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Àlex Gómez-Marín heads The Behavior of Organisms Laboratory at the Institute of Neuroscience in Alicante, Spain. He's one of those theoretical physicist turned neuroscientist, and he has studied a wide range of topics over his career. Most recently, he has become interested in what he calls the "edges of consciousness", which encompasses the many trying to explain what may be happening when we have experiences outside our normal everyday experiences. For example, when we are under the influence of hallucinogens, when have near-death experiences (as Alex has), paranormal experiences, and so on.

So we discuss what led up to his interests in these edges of consciousness, how he now thinks about consciousness and doing science in general, how important it is to make room for all possible explanations of phenomena, and to leave our metaphysics open all the while.

• Alex's website: The Behavior of Organisms Laboratory.
• Twitter: @behaviOrganisms.
• Previous episodes:
  • BI 168 Frauke Sandig and Eric Black w Alex Gomez-Marin: AWARE: Glimpses of Consciousness.
  • BI 136 Michel Bitbol and Alex Gomez-Marin: Phenomenology.
• Related:
  • The Consciousness of Neuroscience.
  • Seeing the consciousness forest for the trees.
  • The stairway to transhumanist heaven.

0:00 - Intro 4:13 - Evolving viewpoints 10:05 - Near-death experience 18:30 - Mechanistic neuroscience vs. the rest 22:46 - Are you doing science? 33:46 - Where is my. mind? 44:55 - Productive vs. permissive brain 59:30 - Panpsychism 1:07:58 - Materialism 1:10:38 - How to choose what to do 1:16:54 - Fruit flies 1:19:52 - AI and the Singularity

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Damian Kelty-Stephen is an experimental psychologist at State University of New York at New Paltz. Last episode with Luis Favela, we discussed many of the ideas from ecological psychology, and how Louie is trying to reconcile those principles with those of neuroscience. In this episode, Damian and I in some ways continue that discussion, because Damian is also interested in unifying principles of ecological psychology and neuroscience. However, he is approaching it from a different perspective that Louie. What drew me originally to Damian was a paper he put together with a bunch of authors offering their own alternatives to the computer metaphor of the brain, which has come to dominate neuroscience. And we discuss that some, and I'll link to the paper in the show notes. But mostly we discuss Damian's work studying the fractal structure of our behaviors, connecting that structure across scales, and linking it to how our brains and bodies interact to produce our behaviors. Along the way, we talk about his interests in cascades dynamics and turbulence to also explain our intelligence and behaviors. So, I hope you enjoy this alternative slice into thinking about how we think and move in our bodies and in the world.

• Damian's website.
• Related papers
  • In search for an alternative to the computer metaphor of the mind and brain.
  • Multifractal emergent processes: Multiplicative interactions override nonlinear component properties.

0:00 - Intro 2:34 - Damian's background 9:02 - Brains 12:56 - Do neuroscientists have it all wrong? 16:56 - Fractals everywhere 28:01 - Fractality, causality, and cascades 32:01 - Cascade instability as a metaphor for the brain 40:43 - Damian's worldview 46:09 - What is AI missing? 54:26 - Turbulence 1:01:02 - Intelligence without fractals? Multifractality 1:10:28 - Ergodicity 1:19:16 - Fractality, intelligence, life 1:23:24 - What's exciting, changing viewpoints

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John Krakauer has been on the podcast multiple times (see links below). Today we discuss some topics framed around what he's been working on and thinking about lately. Things like

• Whether brains actually reorganize after damage
• The role of brain plasticity in general
• The path toward and the path not toward understanding higher cognition
• How to fix motor problems after strokes
• AGI
• Functionalism, consciousness, and much more.

Relevant links:

• John's Lab.
• Twitter: @blamlab
• Related papers
  • What are we talking about? Clarifying the fuzzy concept of representation in neuroscience and beyond.
  • Against cortical reorganisation.
• Other episodes with John:
  • BI 025 John Krakauer: Understanding Cognition
  • BI 077 David and John Krakauer: Part 1
  • BI 078 David and John Krakauer: Part 2
  • BI 113 David Barack and John Krakauer: Two Views On Cognition

Time stamps 0:00 - Intro 2:07 - It's a podcast episode! 6:47 - Stroke and Sherrington neuroscience 19:26 - Thinking vs. moving, representations 34:15 - What's special about humans? 56:35 - Does cortical reorganization happen? 1:14:08 - Current era in neuroscience

In the 4th installment of our 100th episode celebration, previous guests responded to the question:

What ideas, assumptions, or terms do you think is holding back neuroscience/AI, and why?

As usual, the responses are varied and wonderful!

Timestamps:

0:00 - Intro 6:41 - Pieter Roelfsema 7:52 - Grace Lindsay 10:23 - Marcel van Gerven 11:38 - Andrew Saxe 14:05 - Jane Wang 16:50 - Thomas Naselaris 18:14 - Steve Potter 19:18 - Kendrick Kay 22:17 - Blake Richards 27:52 - Jay McClelland 30:13 - Jim DiCarlo 31:17 - Talia Konkle 33:27 - Uri Hasson 35:37 - Wolfgang Maass 38:48 - Paul Cisek 40:41 - Patrick Mayo 41:51 - Konrad Kording 43:22 - David Poeppel 44:22 - Brad Love 46:47 - Rodrigo Quian Quiroga 47:36 - Steve Grossberg 48:47 - Mark Humphries 52:35 - John Krakauer 55:13 - György Buzsáki 59:50 - Stefan Leijnan 1:02:18 - Nathaniel Daw

Part 3 in our 100th episode celebration. Previous guests answered the question:

Given the continual surprising progress in AI powered by scaling up parameters and using more compute, while using fairly generic architectures (eg. GPT-3):

Do you think the current trend of scaling compute can lead to human level AGI? If not, what's missing?

It likely won't surprise you that the vast majority answer "No." It also likely won't surprise you, there is differing opinion on what's missing.

Timestamps:

0:00 - Intro 3:56 - Wolgang Maass 5:34 - Paul Humphreys 9:16 - Chris Eliasmith 12:52 - Andrew Saxe 16:25 - Mazviita Chirimuuta 18:11 - Steve Potter 19:21 - Blake Richards 22:33 - Paul Cisek 26:24 - Brad Love 29:12 - Jay McClelland 34:20 - Megan Peters 37:00 - Dean Buonomano 39:48 - Talia Konkle 40:36 - Steve Grossberg 42:40 - Nathaniel Daw 44:02 - Marcel van Gerven 45:28 - Kanaka Rajan 48:25 - John Krakauer 51:05 - Rodrigo Quian Quiroga 53:03 - Grace Lindsay 55:13 - Konrad Kording 57:30 - Jeff Hawkins 102:12 - Uri Hasson 1:04:08 - Jess Hamrick 1:06:20 - Thomas Naselaris

In this 2nd special 100th episode installment, many previous guests answer the question: What is currently the most important disagreement or challenge in neuroscience and/or AI, and what do you think the right answer or direction is? The variety of answers is itself revealing, and highlights how many interesting problems there are to work on.

Timestamps:

0:00 - Intro 7:10 - Rodrigo Quian Quiroga 8:33 - Mazviita Chirimuuta 9:15 - Chris Eliasmith 12:50 - Jim DiCarlo 13:23 - Paul Cisek 16:42 - Nathaniel Daw 17:58 - Jessica Hamrick 19:07 - Russ Poldrack 20:47 - Pieter Roelfsema 22:21 - Konrad Kording 25:16 - Matt Smith 27:55 - Rafal Bogacz 29:17 - John Krakauer 30:47 - Marcel van Gerven 31:49 - György Buzsáki 35:38 - Thomas Naselaris 36:55 - Steve Grossberg 48:32 - David Poeppel 49:24 - Patrick Mayo 50:31 - Stefan Leijnen 54:24 - David Krakuer 58:13 - Wolfang Maass 59:13 - Uri Hasson 59:50 - Steve Potter 1:01:50 - Talia Konkle 1:04:30 - Matt Botvinick 1:06:36 - Brad Love 1:09:46 - Jon Brennan 1:19:31 - Grace Lindsay 1:22:28 - Andrew Saxe

Brain Inspired turns 100 (episodes) today! To celebrate, my patreon supporters helped me create a list of questions to ask my previous guests, many of whom contributed by answering any or all of the questions. I've collected all their responses into separate little episodes, one for each question. Starting with a light-hearted (but quite valuable) one, this episode has responses to the question, "In the last five years, what new belief, behavior, or habit has most improved your career or well being?" See below for links to each previous guest. And away we go...

Timestamps:

0:00 - Intro 6:13 - David Krakauer 8:50 - David Poeppel 9:32 - Jay McClelland 11:03 - Patrick Mayo 11:45 - Marcel van Gerven 12:11 - Blake Richards 12:25 - John Krakauer 14:22 - Nicole Rust 15:26 - Megan Peters 17:03 - Andrew Saxe 18:11 - Federico Turkheimer 20:03 - Rodrigo Quian Quiroga 22:03 - Thomas Naselaris 23:09 - Steve Potter 24:37 - Brad Love 27:18 - Steve Grossberg 29:04 - Talia Konkle 29:58 - Paul Cisek 32:28 - Kanaka Rajan 34:33 - Grace Lindsay 35:40 - Konrad Kording 36:30 - Mark Humphries

Hakwan, Steve, and I discuss many issues around the scientific study of consciousness. Steve and Hakwan focus on higher order theories (HOTs) of consciousness, related to metacognition. So we discuss HOTs in particular and their relation to other approaches/theories, the idea of approaching consciousness as a computational problem to be tackled with computational modeling, we talk about the cultural, social, and career aspects of choosing to study something as elusive and controversial as consciousness, we talk about two of the models they're working on now to account for various properties of conscious experience, and, of course, the prospects of consciousness in AI. For more on metacognition and awareness, check out episode 73 with Megan Peters.

• Hakwan's lab: Consciousness and Metacognition Lab.
• Steve's lab: The MetaLab.
• Twitter: @hakwanlau; @smfleming.
• Hakwan's brief Aeon article: Is consciousness a battle between your beliefs and perceptions?
• Related papers
  • An Informal Internet Survey on the Current State of Consciousness Science.
  • Opportunities and challenges for a maturing science of consciousness.
  • What is consciousness, and could machines have it?"
  • Understanding the higher-order approach to consciousness.
  • Awareness as inference in a higher-order state space. (Steve's bayesian predictive generative model)
  • Consciousness, Metacognition, & Perceptual Reality Monitoring. (Hakwan's reality-monitoring model a la generative adversarial networks)

Timestamps 0:00 - Intro 7:25 - Steve's upcoming book 8:40 - Challenges to study consciousness 15:50 - Gurus and backscratchers 23:58 - Will the problem of consciousness disappear? 27:52 - Will an explanation feel intuitive? 29:54 - What do you want to be true? 38:35 - Lucid dreaming 40:55 - Higher order theories 50:13 - Reality monitoring model of consciousness 1:00:15 - Higher order state space model of consciousness 1:05:50 - Comparing their models 1:10:47 - Machine consciousness 1:15:30 - Nature of first order representations 1:18:20 - Consciousness prior (Yoshua Bengio) 1:20:20 - Function of consciousness 1:31:57 - Legacy 1:40:55 - Current projects

Brian and I discuss a range of topics related to his latest book, The Alignment Problem: Machine Learning and Human Values. The alignment problem asks how we can build AI that does what we want it to do, as opposed to building AI that will compromise our own values by accomplishing tasks that may be harmful or dangerous to us. Using some of the stories Brain relates in the book, we talk about:

• The history of machine learning and how we got this point;
• Some methods researches are creating to understand what's being represented in neural nets and how they generate their output;
• Some modern proposed solutions to the alignment problem, like programming the machines to learn our preferences so they can help achieve those preferences - an idea called inverse reinforcement learning;
• The thorny issue of accurately knowing our own values- if we get those wrong, will machines also get it wrong?

Links:

• Brian's website.
• Twitter: @brianchristian.
• The Alignment Problem: Machine Learning and Human Values.
• Related papers
  • Norbert Wiener from 1960: Some Moral and Technical Consequences of Automation.

Timestamps: 4:22 - Increased work on AI ethics 8:59 - The Alignment Problem overview 12:36 - Stories as important for intelligence 16:50 - What is the alignment problem 17:37 - Who works on the alignment problem? 25:22 - AI ethics degree? 29:03 - Human values 31:33 - AI alignment and evolution 37:10 - Knowing our own values? 46:27 - What have learned about ourselves? 58:51 - Interestingness 1:00:53 - Inverse RL for value alignment 1:04:50 - Current progress 1:10:08 - Developmental psychology 1:17:36 - Models as the danger 1:25:08 - How worried are the experts?

Omri, David and I discuss using recurrent neural network models (RNNs) to understand brains and brain function. Omri and David both use dynamical systems theory (DST) to describe how RNNs solve tasks, and to compare the dynamical stucture/landscape/skeleton of RNNs with real neural population recordings. We talk about how their thoughts have evolved since their 2103 Opening the Black Box paper, which began these lines of research and thinking. Some of the other topics we discuss:

• The idea of computation via dynamics, which sees computation as a process of evolving neural activity in a state space;
• Whether DST offers a description of mental function (that is, something beyond brain function, closer to the psychological level);
• The difference between classical approaches to modeling brains and the machine learning approach;
• The concept of universality - that the variety of artificial RNNs and natural RNNs (brains) adhere to some similar dynamical structure despite differences in the computations they perform;
• How learning is influenced by the dynamics in an ongoing and ever-changing manner, and how learning (a process) is distinct from optimization (a final trained state).
• David was on episode 5, for a more introductory episode on dynamics, RNNs, and brains.

• Barak Lab
• Twitter: @SussilloDavid
• The papers we discuss or mention:
  • Sussillo, D. & Barak, O. (2013). Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks.
  • Computation Through Neural Population Dynamics.
  • Implementing Inductive bias for different navigation tasks through diverse RNN attrractors.
  • Dynamics of random recurrent networks with correlated low-rank structure.
  • Quality of internal representation shapes learning performance in feedback neural networks.
  • Feigenbaum's universality constant original paper: Feigenbaum, M. J. (1976) "Universality in complex discrete dynamics", Los Alamos Theoretical Division Annual Report 1975-1976
• Talks
  • Universality and individuality in neural dynamics across large populations of recurrent networks.
  • World Wide Theoretical Neuroscience Seminar: Omri Barak, January 6, 2021

Timestamps: 0:00 - Intro 5:41 - Best scientific moment 9:37 - Why do you do what you do? 13:21 - Computation via dynamics 19:12 - Evolution of thinking about RNNs and brains 26:22 - RNNs vs. minds 31:43 - Classical computational modeling vs. machine learning modeling approach 35:46 - What are models good for? 43:08 - Ecological task validity with respect to using RNNs as models 46:27 - Optimization vs. learning 49:11 - Universality 1:00:47 - Solutions dictated by tasks 1:04:51 - Multiple solutions to the same task 1:11:43 - Direct fit (Uri Hasson) 1:19:09 - Thinking about the bigger picture

K, Josh, and I were postdocs together in Jeff Schall's and Geoff Woodman's labs. K and Josh had backgrounds in psychology and were getting their first experience with neurophysiology, recording single neuron activity in awake behaving primates. This episode is a discussion surrounding their reflections and perspectives on neuroscience and psychology, given their backgrounds and experience (we reference episode 84 with György Buzsáki and David Poeppel). We also talk about their divergent paths - K stayed in academia and runs an EEG lab studying human decision-making and memory, and Josh left academia and has worked for three different pharmaceutical and tech companies. So this episode doesn't get into gritty science questions, but is a light discussion about the state of neuroscience, psychology, and AI, and reflections on academia and industry, life in lab, and plenty more.

• The Fukuda Lab.
• Josh's website.
• Twitter: @KeisukeFukuda4

Time stamps 0:00 - Intro 4:30 - K intro 5:30 - Josh Intro 10:16 - Academia vs. industry 16:01 - Concern with legacy 19:57 - Best scientific moment 24:15 - Experiencing neuroscience as a psychologist 27:20 - Neuroscience as a tool 30:38 - Brain/mind divide 33:27 - Shallow vs. deep knowledge in academia and industry  36:05 - Autonomy in industry 42:20 - Is this a turning point in neuroscience? 46:54 - Deep learning revolution 49:34 - Deep nets to understand brains 54:54 - Psychology vs. neuroscience 1:06:42 - Is language sufficient? 1:11:33 - Human-level AI 1:13:53 - How will history view our era of neuroscience? 1:23:28 - What would you have done differently? 1:26:46 - Something you wish you knew

It's generally agreed machine learning and AI provide neuroscience with tools for analysis and theoretical principles to test in brains, but there is less agreement about what neuroscience can provide AI. Should computer scientists and engineers care about how brains compute, or will it just slow them down, for example? Chris, Sam, and I discuss how neuroscience might contribute to AI moving forward, considering the past and present. This discussion also leads into related topics, like the role of prediction versus understanding, AGI, explainable AI, value alignment, the fundamental conundrum that humans specify the ultimate values of the tasks AI will solve, and more. Plus, a question from previous guest Andrew Saxe. Also, check out Sam's previous appearance on the podcast.

• Chris's lab: Human Information Processing lab.
• Sam's lab: Computational Cognitive Neuroscience Lab.
• Twitter: @gershbrain; @summerfieldlab
• Papers we discuss or mention or are related:
  • If deep learning is the answer, then what is the question?
  • Neuroscience-Inspired Artificial Intelligence.
  • Building Machines that Learn and Think Like People.

0:00 - Intro 5:00 - Good ol' days 13:50 - AI for neuro, neuro for AI 24:25 - Intellectual diversity in AI 28:40 - Role of philosophy 30:20 - Operationalization and benchmarks 36:07 - Prediction vs. understanding 42:48 - Role of humans in the loop 46:20 - Value alignment 51:08 - Andrew Saxe question 53:16 - Explainable AI 58:55 - Generalization 1:01:09 - What has AI revealed about us? 1:09:38 - Neuro for AI 1:20:30 - Concluding remarks

Alison and I discuss her work to accelerate learning and thus improve AI by studying how children learn, as Alan Turing suggested in his famous 1950 paper. The ways children learn are via imitation, by learning abstract causal models, and active learning by implementing a high exploration/exploitation ratio. We also discuss child consciousness, psychedelics, the concept of life history, the role of grandparents and elders, and lots more.

• Alison's Website.
• Cognitive Development and Learning Lab.
• Twitter: @AlisonGopnik.
• Related papers:
  • Childhood as a solution to explore-exploit tensions.
  • The Aeon article about grandparents, children, and evolution: Vulnerable Yet Vital.
• Books:
  • The Gardener and the Carpenter: What the New Science of Child Development Tells Us About the Relationship Between Parents and Children.
  • The Scientist in the Crib: What Early Learning Tells Us About the Mind.
  • The Philosophical Baby: What Children's Minds Tell Us About Truth, Love, and the Meaning of Life.

Take-home points:

• Children learn by imitation, and not just unthinking imitation. They pay attention to and evaluate the intentions of others and judge whether a person seems to be a reliable source of information. That is, they learn by sophisticated socially-constrained imitation.
• Children build abstract causal models of the world. This allows them to simulate potential outcomes and test their actions against those simulations, accelerating learning.
• Children keep their foot on the exploration pedal, actively learning by exploring a wide spectrum of actions to determine what works. As we age, our exploratory cognition decreases, and we begin to exploit more what we've learned.

Timestamps 0:00 - Intro 4:40 - State of the field 13:30 - Importance of learning 20:12 - Turing's suggestion 22:49 - Patience for one's own ideas 28:53 - Learning via imitation 31:57 - Learning abstract causal models 41:42 - Life history 43:22 - Learning via exploration 56:19 - Explore-exploit dichotomy 58:32 - Synaptic pruning 1:00:19 - Breakthrough research in careers 1:04:31 - Role of elders 1:09:08 - Child consciousness 1:11:41 - Psychedelics as child-like brain 1:16:00 - Build consciousness into AI?

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By day, Max Bennett is an entrepreneur. He has cofounded and CEO'd multiple AI and technology companies. By many other countless hours, he has studied brain related sciences. Those long hours of research have payed off in the form of this book, A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.

Three lines of research formed the basis for how Max synthesized knowledge into the ideas in his current book: findings from comparative psychology (comparing brains and minds of different species), evolutionary neuroscience (how brains have evolved), and artificial intelligence, especially the algorithms developed to carry out functions. We go through I think all five of the breakthroughs in some capacity. A recurring theme is that each breakthrough may explain multiple new abilities. For example, the evolution of the neocortex may have endowed early mammals with the ability to simulate or imagine what isn't immediately present, and this ability might further explain mammals' capacity to engage in vicarious trial and error (imagining possible actions before trying them out), the capacity to engage in counterfactual learning (what would have happened if things went differently than they did), and the capacity for episodic memory and imagination.

The book is filled with unifying accounts like that, and it makes for a great read. Strap in, because Max gives a sort of masterclass about many of the ideas in his book.

• Twitter:
  • @maxsbennett
• Book:
  • A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.

0:00 - Intro 5:26 - Why evolution is important 7:22 - Maclean's triune brain 14:59 - Breakthrough 1: Steering 29:06 - Fish intelligence 40:38 - Breakthrough 3: Mentalizing 52:44 - How could we improve the human brain? 1:00:44 - What is intelligence? 1:13:50 - Breakthrough 5: Speaking

Dileep and I discuss his theoretical account of how the thalamus and cortex work together to implement visual inference. We talked previously about his Recursive Cortical Network (RCN) approach to visual inference, which is a probabilistic graph model that can solve hard problems like CAPTCHAs, and more recently we talked about using his RCNs with cloned units to account for cognitive maps related to the hippocampus. On this episode, we walk through how RCNs can map onto thalamo-cortical circuits so a given cortical column can signal whether it believes some concept or feature is present in the world, based on bottom-up incoming sensory evidence, top-down attention, and lateral related features. We also briefly compare this bio-RCN version with Randy O'Reilly's Deep Predictive Learning account of thalamo-cortical circuitry.

• Vicarious website - Dileeps AGI robotics company.
• Twitter: @dileeplearning
• The papers we discuss or mention:
  • A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model.
  • From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence.
• Probabilistic graphical models.
• Hierarchical temporal memory.

Time Stamps:

0:00 - Intro 5:18 - Levels of abstraction 7:54 - AGI vs. AHI vs. AUI 12:18 - Ideas and failures in startups 16:51 - Thalamic cortical circuitry computation  22:07 - Recursive cortical networks 23:34 - bio-RCN 27:48 - Cortical column as binary random variable 33:37 - Clonal neuron roles 39:23 - Processing cascade 41:10 - Thalamus 47:18 - Attention as explaining away 50:51 - Comparison with O'Reilly's predictive coding framework 55:39 - Subjective contour effect 1:01:20 - Necker cube

Russ and I discuss cognitive ontologies - the "parts" of the mind and their relations - as an ongoing dilemma of how to map onto each other what we know about brains and what we know about minds. We talk about whether we have the right ontology now, how he uses both top-down and data-driven approaches to analyze and refine current ontologies, and how all this has affected his own thinking about minds. We also discuss some of the current  meta-science issues and challenges in neuroscience  and AI, and Russ answers guest questions from Kendrick Kay and David Poeppel.

• Russ’s website.
• Poldrack Lab.
• Stanford Center For Reproducible Neuroscience.
• Twitter: @russpoldrack.
• Book:
  • The New Mind Readers: What Neuroimaging Can and Cannot Reveal about Our Thoughts.
• The papers we discuss or mention:
  • Atlases of cognition with large-scale human brain mapping.
  • Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?
  • From Brain Maps to Cognitive Ontologies: Informatics and the Search for Mental Structure.
  • Uncovering the structure of self-regulation through data-driven ontology discovery
• Talks:
  • Reproducibility: NeuroHackademy: Russell Poldrack - Reproducibility in fMRI: What is the problem?
  • Cognitive Ontology: Cognitive Ontologies, from Top to Bottom
  • A good series of talks about cognitive ontologies: Online Seminar Series: Problem of Cognitive Ontology.

Some take-home points:

• Our folk psychological cognitive ontology hasn't changed much since early Greek Philosophy, and especially since William James wrote about attention, consciousness, and so on.
• Using encoding models, we can predict brain responses pretty well based on what task a subject is performing or what "cognitive function" a subject is engaging, at least to a course approximation.
• Using a data-driven approach has potential to help determine mental structure, but important human decisions must still be made regarding how exactly to divide up the various "parts" of the mind.

Time points 0:00 - Introduction 5:59 - Meta-science issues 19:00 - Kendrick Kay question 23:00 - State of the field 30:06 - fMRI for understanding minds 35:13 - Computational mind 42:10 - Cognitive ontology 45:17 - Cognitive Atlas 52:05 - David Poeppel question 57:00 - Does ontology matter? 59:18 - Data-driven ontology 1:12:29 - Dynamical systems approach 1:16:25 - György Buzsáki's inside-out approach 1:22:26 - Ontology for AI 1:27:39 - Deep learning hype 

Carsen and I discuss how she uses 2-photon calcium imaging data from over 10,000 neurons to understand the information processing of such large neural population activity. We talk about the tools she makes and uses to analyze the data, and the type of high-dimensional neural activity structure they found, which seems to allow efficient and robust information processing. We also talk about how these findings may help build better deep learning networks, and Carsen's thoughts on how to improve the diversity, inclusivity, and equality in neuroscience research labs. Guest question from Matt Smith.

• Stringer Lab.
• Twitter: @computingnature.
• The papers we discuss or mention:
  • High-dimensional geometry of population responses in visual cortex
  • Spontaneous behaviors drive multidimensional, brain-wide population activity.

Timestamps:

0:00 - Intro 5:51 - Recording > 10k neurons 8:51 - 2-photon calcium imaging 14:56 - Balancing scientific questions and tools 21:16 - Unsupervised learning tools and rastermap 26:14 - Manifolds 32:13 - Matt Smith question 37:06 - Dimensionality of neural activity 58:51 - Future plans 1:00:30- What can AI learn from this? 1:13:26 - Diversity, inclusivity, equality

Chris and I discuss his Spaun large scale model of the human brain (Semantic Pointer Architecture Unified Network), as detailed in his book How to Build a Brain. We talk about his philosophical approach, how Spaun compares to Randy O'Reilly's Leabra networks, the Applied Brain Research Chris co-founded, and I have guest questions from Brad Aimone, Steve Potter, and Randy O'Reilly.

• Chris's website.
• Applied Brain Research.
• The book: How to Build a Brain.
• Nengo (you can run Spaun).
• Paper summary of Spaun: A large-scale model of the functioning brain.

Some takeaways:

• Spaun is an embodied fully functional cognitive architecture with one eye for task instructions and an arm for responses.
• Chris uses elements from symbolic, connectionist, and dynamical systems approaches in cognitive science.
• The neural engineering framework (NEF) is how functions get instantiated in spiking neural networks.
• The semantic pointer architecture (SPA) is how representations are stored and transformed - i.e. the symbolic-like cognitive processing.

Time Points:

0:00 - Intro 2:29 - Sense of awe 6:20 - Large-scale models 9:24 - Descriptive pragmatism 15:43 - Asking better questions 22:48 - Brad Aimone question: Neural engineering framework 29:07 - Engineering to build vs. understand 32:12 - Why is AI world not interested in brains/minds? 37:09 - Steve Potter neuromorphics question 44:51 - Spaun 49:33 - Semantic Pointer Architecture 56:04 - Representations 58:21 - Randy O'Reilly question 1 1:07:33 - Randy O'Reilly question 2 1:10:31 - Spaun vs. Leabra 1:32:43 - How would Chris start over?

Matt and I discuss how cognition and behavior drifts over the course of minutes and hours, and how global brain activity drifts with it. How does the brain continue to produce steady perception and action in the midst of such drift? We also talk about how to think about variability in neural activity. How much of it is noise and how much of it is hidden important activity? Finally, we discuss the effect of recording more and more neurons simultaneously, collecting bigger and bigger datasets, plus guest questions from Adam Snyder and Patrick Mayo.

• Smith Lab.
• Twitter: @SmithLabNeuro.
• Related:
  • Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex.
• Artwork by Melissa Neely

Take home points:

• The “noise” in the variability of neural activity is likely just activity devoted to processing other things.
• Recording lots of neurons simultaneously helps resolve the question of what’s noise and how much information is in a population of neurons.
• There’s a neural signature of the behavioral “slow drift” of our internal cognitive state.
• The neural signature is global, and it’s an open question how the brain compensates to produce steady perception and action.

Timestamps:

0:00 - Intro 4:35 - Adam Snyder question  15:26 - Multi-electrode recordings  17:48 - What is noise in the brain?  23:55 - How many neurons is enough?  27:43 - Patrick Mayo question  33:17 - Slow drift  54:10 - Impulsivity  57:32 - How does drift happen?  59:49 - Relation to AI  1:06:58 - What AI and neuro can teach each other  1:10:02 - Ecologically valid behavior  1:14:39 - Brain mechanisms vs. mind  1:17:36 - Levels of description  1:21:14 - Hard things to make in AI  1:22:48 - Best scientific moment 

Randy and I discuss his LEABRA cognitive architecture that aims to simulate the human brain, plus his current theory about how a loop between cortical regions and the thalamus could implement predictive learning and thus solve how we learn with so few examples. We also discuss what Randy thinks is the next big thing neuroscience can contribute to AI (thanks to a guest question from Anna Schapiro), and much more.

• Computational Cognitive Neuroscience Laboratory.
• The papers we discuss or mention:
  • The Leabra Cognitive Architecture: How to Play 20 Principles with Nature and Win!
  • Deep Predictive Learning in Neocortex and Pulvinar.
  • Unraveling the Mysteries of Motivation.
• His youTube series detailing the theory and workings of Leabra:
  • Computational Cognitive Neuroscience.
• The free textbook:
  • Computational Cognitive Neuroscience

A few take-home points:

• Leabra has been a slow incremental project, inspired in part by Alan Newell’s suggested approach.
• Randy began by developing a learning algorithm that incorporated both kinds of biological learning (error-driven and associative).
• Leabra's core is 3 brain areas - frontal cortex, parietal cortex, and hippocampus - and has grown from there.
• There’s a constant balance between biological realism and computational feasibility.
• It’s important that a cognitive architecture address multiple levels- micro-scale, macro-scale, mechanisms, functions, and so on.
• Deep predictive learning is a possible brain mechanism whereby predictions from higher layer cortex precede input from lower layer cortex in the thalamus, where an error is computed and used to drive learning.
• Randy believes our metacognitive ability to know what we do and don’t know is a key next function to build into AI.

Timestamps: 0:00 -  Intro  3:54 - Skip Intro  6:20 - Being in awe  18:57 - How current AI can inform neuro  21:56 - Anna Schapiro question - how current neuro can inform AI. 29:20 - Learned vs. innate cognition  33:43 - LEABRA  38:33 - Developing Leabra  40:30 - Macroscale 42:33 - Thalamus as microscale  43:22 - Thalamocortical circuitry  47:25 - Deep predictive learning  56:18 - Deep predictive learning vs. backrop  1:01:56 - 10 Hz learning cycle  1:04:58 - Better theory vs. more data  1:08:59 - Leabra vs. Spaun  1:13:59 - Biological realism  1:21:54 - Bottom-up inspiration  1:27:26 - Biggest mistake in Leabra  1:32:14 - AI consciousness  1:34:45 - How would Randy begin again? 

When a waiter hands me the bill, how do I know whether to pay it myself or let my date pay? On this episode, I get a progress update from Dileep on his company, Vicarious, since Dileep's last episode. We also talk broadly about his experience running Vicarious to develop AGI and robotics. Then we turn to his latest brain-inspired AI efforts using cloned structured probabilistic graph models to develop an account of how the hippocampus makes a model of the world and represents our cognitive maps in different contexts, so we can simulate possible outcomes to choose how to act.

Special guest questions from Brad Love (episode 70: How We Learn Concepts) .

• Vicarious website - Dileep's AGI robotics company.
• Twitter: @dileeplearning.
• Papers we discuss:
  • Learning cognitive maps as structured graphs for vicarious evaluation.
  • A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model.
• Probabilistic graphical models.
• Hierarchical temporal memory.

Time stamps:

0:00 - Intro 3:00 - Skip Intro 4:00 - Previous Dileep episode 10:22 - Is brain-inspired AI over-hyped? 14:38 - Compteition in robotics field 15:53 - Vicarious robotics 22:12 - Choosing what product to make 28:13 - Running a startup 30:52 - Old brain vs. new brain 37:53 - Learning cognitive maps as structured graphs 41:59 - Graphical models 47:10 - Cloning and merging, hippocampus 53:36 - Brad Love Question 1 1:00:39 - Brad Love Question 2 1:02:41 - Task examples 1:11:56 - What does hippocampus do? 1:14:14 - Intro to thalamic cortical microcircuit 1:15:21 - What AI folks think of brains 1:16:57 - Which levels inform which levels 1:20:02 - Advice for an AI startup

Ken and I discuss open-endedness, the pursuit of ambitious goals by seeking novelty and interesting products instead of advancing directly toward defined objectives. We talk about evolution as a prime example of an open-ended system that has produced astounding organisms, Ken relates how open-endedness could help advance artificial intelligence and neuroscience, and we discuss a range of topics related to the general concept of open-endedness, and Ken takes a couple questions from Stefan Leijnen and Melanie Mitchell.

Related:

• Ken's website.
• Twitter: @kenneth0stanley.
• The book:
  • Why Greatness Cannot Be Planned: The Myth of the Objective by Kenneth Stanley and Joel Lehman.
• Papers:
  • Evolving Neural Networks Through Augmenting Topologies (2002)
  • Minimal Criterion Coevolution: A New Approach to Open-Ended Search

Some key take-aways:

• Many of the best inventions were not the result of trying to achieve a specific objective.
• Open-endedness is the pursuit of ambitious advances without a clearly defined objective.
• Evolution is a quintessential example of an open-ended process: it produces a vast array of complex beings by searching the space of possible organisms, constrained by the environment, survival, and reproduction.
• Perhaps the key to developing artificial general intelligence is by following an open-ended path rather that pursing objectives (solving the same old benchmark tasks, etc.).

0:00 - Intro 3:46 - Skip Intro 4:30 - Evolution as an Open-ended process 8:25 - Why Greatness Cannot Be Planned 20:46 - Open-endedness in AI 29:35 - Constraints vs. objectives 36:26 - The adjacent possible 41:22 - Serendipity 44:33 - Stefan Leijnen question 53:11 - Melanie Mitchell question 1:00:32 - Efficiency 1:02:13 - Gentle Earth 1:05:25 - Learning vs. evolution 1:10:53 - AGI 1:14:06 - Neuroscience, AI, and open-endedness 1:26:06 - Open AI

Ida and I discuss the current landscape of reinforcement learning in both natural and artificial intelligence, and how the old story of two RL systems in brains - model-free and model-based - is giving way to a more nuanced story of these two systems constantly interacting and additional RL strategies between model-free and model-based to drive the vast repertoire of our habits and goal-directed behaviors. We discuss Ida’s work on one of those “in-between” strategies, the successor representation RL strategy, which maps onto brain activity and accounts for behavior. We also discuss her interesting background and how it affects her outlook and research pursuit, and the role philosophy has played and continues to play in her thought processes.

Related links:

• Ida’s website.
• Twitter: @criticalneuro.
• A nice review of what we discuss:
  • Learning Structures: Predictive Representations, Replay, and Generalization.

Time stamps:

0:00 - Intro 4:50 - Skip intro 9:58 - Core way of thinking 19:58 - Disillusionment 27:22 - Role of philosophy 34:51 - Optimal individual learning strategy 39:28 - Microsoft job 44:48 - Field of reinforcement learning 51:18 - Learning vs. innate priors 59:47 - Incorporating other cognition into RL 1:08:24 - Evolution 1:12:46 - Model-free and model-based RL 1:19:02 - Successor representation 1:26:48 - Are we running all algorithms all the time? 1:28:38 - Heuristics and intuition 1:33:48 - Levels of analysis 1:37:28 - Consciousness

David, Gyuri, and I discuss the issues they argue for in their back and forth commentaries about the importance of neuroscience and psychology, or implementation-level and computational-level, to advance our understanding of brains and minds - and the names we give to the things we study. Gyuri believes it’s time we use what we know and discover about brain mechanisms to better describe the psychological concepts we refer to as explanations for minds; David believes the psychological concepts are constantly being refined and are just as valid as objects of study to understand minds. They both agree these are important and enjoyable topics to debate. Also, special guest questions from Paul Cisek and John Krakauer.

Related:

• Buzsáki lab; Poeppel lab
• Twitter: @davidpoeppel.
• The papers we discuss or mention:
  • Calling Names by Christophe Bernard
  • The Brain–Cognitive Behavior Problem: A Retrospective by György Buzsáki.
  • Against the Epistemological Primacy of the Hardware: The Brain from Inside Out, Turned Upside Down by David Poeppel.
• Books:
  • The Brain from Inside Out by György Buzsáki.
  • The Cognitive Neurosciences (edited by David Poeppel et al).

Timeline:

0:00 - Intro 5:31 - Skip intro 8:42 - Gyuri and David summaries 25:45 - Guest questions 36:25 - Gyuri new language 49:41 - Language and oscillations 53:52 - Do we know what cognitive functions we're looking for? 58:25 - Psychiatry 1:00:25 - Steve Grossberg approach 1:02:12 - Neuroethology 1:09:08 - AI as tabula rasa 1:17: 40 - What's at stake? 1:36:20 - Will the space between neuroscience and psychology disappear?

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Welcome to another special panel discussion episode.

I was recently invited to moderate at discussion amongst 6 people at the annual Aspirational Neuroscience meetup. Aspirational Neuroscience is a nonprofit community run by Kenneth Hayworth. Ken has been on the podcast before on episode 103. Ken helps me introduce the meetup and panel discussion for a few minutes. The goal in general was to discuss how current and developing neuroscience technologies might be used to decode a nontrivial memory from a static connectome - what the obstacles are, how to surmount those obstacles, and so on.

There isn't video of the event, just audio, and because we were all sharing microphones and they were being passed around, you'll hear some microphone type noise along the way - but I did my best to optimize the audio quality, and it turned out mostly quite listenable I believe.

• Aspirational Neuroscience
• Panelists:
  • Anton Arkhipov, Allen Institute for Brain Science.
    • @AntonSArkhipov
  • Konrad Kording, University of Pennsylvania.
    • @KordingLab
  • Tomás Ryan, Trinity College Dublin.
    • @TJRyan_77
  • Srinivas Turaga, Janelia Research Campus.
  • Dong Song, University of Southern California.
    • @dongsong
  • Zhihao Zheng, Princeton University.
    • @zhihaozheng

0:00 - Intro 1:45 - Ken Hayworth 14:09 - Panel Discussion

Jane and I discuss the relationship between AI and neuroscience (cognitive science, etc), from her perspective at Deepmind after a career researching natural intelligence. We also talk about her meta-reinforcement learning work that connects deep reinforcement learning with known brain circuitry and processes, and finally we talk about her recent work using evolutionary strategies to develop altruism and cooperation among the agents in a multi-agent reinforcement learning environment.

Related:

• Jane’s website.
• Twitter: @janexwang. 
• The papers we discuss or mention:
  • Learning to reinforcement learn.
  • Blog post with a link to the paper: Prefrontal cortex as a meta-reinforcement learning system.
  • Deep Reinforcement Learning and its Neuroscientific Implications
  • Evolving Intrinsic Motivations for Altruistic Behavior.
• Books she recommended:
  • Human Compatible: AI and the Problem of Control, by Stuart Russell:
  • Algorithms to Live By, by Brian Christian and Tom Griffiths.

Timeline:

0:00 - Intro 3:36 - Skip Intro 4:45 - Transition to Deepmind 19:56 - Changing perspectives on neuroscience 24:49 - Is neuroscience useful for AI? 33:11 - Is deep learning hitting a wall? 35:57 - Meta-reinforcement learning 52:00 - Altruism in multi-agent RL

Steve and I discuss his long and productive career as a theoretical neuroscientist. We cover his tried and true method of taking a large body of psychological behavioral findings, determining how they fit together and what’s paradoxical about them, developing design principles, theories, and models from that body of data, and using experimental neuroscience to inform and confirm his model predictions. We talk about his Adaptive Resonance Theory (ART) to describe how our brains are self-organizing, adaptive, and deal with changing environments. We also talk about his complementary computing paradigm to describe how two systems can complement each other to create emergent properties neither system can create on its own , how the resonant states in ART support consciousness, his place in the history of both neuroscience and AI, and quite a bit more.

Related:

• Steve's BU website.
• Some papers we discuss or mention (much more on his website):
  • Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world.
  • Towards solving the Hard Problem of Consciousness: The varieties of brain resonances and the conscious experiences that they support.
  • A Path Toward Explainable AI and Autonomous Adaptive Intelligence: Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action.

Topics Time stamps:

0:00 - Intro 5:48 - Skip Intro 9:42 - Beginnings 18:40 - Modeling method 44:05 - Physics vs. neuroscience 54:50 - Historical credit for Hopfield network 1:03:40 - Steve's upcoming book 1:08:24 - Being shy 1:11:21 - Stability plasticity dilemma 1:14:10 - Adaptive resonance theory 1:18:25 - ART matching rule 1:21:35 - Consciousness as resonance 1:29:15 - Complementary computing 1:38:58 - Vigilance to re-orient 1:54:58 - Deep learning vs. ART

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.

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Laura Gradowski is a philosopher of science at the University of Pittsburgh. Pluralism is roughly the idea that there is no unified account of any scientific field, that we should be tolerant of and welcome a variety of theoretical and conceptual frameworks, and methods, and goals, when doing science. Pluralism is kind of a buzz word right now in my little neuroscience world, but it's an old and well-trodden notion... many philosophers have been calling for pluralism for many years. But how pluralistic should we be in our studies and explanations in science? Laura suggests we should be very, very pluralistic, and to make her case, she cites examples in the history of science of theories and theorists that were once considered "fringe" but went on to become mainstream accepted theoretical frameworks. I thought it would be fun to have her on to share her ideas about fringe theories, mainstream theories, pluralism, etc.

We discuss a wide range of topics, but also discuss some specific to the brain and mind sciences. Laura goes through an example of something and someone going from fringe to mainstream - the Garcia effect, named after John Garcia, whose findings went agains the grain of behaviorism, the dominant dogma of the day in psychology. But this overturning only happened after Garcia had to endure a long scientific hell of his results being ignored and shunned. So, there are multiple examples like that, and we discuss a handful. This has led Laura to the conclusion we should accept almost all theoretical frameworks, We discuss her ideas about how to implement this, where to draw the line, and much more.

0:00 - Intro 3:57 - What is fringe? 10:14 - What makes a theory fringe? 14:31 - Fringe to mainstream 17:23 - Garcia effect 28:17 - Fringe to mainstream: other examples 32:38 - Fringe and consciousness 33:19 - Words meanings change over time 40:24 - Pseudoscience 43:25 - How fringe becomes mainstream 47:19 - More fringe characteristics 50:06 - Pluralism as a solution 54:02 - Progress 1:01:39 - Encyclopedia of theories 1:09:20 - When to reject a theory 1:20:07 - How fringe becomes fringe 1:22:50 - Marginilization 1:27:53 - Recipe for fringe theorist

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.

Patrick and I mostly discuss his path from a technician in the then nascent Jim DiCarlo lab, through his graduate school and two postdoc experiences, and finally landing a faculty position, plus the culture and issues in academia in general. We also cover plenty of science, like the role of eye movements in the study of vision, the neuroscience (and concept) of attention, what Patrick thinks of the deep learning hype, and more.

But, this is a special episode, less about the science and more about the experience of an academic neuroscience trajectory/life. Episodes like this will appear in Patreon supporters' private feeds from now on.

Show notes:

• His pre-lab website university page.
• Twitter: @mayo_lab.
• Here’s the paper he recommends to understand attention:
  • Attention can be subdivided into neurobiological components corresponding to distinct behavioral effects.

Brad and I discuss his battle-tested, age-defying cognitive model for how we learn and store concepts by forming and rearranging clusters, how the model maps onto brain areas, and how he's using deep learning models to explore how attention and sensory information interact with concept formation. We also discuss the cognitive modeling approach, Marr's levels of analysis, the term "biological plausibility", emergence and reduction, and plenty more.

Notes:

• Visit Brad’s website.
• Follow Brad on twitter: @ProfData.
• Related papers:
  • Levels of Biological Plausibility.
  • Models in search of a brain.
  • A non-spatial account of place and grid cells based on clustering models of concept learning.
  • Abstract neural representations of category membership beyond information coding stimulus or response.
  • Ventromedial prefrontal cortex compression during concept learning.
  • The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks
  • Learning as the unsupervised alignment of conceptual systems.

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.

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Eric Shea-Brown is a theoretical neuroscientist and principle investigator of the working group on neural dynamics at the University of Washington. In this episode, we talk a lot about dynamics and dimensionality in neural networks... how to think about them, why they matter, how Eric's perspectives have changed through his career. We discuss a handful of his specific research findings about dynamics and dimensionality, like how dimensionality changes when one is performing a task versus when you're just sort of going about your day, what we can say about dynamics just by looking at different structural connection motifs, how different modes of learning can rely on different dimensionalities, and more.We also talk about how he goes about choosing what to work on and how to work on it. You'll hear in our discussion how much credit Eric gives to those surrounding him and those who came before him - he drops tons of references and names, so get ready if you want to follow up on some of the many lines of research he mentions.

• Eric's website.
• Related papers
  • Predictive learning as a network mechanism for extracting low-dimensional latent space representations.
  • A scale-dependent measure of system dimensionality.
  • From lazy to rich to exclusive task representations in neural networks and neural codes.
  • Feedback through graph motifs relates structure and function in complex networks.

0:00 - Intro 4:15 - Reflecting on the rise of dynamical systems in neuroscience 11:15 - DST view on macro scale 15:56 - Intuitions 22:07 - Eric's approach 31:13 - Are brains more or less impressive to you now? 38:45 - Why is dimensionality important? 50:03 - High-D in Low-D 54:14 - Dynamical motifs 1:14:56 - Theory for its own sake 1:18:43 - Rich vs. lazy learning 1:22:58 - Latent variables 1:26:58 - What assumptions give you most pause?

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.