Brain Inspired

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Gaute Einevoll is a professor at the University of Oslo and Norwegian University of Life Sciences. Use develops detailed models of brain networks to use as simulations, so neuroscientists can test their various theories and hypotheses about how networks implement various functions. Thus, the models are tools. The goal is to create models that are multi-level, to test questions at various levels of biological detail; and multi-modal, to predict that handful of signals neuroscientists measure from real brains (something Gaute calls "measurement physics"). We also discuss Gaute's thoughts on Carina Curto's "beautiful vs ugly models", and his reaction to Noah Hutton's In Silico documentary about the Blue Brain and Human Brain projects (Gaute has been funded by the Human Brain Project since its inception).

• Gaute's website.
• Twitter: @GauteEinevoll.
• Related papers:
  • The Scientific Case for Brain Simulations.
  • Brain signal predictions from multi-scale networks using a linearized framework.
  • Uncovering circuit mechanisms of current sinks and sources with biophysical simulations of primary visual cortex
• LFPy: a Python module for calculation of extracellular potentials from multicompartment neuron models.
• Gaute's Sense and Science podcast.

0:00 - Intro

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Noah Hutton writes, directs, and scores documentary and narrative films. On this episode, we discuss his documentary In Silico. In 2009, Noah watched a TED talk by Henry Markram, in which Henry claimed it would take 10 years to fully simulate a human brain. This claim inspired Noah to chronicle the project, visiting Henry and his team periodically throughout. The result was In Silico, which tells the science, human, and social story of Henry's massively funded projects - the Blue Brain Project and the Human Brain Project.

• In Silico website.
  • Rent or buy In Silico.
• Noah's website.
• Twitter: @noah_hutton.

0:00 - Intro

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Lauren Ross is an Associate Professor at the University of California, Irvine. She studies and writes about causal and non-causal explanations in philosophy of science, including distinctions among causal structures. Throughout her work, Lauren employs Jame's Woodward's interventionist approach to causation, which Jim and I discussed in episode 145. In this episode, we discuss Jim's lasting impact on the philosophy of causation, the current dominance of mechanistic explanation and its relation to causation, and various causal structures of explanation, including pathways, cascades, topology, and constraints.

• Lauren's website.
• Twitter: @ProfLaurenRoss
• Related papers
  • A call for more clarity around causality in neuroscience.
  • The explanatory nature of constraints: Law-based, mathematical, and causal.
  • Causal Concepts in Biology: How Pathways Differ from Mechanisms and Why It Matters.
  • Distinguishing topological and causal explanation.
  • Multiple Realizability from a Causal Perspective.
  • Cascade versus mechanism: The diversity of causal structure in science.

0:00 - Intro

Check out my free video series about what's missing in AI and Neuroscience

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James Woodward is a recently retired Professor from the Department of History and Philosophy of Science at the University of Pittsburgh. Jim has tremendously influenced the field of causal explanation in the philosophy of science. His account of causation centers around intervention - intervening on a cause should alter its effect. From this minimal notion, Jim has described many facets and varieties of causal structures. In this episode, we discuss topics from his recent book, Causation with a Human Face: Normative Theory and Descriptive Psychology. In the book, Jim advocates that how we should think about causality - the normative - needs to be studied together with how we actually do think about causal relations in the world - the descriptive. We discuss many topics around this central notion, epistemology versus metaphysics, the the nature and varieties of causal structures.

• Jim's website.
• Making Things Happen: A Theory of Causal Explanation.
• Causation with a Human Face: Normative Theory and Descriptive Psychology.

0:00 - Intro

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Large language models, often now called "foundation models", are the model de jour in AI, based on the transformer architecture. In this episode, I bring together Evelina Fedorenko and Emily M. Bender to discuss how language models stack up to our own language processing and generation (models and brains both excel at next-word prediction), whether language evolved in humans for complex thoughts or for communication (communication, says Ev), whether language models grasp the meaning of the text they produce (Emily says no), and much more.

Evelina Fedorenko is a cognitive scientist who runs the EvLab at MIT. She studies the neural basis of language. Her lab has amassed a large amount of data suggesting language did not evolve to help us think complex thoughts, as Noam Chomsky has argued, but rather for efficient communication. She has also recently been comparing the activity in language models to activity in our brain's language network, finding commonality in the ability to predict upcoming words.

Emily M. Bender is a computational linguist at University of Washington. Recently she has been considering questions about whether language models understand the meaning of the language they produce (no), whether we should be scaling language models as is the current practice (not really), how linguistics can inform language models, and more.

• EvLab.
• Emily's website.
• Twitter: @ev_fedorenko; @emilymbender.
• Related papers
  • Language and thought are not the same thing: Evidence from neuroimaging and neurological patients. (Fedorenko)
  • The neural architecture of language: Integrative modeling converges on predictive processing. (Fedorenko)
  • On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? (Bender)
  • Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data (Bender)

0:00 - Intro

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Rodolphe Sepulchre is a control engineer and theorist at Cambridge University. He focuses on applying feedback control engineering principles to build circuits that model neurons and neuronal circuits. We discuss his work on mixed feedback control - positive and negative - as an underlying principle of the mixed digital and analog brain signals,, the role of neuromodulation as a controller, applying these principles to Eve Marder's lobster/crab neural circuits, building mixed-feedback neuromorphics, some feedback control history, and how "If you wish to contribute original work, be prepared to face loneliness," among other topics.

• Rodolphe's website.
• Related papers
  • Spiking Control Systems.
  • Control Across Scales by Positive and Negative Feedback.
  • Neuromorphic control. (arXiv version)
• Related episodes:
  • BI 130 Eve Marder: Modulation of Networks
  • BI 119 Henry Yin: The Crisis in Neuroscience

0:00 - Intro

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Cameron Buckner is a philosopher and cognitive scientist at The University of Houston. He is writing a book about the age-old philosophical debate on how much of our knowledge is innate (nature, rationalism) versus how much is learned (nurture, empiricism). In the book and his other works, Cameron argues that modern AI can help settle the debate. In particular, he suggests we focus on what types of psychological "domain-general faculties" underlie our own intelligence, and how different kinds of deep learning models are revealing how those faculties may be implemented in our brains. The hope is that by building systems that possess the right handful of faculties, and putting those systems together in a way they can cooperate in a general and flexible manner, it will result in cognitive architectures we would call intelligent. Thus, what Cameron calls The New DoGMA: Domain-General Modular Architecture. We also discuss his work on mental representation and how representations get their content - how our thoughts connect to the natural external world. 

• Cameron's Website.
• Twitter: @cameronjbuckner.
• Related papers
  • Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.
  • A Forward-Looking Theory of Content.
• Other sources Cameron mentions:
  • Innateness, AlphaZero, and Artificial Intelligence (Gary Marcus).
  • Radical Empiricism and Machine Learning Research (Judea Pearl).
  • Fodor’s guide to the Humean mind (Tamás Demeter).

0:00 - Intro

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Carina Curto is a professor in the Department of Mathematics at The Pennsylvania State University. She uses her background skills in mathematical physics/string theory to study networks of neurons. On this episode, we discuss the world of topology in neuroscience - the study of the geometrical structures mapped out by active populations of neurons. We also discuss her work on "combinatorial linear threshold networks" (CLTNs). Unlike the large deep learning models popular today as models of brain activity, the CLTNs Carina builds are relatively simple, abstracted graphical models. This property is important to Carina, whose goal is to develop mathematically tractable neural network models. Carina has worked out how the structure of many CLTNs allows prediction of the model's allowable dynamics, how motifs of model structure can be embedded in larger models while retaining their dynamical features, and more. The hope is that these elegant models can tell us more about the principles our messy brains employ to generate the robust and beautiful dynamics underlying our cognition.

• Carina's website.
• The Mathematical Neuroscience Lab.
• Related papers
  • A major obstacle impeding progress in brain science is the lack of beautiful models.
  • What can topology tells us about the neural code?
  • Predicting neural network dynamics via graphical analysis

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Jeff Schall is the director of the Center for Visual Neurophysiology at York University, where he runs the Schall Lab. His research centers around studying the mechanisms of our decisions, choices, movement control, and attention within the saccadic eye movement brain systems and in mathematical psychology models- in other words, how we decide where and when to look. Jeff was my postdoctoral advisor at Vanderbilt University, and I wanted to revisit a few guiding principles he instills in all his students. Linking Propositions by Davida Teller are a series of logical statements to ensure we rigorously connect the brain activity we record to the psychological functions we want to explain. Strong Inference by John Platt is the scientific method on steroids - a way to make our scientific practice most productive and efficient. We discuss both of these topics in the context of Jeff's eye movement and decision-making science. We also discuss how neurophysiology has changed over the past 30 years, we compare the relatively small models he employs with the huge deep learning models, many of his current projects, and plenty more. If you want to learn more about Jeff's work and approach, I recommend reading in order two of his review papers we discuss as well. One was written 20 years ago (On Building a Bridge Between Brain and Behavior), and the other 2-ish years ago (Accumulators, Neurons, and Response Time).

• Schall Lab.
• Twitter: @LabSchall.
• Related papers
  • Linking Propositions.
  • Strong Inference.
  • On Building a Bridge Between Brain and Behavior.
  • Accumulators, Neurons, and Response Time.

0:00 - Intro

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Marc Howard runs his Theoretical Cognitive Neuroscience Lab at Boston University, where he develops mathematical models of cognition, constrained by psychological and neural data. In this episode, we discuss the idea that a Laplace transform and its inverse may serve as a unified framework for memory. In short, our memories are compressed on a continuous log-scale: as memories get older, their representations "spread out" in time. It turns out this kind of representation seems ubiquitous in the brain and across cognitive functions, suggesting it is likely a canonical computation our brains use to represent a wide variety of cognitive functions. We also discuss some of the ways Marc is incorporating this mathematical operation in deep learning nets to improve their ability to handle information at different time scales.

0:00 - Intro