Brain Inspired

David and I discuss the latest efforts he and his Elemental Cognition team have made to create machines that can understand stories the way humans can and do. The long term vision is to create what David calls "thought partners", which are virtual assistants that can learn and synthesize a massive amount of information for us when we need that information for whatever project we're working on. We also discuss the nature of understanding, language, the role of the biological sciences for AI, and more.

• Dave’s business Elemental Cognition.
• The paper we discuss:
  • To Test Machine Comprehension, Start by Defining Comprehension.

Rodrigo and I discuss concept cells and his latest book, NeuroScience Fiction. The book is a whirlwind of many of the big questions in neuroscience, each one framed by of one of Rodrigo’s favorite science fiction films and buttressed by tons of history, literature, and philosophy. We discuss a few of the topics in the book, like AI, identity, free will, consciousness, and immortality, and we keep returning to concept cells and the role of abstraction in human cognition.

Notes:

• Rodrigo's lab website: Centre for Systems Neuroscience at the University of Leicester, UK
• His book:
  • NeuroScience Fiction: From "2001: A Space Odyssey" to "Inception," How Neuroscience Is Transforming Sci-Fi into Reality―While Challenging Our Beliefs About the Mind, Machines, and What Makes us Human.
• Papers we discuss or mention:
  • Concept cells: the building blocks of declarative memory functions.
  • Neural representations across species.
  • Searching for the neural correlates of human intelligence.
• Talks:
  • Concept cells and their role in memory - Part 1 and Part 2

In this second part of my conversion with Paul (listen to the first part), we continue our discussion about how to understand brains as feedback control mechanisms - controlling our internal state and extending that control into the world - and how Paul thinks the key to understanding intelligence is to trace our evolutionary past through phylogenetic refinement.

• Paul's lab website.
• (A few of) his papers we discuss or mention:
  • Resynthesizing behavior through phylogenetic refinement.
  • Navigating the affordance landscape: Feedback control as a process model of behavior and cognition.
  • Neural Mechanisms for Interacting with a World Full of Action Choices.
• Books Paul recommends about these topics:
  • The Ecological Approach to Visual Perception by Gibson.
  • Brains Through Time: A Natural History of Vertebrates by Striedter and Northcutt.
  • The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, And The Origin Of Insight by Passingham and Wise.
  • The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations by Murray, Wise, and Graham.
  • The ancient origins of consciousness:How the brain created experience by Feinberg and Mallatt.
  • Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought by Hendriks-Jansen.
• In case, like me, you didn’t know what an amphioxus is… here you go.

In this first part of our conversation, Paul and I discuss his approach to understanding how the brain (and intelligence) works. Namely, he believes we are fundamentally action and movement oriented - all of our behavior and cognition is based on controlling ourselves and our environment through feedback control mechanisms, and basically all neural activity should be understood through that lens. This contrasts with the view that we serially perceive the environment, make internal representations of what we perceive, do some cognition on those representations, and transform that cognition into decisions about how to move. From that premise, Paul also believes the best (and perhaps only) way to understand our current brains is by tracing out the evolutionary steps that took us from our single celled first organisms all the way to us - a process he calls phylogenetic refinement.

• Paul's lab website.
• (A few of) his papers we discuss or mention:
  • Resynthesizing behavior through phylogenetic refinement.
  • Navigating the affordance landscape: Feedback control as a process model of behavior and cognition.
  • Neural Mechanisms for Interacting with a World Full of Action Choices.
• Books Paul recommends about these topics:
  • The Ecological Approach to Visual Perception by Gibson.
  • Brains Through Time: A Natural History of Vertebrates by Striedter and Northcutt.
  • The Neurobiology of the Prefrontal Cortex: Anatomy, Evolution, And The Origin Of Insight by Passingham and Wise.
  • The Evolution of Memory Systems: Ancestors, Anatomy, and Adaptations by Murray, Wise, and Graham.
  • The ancient origins of consciousness:How the brain created experience by Feinberg and Mallatt.
  • Catching Ourselves in the Act: Situated Activity, Interactive Emergence, Evolution, and Human Thought by Hendriks-Jansen.
• In case, like me, you didn’t know what an amphioxus is… here you go.

Thomas and I discuss the role of recurrence in visual cognition: how brains somehow excel with so few “layers” compared to deep nets, how feedback recurrence can underlie visual reasoning, how LSTM gate-like processing could explain the function of canonical cortical microcircuits, the current limitations of deep learning networks like adversarial examples, and a bit of history in modeling our hierarchical visual system, including his work with the HMAX model and interacting with the deep learning folks as convolutional neural networks were being developed.

Show Notes:

• Visit the Serre Lab website.
• Follow Thomas on twitter: @tserre.
• Good reviews that references all the work we discussed, including the HMAX model:
  • Beyond the feedforward sweep: feedback computations in the visual cortex.
  • Deep learning: the good, the bad and the ugly.
• Papers about the topics we discuss:
  • Complementary Surrounds Explain Diverse Contextual Phenomena Across Visual Modalities.
  • Recurrent neural circuits for contour detection.
  • Learning long-range spatial dependencies with horizontal gated-recurrent units.

Galit and I discuss the independent roles of prediction and explanation in scientific models, their history and eventual separation in the philosophy of science, how they can inform each other, and how statisticians like Galit view the current deep learning explosion.

• Galit's website.
• Follow her on twitter: @gshmueli.
• The papers we discuss or mention:
  • To Explain or To Predict?
  • Predictive Analytics in Information Systems Research.

Uri and I discuss his recent perspective that conceives of brains as super-over-parameterized models that try to fit everything as exactly as possible rather than trying to abstract the world into usable models. He was inspired by the way artificial neural networks overfit data when they can, and how evolution works the same way on a much slower timescale.

Show notes:

• Uri's lab website.
• Follow his lab on twitter: @HassonLab.
• The paper we discuss:
  • Direct Fit to Nature: An EvolutionaryPerspective on Biological and Artificial Neural Networks.
  • Here’s the BioRxiv version in case the above doesn’t work. 
  • Uri mentioned his newest paper: Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.

Stefan and I discuss creativity and constraint in artificial and biological intelligence. We talk about his Asimov Institute and its goal of artificial creativity and constraint, different types and functions of creativity, the neuroscience of creativity and its relation to intelligence, how constraint is an essential factor in all creative processes, and how computational accounts of intelligence may need to be discarded to account for our unique creative abilities. 

Show notes:

• The Asimov Institute.
  • Get that Zoo of Networks poster we talk about! See preview below.
• His site at Utrecht University of Applied Sciences.
• Stefan’s personal website.
• Follow the Asimov Institute on twitter: @asimovinstitute .
• Stuff mentioned:
  • Creativity and Constraint in Artificial Systems (Leijnen 2014 Dissertation).
  • Incomplete Nature - Terrance Deacon’s long, challenging read with fascinating original ideas.
  • Neither Ghost Nor Machine - Jeremy Sherman’s succinct, readable summary of some arguments in Incomplete Nature.

Jörn, Niko and I continue the discussion of mental representation from last episode with Michael Rescorla, then we discuss their review paper, Peeling The Onion of Brain Representations, about different ways to extract and understand what information is represented in measured brain activity patterns.

Show notes:

• Jörn's lab website.
• Niko's lab website.
• Jörn on twitter: DiedrichsenLab.
• Niko on twitter: KriegeskorteLab.
• The papers we discuss or mention:
  • Peeling the Onion of Brain Representations. Annual Review of Neuroscience, 2019
  • Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS, 2017.

Michael and I discuss the philosophy and a bit of history of mental representation including the computational theory of mind and the language of thought hypothesis, how science and philosophy interact, how representation relates to computation in brains and machines, levels of computational explanation, and we discuss some examples of representational approaches to mental processes like bayesian modeling.

Show notes:

• Michael's website (with links to a ton of his publications).
• Science and Philosophy
  • Why science needs philosophy by Laplane et al 2019.
  • Why Cognitive Science Needs Philosophy and Vice Versa by Paul Thagard, 2009.
• Some of Michael's papers/articles we discuss or mention:
  • The Computational Theory of Mind.
  • Levels of Computational Explanation.
  • Computational Modeling of the Mind: What Role for Mental Representation?
  • From Ockham to Turing --- and Back Again.
• Talks:
  • Predictive coding “debate” with Michael and a few other folks.
  • An overview and history of the philosophy of representation.
• Books we mentioned:
  • The Structure of Scientific Revolutions by Thomas Kuhn.
  • Memory and the Computational Brain by Randy Gallistel and Adam King.
  • Representation In Cognitive Science by Nicholas Shea.
  • Types and Tokens: On Abstract Objects by Linda Wetzel.
  • Probabilistic Robotics by Thrun, Burgard, and Fox.