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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 117 Anil Seth: Being You Oct 19, 2021

Show notes

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Anil and I discuss a range of topics from his book, BEING YOU A New Science of Consciousness. Anil lays out his framework for explaining consciousness, which is embedded in what he calls the "real problem" of consciousness. You know the "hard problem", which was David Chalmers term for our eternal difficulties to explain why we have subjective awareness at all instead of being unfeeling, unexperiencing machine-like organisms. Anil's "real problem" aims to explain, predict, and control the phenomenal properties of consciousness, and his hope is that, by doing so, the hard problem of consciousness will dissolve much like the mystery of explaining life dissolved with lots of good science.

Anil's account of perceptual consciousness, like seeing red, is that it's rooted in predicting our incoming sensory data. His account of our sense of self, is that it's rooted in predicting our bodily states to control them.

We talk about that and a lot of other topics from the book, like consciousness as "controlled hallucinations", free will, psychedelics, complexity and emergence, and the relation between life, intelligence, and consciousness. Plus, Anil answers a handful of questions from Megan Peters and Steve Fleming, both previous brain inspired guests.

Anil's website.
Twitter: @anilkseth.
Anil's book: BEING YOU A New Science of Consciousness.
Megan's previous episode:
- BI 073 Megan Peters: Consciousness and Metacognition
Steve's previous episodes
- BI 099 Hakwan Lau and Steve Fleming: Neuro-AI Consciousness
- BI 107 Steve Fleming: Know Thyself

0:00 - Intro 6:32 - Megan Peters Q: Communicating Consciousness 15:58 - Human vs. animal consciousness 19:12 - BEING YOU A New Science of Consciousness 20:55 - Megan Peters Q: Will the hard problem go away? 30:55 - Steve Fleming Q: Contents of consciousness 41:01 - Megan Peters Q: Phenomenal character vs. content 43:46 - Megan Peters Q: Lempels of complexity 52:00 - Complex systems and emergence 55:53 - Psychedelics 1:06:04 - Free will 1:19:10 - Consciousness vs. life vs. intelligence

BI 116 Michael W. Cole: Empirical Neural Networks Oct 12, 2021

Show notes

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Mike and I discuss his modeling approach to study cognition. Many people I have on the podcast use deep neural networks to study brains, where the idea is to train or optimize the model to perform a task, then compare the model properties with brain properties. Mike's approach is different in at least two ways. One, he builds the architecture of his models using connectivity data from fMRI recordings. Two, he doesn't train his models; instead, he uses functional connectivity data from the fMRI recordings to assign weights between nodes of the network (in deep learning, the weights are learned through lots of training). Mike calls his networks empirically-estimated neural networks (ENNs), and/or network coding models. We walk through his approach, what we can learn from models like ENNs, discuss some of his earlier work on cognitive control and our ability to flexibly adapt to new task rules through instruction, and he fields questions from Kanaka Rajan, Kendrick Kay, and Patryk Laurent.

The Cole Neurocognition lab.
Twitter: @TheColeLab.
Related papers
- Discovering the Computational Relevance of Brain Network Organization.
- Constructing neural network models from brain data reveals representational transformation underlying adaptive behavior.
Kendrick Kay's previous episode: BI 026 Kendrick Kay: A Model By Any Other Name.
Kanaka Rajan's previous episode: BI 054 Kanaka Rajan: How Do We Switch Behaviors?

0:00 - Intro 4:58 - Cognitive control 7:44 - Rapid Instructed Task Learning and Flexible Hub Theory 15:53 - Patryk Laurent question: free will 26:21 - Kendrick Kay question: fMRI limitations 31:55 - Empirically-estimated neural networks (ENNs) 40:51 - ENNs vs. deep learning 45:30 - Clinical relevance of ENNs 47:32 - Kanaka Rajan question: a proposed collaboration 56:38 - Advantage of modeling multiple regions 1:05:30 - How ENNs work 1:12:48 - How ENNs might benefit artificial intelligence 1:19:04 - The need for causality 1:24:38 - Importance of luck and serendipity

BI 115 Steve Grossberg: Conscious Mind, Resonant Brain Oct 02, 2021

Show notes

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Steve and I discuss his book Conscious Mind, Resonant Brain: How Each Brain Makes a Mind. The book is a huge collection of his models and their predictions and explanations for a wide array of cognitive brain functions. Many of the models spring from his Adaptive Resonance Theory (ART) framework, which explains how networks of neurons deal with changing environments while maintaining self-organization and retaining learned knowledge. ART led Steve to the hypothesis that all conscious states are resonant states, which we discuss. There are also guest questions from György Buzsáki, Jay McClelland, and John Krakauer.

Steve's BU website.
Conscious Mind, Resonant Brain: How Each Brain Makes a Mind
Previous Brain Inspired episode:
- BI 082 Steve Grossberg: Adaptive Resonance Theory

0:00 - Intro 2:38 - Conscious Mind, Resonant Brain 11:49 - Theoretical method 15:54 - ART, learning, and consciousness 22:58 - Conscious vs. unconscious resonance 26:56 - Györy Buzsáki question 30:04 - Remaining mysteries in visual system 35:16 - John Krakauer question 39:12 - Jay McClelland question 51:34 - Any missing principles to explain human cognition? 1:00:16 - Importance of an early good career start 1:06:50 - Has modeling training caught up to experiment training? 1:17:12 - Universal development code

BI 114 Mark Sprevak and Mazviita Chirimuuta: Computation and the Mind Sep 22, 2021

Show notes

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Mark and Mazviita discuss the philosophy and science of mind, and how to think about computations with respect to understanding minds. Current approaches to explaining brain function are dominated by computational models and the computer metaphor for brain and mind. But there are alternative ways to think about the relation between computations and brain function, which we explore in the discussion. We also talk about the role of philosophy broadly and with respect to mind sciences, pluralism and perspectival approaches to truth and understanding, the prospects and desirability of naturalizing representations (accounting for how brain representations relate to the natural world), and much more.

Mark's website.
Mazviita's University of Edinburgh page.
Twitter (Mark): @msprevak.
Mazviita's previous Brain Inspired episode:
- BI 072 Mazviita Chirimuuta: Understanding, Prediction, and Reality
The related book we discuss:
- The Routledge Handbook of the Computational Mind 2018 Mark Sprevak Matteo Colombo (Editors)

0:00 - Intro 5:26 - Philosophy contributing to mind science 15:45 - Trend toward hyperspecialization 21:38 - Practice-focused philosophy of science 30:42 - Computationalism 33:05 - Philosophy of mind: identity theory, functionalism 38:18 - Computations as descriptions 41:27 - Pluralism and perspectivalism 54:18 - How much of brain function is computation? 1:02:11 - AI as computationalism 1:13:28 - Naturalizing representations 1:30:08 - Are you doing it right?

BI 113 David Barack and John Krakauer: Two Views On Cognition Sep 12, 2021

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David and John discuss some of the concepts from their recent paper Two Views on the Cognitive Brain, in which they argue the recent population-based dynamical systems approach is a promising route to understanding brain activity underpinning higher cognition. We discuss mental representations, the kinds of dynamical objects being used for explanation, and much more, including David's perspectives as a practicing neuroscientist and philosopher.

David's webpage.
John's Lab.
Twitter:
- David: @DLBarack
- John: @blamlab
Paper: Two Views on the Cognitive Brain.
John's previous episodes:

Timestamps

0:00 - Intro 3:13 - David's philosophy and neuroscience experience 20:01 - Renaissance person 24:36 - John's medical training 31:58 - Two Views on the Cognitive Brain 44:18 - Representation 49:37 - Studying populations of neurons 1:05:17 - What counts as representation 1:18:49 - Does this approach matter for AI?

BI ViDA Panel Discussion: Deep RL and Dopamine Sep 02, 2021

Show notes

BI 112 Ali Mohebi and Ben Engelhard: The Many Faces of Dopamine Aug 26, 2021

Show notes

BI 112:

Ali Mohebi and Ben Engelhard

The Many Faces of Dopamine

Announcement:

Ben has started his new lab and is recruiting grad students.

Check out his lab here and apply!

Engelhard Lab

Ali and Ben discuss the ever-expanding discoveries about the roles dopamine plays for our cognition. Dopamine is known to play a role in learning – dopamine (DA) neurons fire when our reward expectations aren’t met, and that signal helps adjust our expectation. Roughly, DA corresponds to a reward prediction error. The reward prediction error has helped reinforcement learning in AI develop into a raging success, specially with deep reinforcement learning models trained to out-perform humans in games like chess and Go. But DA likely contributes a lot more to brain function. We discuss many of those possible roles, how to think about computation with respect to neuromodulators like DA, how different time and spatial scales interact, and more.

Dopamine: A Simple AND Complex Story

by Daphne Cornelisse

Guests

Timestamps:

0:00 – Intro 5:02 – Virtual Dopamine Conference 9:56 – History of dopamine’s roles 16:47 – Dopamine circuits 21:13 – Multiple roles for dopamine 31:43 – Deep learning panel discussion 50:14 – Computation and neuromodulation

BI NMA 06: Advancing Neuro Deep Learning Panel Aug 19, 2021

Show notes

BI NMA 05: NLP and Generative Models Panel Aug 13, 2021

Show notes

BI NMA 05:

NLP and Generative Models Panel

This is the 5th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the 2nd of 3 in the deep learning series. In this episode, the panelists discuss their experiences “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).

Panelists

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.
Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
Fourth panel, about some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

BI NMA 04: Deep Learning Basics Panel Aug 06, 2021

Show notes

BI NMA 04:

Deep Learning Basics Panel

This is the 4th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the first of 3 in the deep learning series. In this episode, the panelists discuss their experiences with some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.

Guests

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.
Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
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.

Timestamps:

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