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.

In this second part of my discussion with Wolfgang (check out the first part), we talk about spiking neural networks in general, principles of brain computation he finds promising for implementing better network models, and we quickly overview some of his recent work on using these principles to build models with biologically plausible learning mechanisms, a spiking network analog of the well-known LSTM recurrent network, and meta-learning using reservoir computing.

• Wolfgang's website.
• Advice To a Young Investigator (has the quote at the beginning of the episode) by Santiago Ramon y Cajal.
• Papers we discuss or mention:
  • Searching for principles of brain computation.
  • Brain Computation: A Computer Science Perspective.
  • Long short-term memory and learning-to-learn in networks of spiking neurons.
  • A solution to the learning dilemma for recurrent networks of spiking neurons.
  • Reservoirs learn to learn.
• Talks that cover some of these topics:
  • Computation in Networks of Neurons in the Brain I.
  • Computation in Networks of Neurons in the Brain II.

In this first part of our conversation (here's the second part), Wolfgang and I discuss the state of theoretical and computational neuroscience, and how experimental results in neuroscience should guide theories and models to understand and explain how brains compute. We also discuss brain-machine interfaces, neuromorphics, and more. In the next part (here), we discuss principles of brain processing to inform and constrain theories of computations, and we briefly talk about some of his most recent work making spiking neural networks that incorporate some of these brain processing principles.

• Wolfgang's website.
• The book Wolfgang recommends:
  • The Brain from Inside Out by György Buzsáki.
• Papers we discuss or mention:
  • Searching for principles of brain computation.
  • Brain Computation: A Computer Science Perspective.
  • Long short-term memory and learning-to-learn in networks of spiking neurons.
  • A solution to the learning dilemma for recurrent networks of spiking neurons.
  • Reservoirs learn to learn.
• Talks that cover some of these topics:
  • Computation in Networks of Neurons in the Brain I.
  • Computation in Networks of Neurons in the Brain II.

Nicole and I discuss how a signature for visual memory can be coded among the same population of neurons known to encode object identity, how the same coding scheme arises in convolutional neural networks trained to identify objects, and how neuroscience and machine learning (reinforcement learning) can join forces to understand how curiosity and novelty drive efficient learning.

• Check out Nicole’s Visual Memory Laboratory website.
• Follow her on twitter: @VisualMemoryLab
• The papers we discuss or mention:
  • Single-exposure visual memory judgments are reflected in inferotemporal cortex.
  • Population response magnitude variation in inferotemporal cortex predicts image memorability.
  • Visual novelty, curiosity, and intrinsic reward in machine learning and the brain.
• The work by Dan Yamins’s group that Nicole mentions:
  • Local Aggregation for Unsupervised Learning of Visual Embeddings

I speak with Tom Griffiths about his “resource-rational framework”, inspired by Herb Simon's bounded rationality and Stuart Russel’s bounded optimality concepts. The resource-rational framework illuminates how the constraints of optimizing our available cognition can help us understand what algorithms our brains use to get things done, and can serve as a bridge between Marr’s computational, algorithmic, and implementation levels of understanding. We also talk cognitive prostheses, artificial general intelligence, consciousness, and more.

• Visit Tom's Computational Cognitive Science Lab.
• Check out his book with Brian Christian, Algorithms To Live By.
• Some of the papers we discuss or mention:
  • Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic.
  • Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources.
• Data on the mind - the data repository we discussed briefly
• A paper that discusses it: Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets.

Thomas and I talk about what happens in the brain’s visual system when you see something versus imagine it. He uses generative encoding and decoding models and brain signals like fMRI and EEG to test the nature of mental imagery. We also discuss the huge fMRI dataset of natural images he’s collected to infer models of the entire visual system, how we’ve still not tapped the potential of fMRI, and more.

• Thomas's lab website. 
• Papers we discuss or mention:
  • Resolving Ambiguities of MVPA Using Explicit Models of Representation.
  • Human brain activity during mental imagery exhibits signatures of inference in a hierarchical generative model.