Brain Inspired

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Johannes (Yogi) is a freelance philosopher, researcher & educator. We discuss many of the topics in his online course, Beyond Networks: The Evolution of Living Systems. The course is focused on the role of agency in evolution, but it covers a vast range of topics: process vs. substance metaphysics, causality, mechanistic dynamic explanation, teleology, the important role of development mediating genotypes, phenotypes, and evolution, what makes biological organisms unique, the history of evolutionary theory, scientific perspectivism, and a view toward the necessity of including agency in evolutionary theory. I highly recommend taking his course. We also discuss the role of agency in artificial intelligence, how neuroscience and evolutionary theory are undergoing parallel re-evaluations, and Yogi answers a guest question from Kevin Mitchell.

• Yogi's website and blog: Untethered in the Platonic Realm.
• Twitter: @yoginho.
• His youtube course: Beyond Networks: The Evolution of Living Systems.
• Kevin Mitchell's previous episode: BI 111 Kevin Mitchell and Erik Hoel: Agency, Emergence, Consciousness.

0:00 - Intro

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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

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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

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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

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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

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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:
  • BI 025 John Krakauer: Understanding Cognition
  • BI 077 David and John Krakauer: Part 1
  • BI 078 David and John Krakauer: Part 2

Timestamps

0:00 - Intro

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

Check out his lab here and apply!

Dopamine: A Simple AND Complex Story 

by Daphne Cornelisse

Timestamps:

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).

The other panels: