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Brain Inspired — Natural Sciences
Brain Inspired

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 neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

Copyright: © 2019 Brain-Inspired

Latest Episodes:

BI 111 Kevin Mitchell and Erik Hoel: Agency, Emergence, Consciousness Jul 28, 2021

Show notes

Erik, Kevin, and I discuss... well a lot of things.

Erik's recent novel The Revelations is a story about a group of neuroscientists trying to develop a good theory of consciousness (with a murder mystery plot).

Kevin's book Innate - How the Wiring of Our Brains Shapes Who We Are describes the messy process of getting from DNA, traversing epigenetics and development, to our personalities.

We talk about both books, then dive deeper into topics like whether brains evolved for moving our bodies vs. consciousness, how information theory is lending insights to emergent phenomena, and the role of agency with respect to what counts as intelligence.

Kevin's website.
Eriks' website.
Twitter: @WiringtheBrain (Kevin); @erikphoel (Erik)
Books:
- INNATE – How the Wiring of Our Brains Shapes Who We Are
- The Revelations
Papers
- Erik

Timestamps

0:00 - Intro 3:28 - The Revelations - Erik's novel 15:15 - Innate - Kevin's book 22:56 - Cycle of progress 29:05 - Brains for movement or consciousness? 46:46 - Freud's influence 59:18 - Theories of consciousness 1:02:02 - Meaning and emergence 1:05:50 - Reduction in neuroscience 1:23:03 - Micro and macro - emergence 1:29:35 - Agency and intelligence

BI NMA 03: Stochastic Processes Panel Jul 22, 2021

Show notes

Panelists:

Yael Niv.
- @yael_niv
Konrad Kording
- @KordingLab.
- Previous BI episodes:
  - BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.
  - BI 014 Konrad Kording: Regulators, Mount Up!
Sam Gershman.
- @gershbrain.
- Previous BI episodes:
  - BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?
  - BI 028 Sam Gershman: Free Energy Principle & Human Machines.
Tim Behrens.
- @behrenstim.
- Previous BI episodes:
  - BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.
  - BI 024 Tim Behrens: Cognitive Maps.

This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.

The other panels:

First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
Second panel, about linear systems, real neurons, and dynamic networks.
Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

BI NMA 02: Dynamical Systems Panel Jul 15, 2021

Show notes

Panelists:

This is the second in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with linear systems, real neurons, and dynamic networks.

Other panels:

First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

BI NMA 01: Machine Learning Panel Jul 12, 2021

Show notes

Panelists:

This is the first in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.

Other panels:

Second panel, about linear systems, real neurons, and dynamic networks.
Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

BI 110 Catherine Stinson and Jessica Thompson: Neuro-AI Explanation Jul 06, 2021

Show notes

Catherine, Jess, and I use some of the ideas from their recent papers to discuss how different types of explanations in neuroscience and AI could be unified into explanations of intelligence, natural or artificial. Catherine has written about how models are related to the target system they are built to explain. She suggests both the model and the target system should be considered as instantiations of a specific kind of phenomenon, and explanation is a product of relating the model and the target system to that specific aspect they both share. Jess has suggested we shift our focus of explanation from objects - like a brain area or a deep learning model - to the shared class of phenomenon performed by those objects. Doing so may help bridge the gap between the different forms of explanation currently used in neuroscience and AI. We also discuss Henk de Regt's conception of scientific understanding and its relation to explanation (they're different!), and plenty more.

Catherine's website.
Jessica's blog.
Twitter: Jess: @tsonj.
Related papers
- From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence - Catherine
- Forms of explanation and understanding for neuroscience and artificial intelligence - Jess
Jess is a postdoc in Chris Summerfield's lab, and Chris and San Gershman were on a recent episode.
Understanding Scientific Understanding by Henk de Regt.

Timestamps: 0:00 - Intro 11:11 - Background and approaches 27:00 - Understanding distinct from explanation 36:00 - Explanations as programs (early explanation) 40:42 - Explaining classes of phenomena 52:05 - Constitutive (neuro) vs. etiological (AI) explanations 1:04:04 - Do nonphysical objects count for explanation? 1:10:51 - Advice for early philosopher/scientists

BI 109 Mark Bickhard: Interactivism Jun 26, 2021

Show notes

Mark and I discuss a wide range of topics surrounding his Interactivism framework for explaining cognition. Interactivism stems from Mark's account of representations and how what we represent in our minds is related to the external world - a challenge that has plagued the mind-body problem since the beginning. Basically, representations are anticipated interactions with the world, that can be true (if enacting one helps an organism maintain its thermodynamic relation with the world) or false (if it doesn't). And representations are functional, in that they function to maintain far from equilibrium thermodynamics for the organism for self-maintenance. Over the years, Mark has filled out Interactivism, starting with a process metaphysics foundation and building from there to account for representations, how our brains might implement representations, and why AI is hindered by our modern "encoding" version of representation. We also compare interactivism to other similar frameworks, like enactivism, predictive processing, and the free energy principle.

For related discussions on the foundations (and issues of) representations, check out episode 60 with Michael Rescorla, episode 61 with Jörn Diedrichsen and Niko Kriegeskorte, and especially episode 79 with Romain Brette.

Mark's website.
Related papers
- Interactivism: A manifesto.
- Plenty of other papers available via his website.
Also mentioned:
- The First Half Second The Microgenesis and Temporal Dynamics of Unconscious and Conscious Visual Processes. 2006, Haluk Ögmen, Bruno G. Breitmeyer
- Maiken Nedergaard's work on sleep.

Timestamps 0:00 - Intro 5:06 - Previous and upcoming book 9:17 - Origins of Mark's thinking 14:31 - Process vs. substance metaphysics 27:10 - Kinds of emergence 32:16 - Normative emergence to normative function and representation 36:33 - Representation in Interactivism 46:07 - Situation knowledge 54:02 - Interactivism vs. Enactivism 1:09:37 - Interactivism vs Predictive/Bayesian brain 1:17:39 - Interactivism vs. Free energy principle 1:21:56 - Microgenesis 1:33:11 - Implications for neuroscience 1:38:18 - Learning as variation and selection 1:45:07 - Implications for AI 1:55:06 - Everything is a clock 1:58:14 - Is Mark a philosopher?

BI 108 Grace Lindsay: Models of the Mind Jun 16, 2021

Show notes

Grace's website
Twitter: @neurograce.
Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain.
We talked about Grace's work using convolutional neural networks to study vision and attention way back on episode 11.

Grace and I discuss her new book Models of the Mind, about the blossoming and conceptual foundations of the computational approach to study minds and brains. Each chapter of the book focuses on one major topic and provides historical context, the major concepts that connect models to brain functions, and the current landscape of related research endeavors. We cover a handful of those during the episode, including the birth of AI, the difference between math in physics and neuroscience, determining the neural code and how Shannon information theory plays a role, whether it's possible to guess a brain function based on what we know about some brain structure, "grand unified theories" of the brain. We also digress and explore topics beyond the book.

Timestamps 0:00 - Intro 4:19 - Cognition beyond vision 12:38 - Models of the Mind - book overview 14:00 - The good and bad of using math 21:33 - I quiz Grace on her own book 25:03 - Birth of AI and computational approach 38:00 - Rediscovering old math for new neuroscience 41:00 - Topology as good math to know now 45:29 - Physics vs. neuroscience math 49:32 - Neural code and information theory 55:03 - Rate code vs. timing code 59:18 - Graph theory - can you deduce function from structure? 1:06:56 - Multiple realizability 1:13:01 - Grand Unified theories of the brain

BI 107 Steve Fleming: Know Thyself Jun 06, 2021

Show notes

Steve and I discuss many topics from his new book Know Thyself: The Science of Self-Awareness. The book covers the full range of what we know about metacognition and self-awareness, including how brains might underlie metacognitive behavior, computational models to explain mechanisms of metacognition, how and why self-awareness evolved, which animals beyond humans harbor metacognition and how to test it, its role and potential origins in theory of mind and social interaction, how our metacognitive skills develop over our lifetimes, what our metacognitive skill tells us about our other psychological traits, and so on. We also discuss what it might look like when we are able to build metacognitive AI, and whether that's even a good idea.

Steve's lab: The MetaLab.
Twitter: @smfleming.
Steve and Hakwan Lau on episode 99 about consciousness.
Papers:
- Metacognitive training: Domain-General Enhancements of Metacognitive Ability Through Adaptive Training
The book:
- Know Thyself: The Science of Self-Awareness.

Timestamps 0:00 - Intro 3:25 - Steve's Career 10:43 - Sub-personal vs. personal metacognition 17:55 - Meditation and metacognition 20:51 - Replay tools for mind-wandering 30:56 - Evolutionary cultural origins of self-awareness 45:02 - Animal metacognition 54:25 - Aging and self-awareness 58:32 - Is more always better? 1:00:41 - Political dogmatism and overconfidence 1:08:56 - Reliance on AI 1:15:15 - Building self-aware AI 1:23:20 - Future evolution of metacognition

BI 106 Jacqueline Gottlieb and Robert Wilson: Deep Curiosity May 27, 2021

Show notes

Jackie and Bob discuss their research and thinking about curiosity.

Jackie's background is studying decision making and attention, recording neurons in nonhuman primates during eye movement tasks, and she's broadly interested in how we adapt our ongoing behavior. Curiosity is crucial for this, so she recently has focused on behavioral strategies to exercise curiosity, developing tasks that test exploration, information sampling, uncertainty reduction, and intrinsic motivation.

Bob's background is developing computational models of reinforcement learning (including the exploration-exploitation tradeoff) and decision making, and he behavior and neuroimaging data in humans to test the models. He's broadly interested in how and whether we can understand brains and cognition using mathematical models. Recently he's been working on a model for curiosity known as deep exploration, which suggests we make decisions by deeply simulating a handful of scenarios and choosing based on the simulation outcomes.

We also discuss how one should go about their career (qua curiosity), how eye movements compare with other windows into cognition, and whether we can and should create curious AI agents (Bob is an emphatic yes, and Jackie is slightly worried that will be the time to worry about AI).

Jackie's lab: Jacqueline Gottlieb Laboratory at Columbia University.
Bob's lab: Neuroscience of Reinforcement Learning and Decision Making.
Twitter: Bob: @NRDLab (Jackie's not on twitter).
Related papers
Bob mentions an influential talk by Benjamin Van Roy:
- Generalization and Exploration via Value Function Randomization.
Bob mentions his paper with Anne Collins:
- Ten simple rules for the computational modeling of behavioral data.

Timestamps:

0:00 - Intro 4:15 - Central scientific interests 8:32 - Advent of mathematical models 12:15 - Career exploration vs. exploitation 28:03 - Eye movements and active sensing 35:53 - Status of eye movements in neuroscience 44:16 - Why are we curious? 50:26 - Curiosity vs. Exploration vs. Intrinsic motivation 1:02:35 - Directed vs. random exploration 1:06:16 - Deep exploration 1:12:52 - How to know what to pay attention to 1:19:49 - Does AI need curiosity? 1:26:29 - What trait do you wish you had more of?

BI 105 Sanjeev Arora: Off the Convex Path May 17, 2021

Show notes

Sanjeev and I discuss some of the progress toward understanding how deep learning works, specially under previous assumptions it wouldn't or shouldn't work as well as it does. Deep learning theory poses a challenge for mathematics, because its methods aren't rooted in mathematical theory and therefore are a "black box" for math to open. We discuss how Sanjeev thinks optimization, the common framework for thinking of how deep nets learn, is the wrong approach. Instead, a promising alternative focuses on the learning trajectories that occur as a result of different learning algorithms. We discuss two examples of his research to illustrate this: creating deep nets with infinitely large layers (and the networks still find solutions among the infinite possible solutions!), and massively increasing the learning rate during training (the opposite of accepted wisdom, and yet, again, the network finds solutions!). We also discuss his past focus on computational complexity and how he doesn't share the current neuroscience optimism comparing brains to deep nets.

Sanjeev's website.
His Research group website.
His blog: Off The Convex Path.
Papers we discuss
- On Exact Computation with an Infinitely Wide Neural Net.
- An Exponential Learning Rate Schedule for Deep Learning
Related
- The episode with Andrew Saxe covers related deep learning theory in episode 52.
- Omri Barak discusses the importance of learning trajectories to understand RNNs in episode 97.
- Sanjeev mentions Christos Papadimitriou.

Timestamps 0:00 - Intro 7:32 - Computational complexity 12:25 - Algorithms 13:45 - Deep learning vs. traditional optimization 17:01 - Evolving view of deep learning 18:33 - Reproducibility crisis in AI? 21:12 - Surprising effectiveness of deep learning 27:50 - "Optimization" isn't the right framework 30:08 - Infinitely wide nets 35:41 - Exponential learning rates 42:39 - Data as the next frontier 44:12 - Neuroscience and AI differences 47:13 - Focus on algorithms, architecture, and objective functions 55:50 - Advice for deep learning theorists 58:05 - Decoding minds

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