Brain Inspired

This is the 4th in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. This is the first of 3 in the deep learning series. In this episode, the panelists discuss their experiences with some basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.

Erik, Kevin, and I discuss... well a lot of things.

Erik's recent novel The Revelations is a story about a group of neuroscientists trying to develop a good theory of consciousness (with a murder mystery plot).

Kevin's book Innate - How the Wiring of Our Brains Shapes Who We Are describes the messy process of getting from DNA, traversing epigenetics and development, to our personalities.

We talk about both books, then dive deeper into topics like whether brains evolved for moving our bodies vs. consciousness, how information theory is lending insights to emergent phenomena, and the role of agency with respect to what counts as intelligence.

• Kevin's website.
• Eriks' website.
• Twitter: @WiringtheBrain (Kevin); @erikphoel (Erik)
• Books:
  • INNATE – How the Wiring of Our Brains Shapes Who We Are
  • The Revelations
• Papers
  • Erik
    • Falsification and consciousness.
    • The emergence of informative higher scales in complex networks.
    • Emergence as the conversion of information: A unifying theory.

Timestamps

0:00 - Intro

Panelists:

• Yael Niv.
  • @yael_niv
• Konrad Kording
  • @KordingLab.
  • Previous BI episodes:
    • BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments.
    • BI 014 Konrad Kording: Regulators, Mount Up!
• Sam Gershman.
  • @gershbrain.
  • Previous BI episodes:
    • BI 095 Chris Summerfield and Sam Gershman: Neuro for AI?
    • BI 028 Sam Gershman: Free Energy Principle & Human Machines.
• Tim Behrens.
  • @behrenstim.
  • Previous BI episodes:
    • BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay.
    • BI 024 Tim Behrens: Cognitive Maps.

This is the third in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.

The other panels:

• First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
• Second panel, about linear systems, real neurons, and dynamic networks.
• Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
• Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
• Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Panelists:

• Adrienne Fairhall.
  • @alfairhall.
• Bing Brunton.
  • @bingbrunton.
• Kanaka Rajan.
  • @rajankdr.
  • BI 054 Kanaka Rajan: How Do We Switch Behaviors?

This is the second in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with linear systems, real neurons, and dynamic networks.

Other panels:

• First panel, about model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.
• Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
• Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
• Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
• Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Panelists:

• Athena Akrami: @AthenaAkrami.
• Demba Ba.
• Gunnar Blohm: @GunnarBlohm.
• Kunlin Wei.

This is the first in a series of panel discussions in collaboration with Neuromatch Academy, the online computational neuroscience summer school. In this episode, the panelists discuss their experiences with model fitting, GLMs/machine learning, dimensionality reduction, and deep learning.

Other panels:

• Second panel, about linear systems, real neurons, and dynamic networks.
• Third panel, about stochastic processes, including Bayes, decision-making, optimal control, reinforcement learning, and causality.
• Fourth panel, about basics in deep learning, including Linear deep learning, Pytorch, multi-layer-perceptrons, optimization, & regularization.
• Fifth panel, about “doing more with fewer parameters: Convnets, RNNs, attention & transformers, generative models (VAEs & GANs).
• Sixth panel, about advanced topics in deep learning: unsupervised & self-supervised learning, reinforcement learning, continual learning/causality.

Catherine, Jess, and I use some of the ideas from their recent papers to discuss how different types of explanations in neuroscience and AI could be unified into explanations of intelligence, natural or artificial. Catherine has written about how models are related to the target system they are built to explain. She suggests both the model and the target system should be considered as instantiations of a specific kind of phenomenon, and explanation is a product of relating the model and the target system to that specific aspect they both share. Jess has suggested we shift our focus of explanation from objects - like a brain area or a deep learning model - to the shared class of phenomenon performed by those objects. Doing so may help bridge the gap between the different forms of explanation currently used in neuroscience and AI. We also discuss Henk de Regt's conception of scientific understanding and its relation to explanation (they're different!), and plenty more.

• Catherine's website.
• Jessica's blog.
• Twitter: Jess: @tsonj.
• Related papers
  • From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence - Catherine
  • Forms of explanation and understanding for neuroscience and artificial intelligence - Jess
• Jess is a postdoc in Chris Summerfield's lab, and Chris and San Gershman were on a recent episode.
• Understanding Scientific Understanding by Henk de Regt.

Timestamps:

Mark and I discuss a wide range of topics surrounding his Interactivism framework for explaining cognition. Interactivism stems from Mark's account of representations and how what we represent in our minds is related to the external world - a challenge that has plagued the mind-body problem since the beginning. Basically, representations are anticipated interactions with the world, that can be true (if enacting one helps an organism maintain its thermodynamic relation with the world) or false (if it doesn't). And representations are functional, in that they function to maintain far from equilibrium thermodynamics for the organism for self-maintenance. Over the years, Mark has filled out Interactivism, starting with a process metaphysics foundation and building from there to account for representations, how our brains might implement representations, and why AI is hindered by our modern "encoding" version of representation. We also compare interactivism to other similar frameworks, like enactivism, predictive processing, and the free energy principle.

• Mark's website.
• Related papers
  • Interactivism: A manifesto.
  • Plenty of other papers available via his website.
• Also mentioned:
  • The First Half Second The Microgenesis and Temporal Dynamics of Unconscious and Conscious Visual Processes. 2006, Haluk Ögmen, Bruno G. Breitmeyer
  • Maiken Nedergaard's work on sleep.

Timestamps

• Grace's website
• Twitter: @neurograce.
• Models of the Mind: How Physics, Engineering and Mathematics Have Shaped Our Understanding of the Brain.
• We talked about Grace's work using convolutional neural networks to study vision and attention way back on episode 11.

Grace and I discuss her new book Models of the Mind, about the blossoming and conceptual foundations of the computational approach to study minds and brains. Each chapter of the book focuses on one major topic and provides historical context, the major concepts that connect models to brain functions, and the current landscape of related research endeavors. We cover a handful of those during the episode, including the birth of AI, the difference between math in physics and neuroscience, determining the neural code and how Shannon information theory plays a role, whether it's possible to guess a brain function based on what we know about some brain structure, "grand unified theories" of the brain. We also digress and explore topics beyond the book. 

Timestamps

Steve and I discuss many topics from his new book Know Thyself: The Science of Self-Awareness. The book covers the full range of what we know about metacognition and self-awareness, including how brains might underlie metacognitive behavior, computational models to explain mechanisms of metacognition, how and why self-awareness evolved, which animals beyond humans harbor metacognition and how to test it, its role and potential origins in theory of mind and social interaction, how our metacognitive skills develop over our lifetimes, what our metacognitive skill tells us about our other psychological traits, and so on. We also discuss what it might look like when we are able to build metacognitive AI, and whether that's even a good idea.

• Steve's lab: The MetaLab.
• Twitter: @smfleming.
• Steve and Hakwan Lau on episode 99 about consciousness. 
• Papers:
  • Metacognitive training: Domain-General Enhancements of Metacognitive Ability Through Adaptive Training
• The book:
  • Know Thyself: The Science of Self-Awareness.

Timestamps

Jackie and Bob discuss their research and thinking about curiosity.

Jackie's background is studying decision making and attention, recording neurons in nonhuman primates during eye movement tasks, and she's broadly interested in how we adapt our ongoing behavior. Curiosity is crucial for this, so she recently has focused on behavioral strategies to exercise curiosity, developing tasks that test exploration, information sampling, uncertainty reduction, and intrinsic motivation.

Bob's background is developing computational models of reinforcement learning (including the exploration-exploitation tradeoff) and decision making, and he behavior and neuroimaging data in humans to test the models. He's broadly interested in how and whether we can understand brains and cognition using mathematical models. Recently he's been working on a model for curiosity known as deep exploration, which suggests we make decisions by deeply simulating a handful of scenarios and choosing based on the simulation outcomes.

We also discuss how one should go about their career (qua curiosity), how eye movements compare with other windows into cognition, and whether we can and should create curious AI agents (Bob is an emphatic yes, and Jackie is slightly worried that will be the time to worry about AI).

• Jackie's lab: Jacqueline Gottlieb Laboratory at Columbia University.
• Bob's lab: Neuroscience of Reinforcement Learning and Decision Making.
• Twitter: Bob: @NRDLab (Jackie's not on twitter).
• Related papers
  • Curiosity, information demand and attentional priority.
  • Balancing exploration and exploitation with information and randomization.
  • Deep exploration as a unifying account of explore-exploit behavior.
• Bob mentions an influential talk by Benjamin Van Roy:
  • Generalization and Exploration via Value Function Randomization.
• Bob mentions his paper with Anne Collins:
  • Ten simple rules for the computational modeling of behavioral data.

Timestamps:

0:00 - Intro