Brain Inspired

In this second part of our conversation David, John, and I continue to discuss the role of complexity science in the study of intelligence, brains, and minds. We also get into functionalism and multiple realizability, dynamical systems explanations, the role of time in thinking, and more. Be sure to listen to the first part, which lays the foundation for what we discuss in this episode.

Notes:

• David’s page at the Santa Fe Institute.
• John’s BLAM lab website.
• Follow SFI on twitter: @sfiscience.
• BLAM on Twitter: @blamlab
• Related Krakauer stuff:
  • At the limits of thought. An Aeon article by David
  • Complex Time: Cognitive Regime Shift II - When/Why/How the Brain Breaks. A video conversation with both John and David.
  • Complexity Podcast.
• Books mentioned:
  • Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, ed. David Krakauer.
  • Understanding Scientific Understanding by Henk de Regt.
  • The Idea of the Brain by Matthew Cobb.
  • New Dark Age: Technology and the End of the Future by James Bridle.
  • The River of Consciousness by Oliver Sacks.

David, John, and I discuss the role of complexity science in the study of intelligence. In this first part, we talk about complexity itself, its role in neuroscience, emergence and levels of explanation, understanding, epistemology and ontology, and really quite a bit more.

Notes:

• David’s page at the Santa Fe Institute.
• John’s BLAM lab website.
• Follow SFI on twitter: @sfiscience.
• BLAM on Twitter: @blamlab
• Related Krakauer stuff:
  • At the limits of thought. An Aeon article by David
  • Complex Time: Cognitive Regime Shift II - When/Why/How the Brain Breaks. A video conversation with both John and David.
  • Complexity Podcast.
• Books mentioned:
  • Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, ed. David Krakauer.
  • Understanding Scientific Understanding by Henk de Regt.
  • The Idea of the Brain by Matthew Cobb.
  • New Dark Age: Technology and the End of the Future by James Bridle.
  • The River of Consciousness by Oliver Sacks.

Brad and I discuss his battle-tested, age-defying cognitive model for how we learn and store concepts by forming and rearranging clusters, how the model maps onto brain areas, and how he's using deep learning models to explore how attention and sensory information interact with concept formation. We also discuss the cognitive modeling approach, Marr's levels of analysis, the term "biological plausibility", emergence and reduction, and plenty more.

Notes:

• Visit Brad’s website.
• Follow Brad on twitter: @ProfData.
• Related papers:
  • Levels of Biological Plausibility.
  • Models in search of a brain.
  • A non-spatial account of place and grid cells based on clustering models of concept learning.
  • Abstract neural representations of category membership beyond information coding stimulus or response.
  • Ventromedial prefrontal cortex compression during concept learning.
  • The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks
  • Learning as the unsupervised alignment of conceptual systems.

Patrick and I mostly discuss his path from a technician in the then nascent Jim DiCarlo lab, through his graduate school and two postdoc experiences, and finally landing a faculty position, plus the culture and issues in academia in general. We also cover plenty of science, like the role of eye movements in the study of vision, the neuroscience (and concept) of attention, what Patrick thinks of the deep learning hype, and more.

But, this is a special episode, less about the science and more about the experience of an academic neuroscience trajectory/life. Episodes like this will appear in Patreon supporters' private feeds from now on.

Show notes:

• His pre-lab website university page.
• Twitter: @mayo_lab.
• Here’s the paper he recommends to understand attention:
  • Attention can be subdivided into neurobiological components corresponding to distinct behavioral effects.

Mazviita and I discuss the growing divide between prediction and understanding as neuroscience models and deep learning networks become bigger and more complex. She describes her non-factive account of understanding, which among other things suggests that the best predictive models may deliver less understanding. We also discuss the brain as a computer metaphor, and whether it's really possible to ignore all the traditionally "non-computational" parts of the brain like metabolism and other life processes.

Show notes:

• Her website.
• Outside color website (with links to more of her publications)
• Her book Outside Color: Perceptual Science and the Puzzle of Color in Philosophy.
• Papers we discuss or mention:
  • Prediction Versus Understanding in Computationally Enhanced Neuroscience.
  • Your brain is like a computer: function, analogy, simplification.
  • Charting the Heraclitean Brain: Perspectivism and Simplification in Models of the Motor Cortex.