Support the show to get full episodes and join the Discord community. 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 MetacognitionSteve's previous episodesBI 099 Hakwan Lau and Steve Fleming: Neuro-AI ConsciousnessBI 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

Brain Inspired

BI 117 Anil Seth: Being You

Support the show to get full episodes and join the Discord community. 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 papersDiscovering 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 116 Michael W. Cole: Empirical Neural Networks

Support the show to get full episodes and join the Discord community 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 MindPrevious 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 115 Steve Grossberg: Conscious Mind, Resonant Brain

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 RealityThe 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 114 Mark Sprevak and Mazviita Chirimuuta: Computation and the Mind

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: @DLBarackJohn: @blamlabPaper: Two Views on the Cognitive Brain.John's previous episodes:BI 025 John Krakauer: Understanding CognitionBI 077 David and John Krakauer: Part 1BI 078 David and John Krakauer: Part 2 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 113 David Barack and John Krakauer: Two Views On Cognition

BI ViDA Panel Discussion: Deep RL and Dopamine

BI 112 Ali Mohebi and Ben Engelhard: The Many Faces of Dopamine

This is the 6th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the 3rd of 3 in the deep learning series. In this episode, the panelists discuss their experiences with advanced topics in deep learning; unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

BI NMA 06: Advancing Neuro Deep Learning 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).

BI NMA 05: NLP and Generative Models 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.

BI NMA 04: Deep Learning Basics Panel

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

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

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.

BI NMA 03: Stochastic Processes Panel

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.

BI NMA 02: Dynamical Systems Panel

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.

BI NMA 01: Machine Learning Panel

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.

BI 110 Catherine Stinson and Jessica Thompson: Neuro-AI Explanation

Neuroscience and artificial intelligence work better together. 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 show 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.

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Brain Inspired

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