge drift and advanced motte-and-bailey.Books recommended by Ioana and

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01:22:30

BI 026 Kendrick Kay: A Model By Any Other Name

Brain Inspired ·

2019/01/31

Image courtesy of Kendrick Kay:Brain art Show notes: Check out Kendrick’s lab website: CVN lab. Follow him on twitter: @cvnlab. The papers we discuss: Bottom-up and top-down computations in word- and face-selective cortex. Principles for models of neural information processing. Appreciating diversity of goals in computational neuroscience. A nice talk about the model we discuss:Bottom Up and Top Down Computations in Word- and Face-Selective Cortex Cognitive Computational

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01:46:16

BI 025 John Krakauer: Understanding Cognition

Brain Inspired ·

2019/01/25

Show notes BLAM (Brain, Learning, Animation, and Movement) Lab homepage:http://blam-lab.org/ BLAM on Twitter: @blamlab Papers we discuss: Neuroscience Needs Behavior: Correcting a Reductionist Bias. John and his brother David’s interview in Current Biology: John and David Krakauer Mark Humphries’ piece on John’s “Neuroscience Needs Behavior” paper. Tinbergen’s 4 Questions. John’s book on stroke recovery:Broken Movement: The Neurobiology of Motor Recovery after Stroke (The MIT

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01:09:27

BI 024 Tim Behrens: Cognitive Maps

Brain Inspired ·

2019/01/15

Show notes: Tim’s Neuroscience homepage: Follow Tim on Twitter: @behrenstim. Edward Tolman’s cognitive maps work: Cognitive maps in rats and men. Place Cells and Grid Cells: O’Keefe early place cell paper: The Hippocampus as a Spatial Map. O’Keefe’s book about the work: The Hippocampus as a Cognitive Map. Mosers and their students discover grid cells:Microstructure of a spatial map in the entorhinal cortex. Overview. Here’s a good talk Tim gives on the subject: Building models

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01:04:05

BI 023 Marcel van Gerven: Mind Decoding with GANs

Brain Inspired ·

2018/12/28

Show notes: Donders Institute for Brain, Cognition and Behaviour Artificial Cognitive Systems on Twitter. Artificial Cognitive Systems research group. The paper we discuss: Generative adversarial networks for reconstructing natural images from brain activity. Read their Mindcodec blog. Nice overview of GANs (appropriately titled):Generative Adversarial Networks: An Overview.

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00:59:22

BI 022 Melanie Mitchell: Complexity, and AI Shortcomings

Brain Inspired ·

2018/12/18

Show notes: Follow Melanie on Twitter: @MelMitchell1. Learn more about what she does at her homepage. Here is her New York Times Op-Ed about AI. And here is a talk which goes into more depth: AI and the Barrier of Meaning. Her book, Complexity: A Guided Tour, has won awards. Check out all the free coures and tutorials at Complexity Explorer. Here’s a talk she gave: Introduction to Complexity. Melanie’s PhD advisor was Douglas Hofstadter, author of Godel, Escher, Bach: The Eternal

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241 results

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