Pieter and I discuss his ongoing quest to figure out how the brain implements learning that solves the credit assignment problem, like backpropagation does for neural networks. We also talk about his work to understand how we perceive individual objects in a crowded scene, his neurophysiological recordings in support of the global neuronal workspace hypothesis of consciousness, and the visual prosthetic device he’s developing to cure blindness by directly stimulating early visual cortex. 

Related:

• Pieter's lab website.
• Twitter: @Pieters_Tweet.
• His startup to cure blindness: Phosphoenix.
• Talk:
  • Seeing and thinking with your visual brain
• The papers we discuss or mention:
  • Control of synaptic plasticity in deep cortical networks.
  • A Biologically Plausible Learning Rule for Deep Learning in the Brain.
  • Conscious Processing and the Global Neuronal Workspace Hypothesis.
• Pieter's neuro-origin book inspiration (like so many others): Gödel, Escher, Bach: An Eternal Golden Braid by Douglas Hofstadter.

Daeyeol and I discuss his book Birth of Intelligence: From RNA to Artificial Intelligence, which argues intelligence is a function of and inseparable from life, bound by self-replication and evolution. The book covers a ton of neuroscience related to decision making and learning, though we focused on a few theoretical frameworks and ideas like division of labor and principal-agent relationships to understand how our brains and minds are related to our genes, how AI is related to humans (for now), metacognition, consciousness, and a ton more.

Related:

• Lee Lab for Learning and Decision Making.
• Twitter: @daeyeol_lee.
• Daeyeol’s side passion, creating music.
• His book: Birth of Intelligence: From RNA to Artificial Intelligence.

Romain and I discuss his theoretical/philosophical work examining how neuroscientists rampantly misuse the word "code" when making claims about information processing in brains. We talk about the coding metaphor, various notions of information, the different roles and facets of mental representation, perceptual invariance, subjective physics, process versus substance metaphysics, and the experience of writing a Behavior and Brain Sciences article (spoiler: it's a demanding yet rewarding experience).

• Romain's website.
• Twitter: @RomainBrette.
• The papers we discuss or mention:.
  • Philosophy of the spike: rate-based vs. spike-based theories of the brain.
  • Is coding a relevant metaphor for the brain? (bioRxiv link).
  • Subjective physics.
• Related works
  • The Ecological Approach to Visual Perception by James Gibson.
  • Why Red Doesn't Sound Like a Bell by Kevin O’Reagan.

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.

Olaf and I discuss the explosion of network neuroscience, which uses network science tools to map the structure (connectome) and activity of the brain at various spatial and temporal scales. We talk about the possibility of bridging physical and functional connectivity via communication dynamics, and about the relation between network science and artificial neural networks and plenty more.

Notes:

• Computational Cognitive Neuroscience Laboratory.
• Twitter: @spornslab
• His excellent book: Networks of the Brain.
• Related papers:
  • Network Neuroscience.
  • The economy of brain network organization.
  • Communication dynamics in complex brain networks.

Jim and I discuss his reverse engineering approach to visual intelligence, using deep models optimized to perform object recognition tasks. We talk about the history of his work developing models to match the neural activity in the ventral visual stream, how deep learning connects with those models, and some of his recent work: adding recurrence to the models to account for more difficult object recognition, using unsupervised learning to account for plasticity in the visual stream, and controlling neural activity  by creating specific images for subjects to view.

Notes:

• The DiCarlo Lab at MIT.
• Related papers:
  • Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.
  • Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition.
  • Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex.
  • Neural population control via deep image synthesis.

Ginger and I discuss her book Are You Sure? The Unconscious Origins of Certainty, which summarizes Richard Burton's work exploring the experience and phenomenal origin of feeling confident, and how the vast majority of our brain processing occurs outside our conscious awareness.

• Are You Sure? The Unconscious Origins of Certainty.
• Brain Science Podcast.

Megan and I discuss her work using metacognition as a way to study subjective awareness, or confidence. We talk about using computational and neural network models to probe how decisions are related to our confidence, the current state of the science of consciousness, and her newest project using fMRI decoded neurofeedback to induce particular brain states in subjects so we can learn about conscious and unconscious brain processing.

Notes:

• Visit Megan's cognitive & neural computation lab.
• Twitter: @meganakpeters
• The papers we discuss or mention:
  • Human intracranial electrophysiology suggests suboptimal calculations underlie perceptual confidence
  • Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior.

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.