Brain Inspired

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Hakwan and I discuss many of the topics in his new book, In Consciousness we Trust: The Cognitive Neuroscience of Subjective Experience. Hakwan describes his perceptual reality monitoring theory of consciousness, which suggests consciousness may act as a systems check between our sensory perceptions and higher cognitive functions. We also discuss his latest thoughts on mental quality space and how it relates to perceptual reality monitoring. Among many other topics, we chat about the many confounds and challenges to empirically studying consciousness, a topic featured heavily in the first half of his book. Hakwan was on a previous episode with Steve Fleming, BI 099 Hakwan Lau and Steve Fleming: Neuro-AI Consciousness.

• Hakwan's lab: Consciousness and Metacognition Lab.
• Twitter: @hakwanlau.
• Book:
  • In Consciousness we Trust: The Cognitive Neuroscience of Subjective Experience.

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Tomás and I discuss his research and ideas on how memories are encoded (the engram), the role of forgetting, and the overlapping mechanisms of memory and instinct. Tomás uses otpogenetics and other techniques to label and control neurons involved in learning and memory, and has shown that forgotten memories can be restored by stimulating "engram cells" originally associated with the forgotten memory. This line of research has led Tomás to think forgetting might be a learning mechanism itself, a adaption our brains make based on the predictability and affordances of the environment. His work on engrams has also led Tomás to think our instincts (ingrams) may share the same mechanism of our memories (engrams), and that memories may transition to instincts across generations. We begin by addressing Randy Gallistel's engram ideas from the previous episode: BI 126 Randy Gallistel: Where Is the Engram?

• Ryan Lab.
• Twitter: @TJRyan_77.
• Related papers
  • Engram cell connectivity: an evolving substrate for information storage.
  • Forgetting as a form of adaptive engram cell plasticity.
  • Memory and Instinct as a Continuum of Information Storage in The Cognitive Neurosciences.
  • The Bandwagon by Claude Shannon.

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Randy and I discuss his long-standing interest in how the brain stores information to compute. That is, where is the engram, the physical trace of memory in the brain? Modern neuroscience is dominated by the view that memories are stored among synaptic connections in populations of neurons. Randy believes a more reasonable and reliable way to store abstract symbols, like numbers, is to write them into code within individual neurons. Thus, the spiking code, whatever it is, functions to write and read memories into and out of intracellular substrates, like polynucleotides (DNA, RNA, e.g.). He lays out his case in detail in his book with Adam King, Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience. We also talk about some research and theoretical work since then that support his views.

• Randy's Rutger's website.
• Book:
  • Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience.
• Related papers:
  • The theoretical RNA paper Randy mentions: An RNA-based theory of natural universal computation.
  • Evidence for intracellular engram in cerebellum: Memory trace and timing mechanism localized to cerebellar Purkinje cells.
• The exchange between Randy and John Lisman.
• The blog post Randy mentions about Universal function approximation:
  • The Truth About the [Not So] Universal Approximation Theorem

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Doris, Tony, and Blake are the organizers for this year's NAISys conference, From Neuroscience to Artificially Intelligent Systems (NAISys), at Cold Spring Harbor. We discuss the conference itself, some history of the neuroscience and AI interface, their current research interests, and a handful of topics around evolution, innateness, development, learning, and the current and future prospects for using neuroscience to inspire new ideas in artificial intelligence.

• From Neuroscience to Artificially Intelligent Systems (NAISys).
• Doris:
  • @doristsao.
  • Tsao Lab.
  • Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.
• Tony:
  • @TonyZador.
  • Zador Lab.
  • A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains.
• Blake:
  • @tyrell_turing.
  • The Learning in Neural Circuits Lab.
  • The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning.

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Robin and I discuss many of the ideas in his book The Self-Assembling Brain: How Neural Networks Grow Smarter. The premise is that our DNA encodes an algorithmic growth process that unfolds information via time and energy, resulting in a connected neural network (our brains!) imbued with vast amounts of information from the "start". This contrasts with modern deep learning networks, which start with minimal initial information in their connectivity, and instead rely almost solely on learning to gain their function. Robin suggests we won't be able to create anything with close to human-like intelligence unless we build in an algorithmic growth process and an evolutionary selection process to create artificial networks.

• Hiesinger Neurogenetics Laboratory
• Twitter: @HiesingerLab.
• Book: The Self-Assembling Brain: How Neural Networks Grow Smarter

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Irina is a faculty member at MILA-Quebec AI Institute and a professor at Université de Montréal. She has worked from both ends of the neuroscience/AI interface, using AI for neuroscience applications, and using neural principles to help improve AI. We discuss her work on biologically-plausible alternatives to back-propagation, using "auxiliary variables" in addition to the normal connection weight updates. We also discuss the world of lifelong learning, which seeks to train networks in an online manner to improve on any tasks as they are introduced. Catastrophic forgetting is an obstacle in modern deep learning, where a network forgets old tasks when it is trained on new tasks. Lifelong learning strategies, like continual learning, transfer learning, and meta-learning seek to overcome catastrophic forgetting, and we talk about some of the inspirations from neuroscience being used to help lifelong learning in networks.

• Irina's website.
• Twitter: @irinarish
• Related papers:
  • Beyond Backprop: Online Alternating Minimization with Auxiliary Variables.
  • Towards Continual Reinforcement Learning: A Review and Perspectives.
• Lifelong learning video tutorial: DLRL Summer School 2021 - Lifelong Learning - Irina Rish.

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Ko and I discuss a range of topics around his work to understand our visual intelligence. Ko was a postdoc in James Dicarlo's lab, where he helped develop the convolutional neural network models that have become the standard for explaining core object recognition. He is starting his own lab at York University, where he will continue to expand and refine the models, adding important biological details and incorporating models for brain areas outside the ventral visual stream. He will also continue recording neural activity, and performing perturbation studies to better understand the networks involved in our visual cognition.

• VISUAL INTELLIGENCE AND TECHNOLOGICAL ADVANCES LAB
• Twitter: @KohitijKar.
• Related papers
  • Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior.
  • Neural population control via deep image synthesis.
• BI 075 Jim DiCarlo: Reverse Engineering Vision

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Mac and I discuss his systems level approach to understanding brains, and his theoretical work suggesting important roles for the thalamus, basal ganglia, and cerebellum, shifting the dynamical landscape of brain function within varying behavioral contexts. We also discuss his recent interest in the ascending arousal system and neuromodulators. Mac thinks the neocortex has been the sole focus of too much neuroscience research, and that the subcortical brain regions and circuits have a much larger role underlying our intelligence.

• Shine Lab
• Twitter: @jmacshine
• Related papers
  • The thalamus integrates the macrosystems of the brain to facilitate complex, adaptive brain network dynamics.
  • Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics.

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James, Andrew, and Weinan discuss their recent theory about how the brain might use complementary learning systems to optimize our memories. The idea is that our hippocampus creates our episodic memories for individual events, full of particular details. And through a complementary process, slowly consolidates those memories within our neocortex through mechanisms like hippocampal replay. The new idea in their work suggests a way for the consolidated cortical memory to become optimized for generalization, something humans are known to be capable of but deep learning has yet to build. We discuss what their theory predicts about how the "correct" process depends on how much noise and variability there is in the learning environment, how their model solves this, and how it relates to our brain and behavior.

• James' Janelia page.
• Weinan's Janelia page.
• Andrew's website.
• Twitter:
  • Andrew: @SaxeLab
  • Weinan: @sunw37
• Paper we discuss:
  • Organizing memories for generalization in complementary learning systems.
• Andrew's previous episode: BI 052 Andrew Saxe: Deep Learning Theory

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Henry and I discuss why he thinks neuroscience is in a crisis (in the Thomas Kuhn sense of scientific paradigms, crises, and revolutions). Henry thinks our current concept of the brain as an input-output device, with cognition in the middle, is mistaken. He points to the failure of neuroscience to successfully explain behavior despite decades of research. Instead, Henry proposes the brain is one big hierarchical set of control loops, trying to control their output with respect to internally generated reference signals. He was inspired by control theory, but points out that most control theory for biology is flawed by not recognizing that the reference signals are internally generated. Instead, most control theory approaches, and neuroscience research in general, assume the reference signals are what gets externally supplied... by the experimenter.

• Yin lab at Duke.
• Twitter: @HenryYin19.
• Related papers
  • The Crisis in Neuroscience.
  • Restoring Purpose in Behavior.
  • Achieving natural behavior in a robot using neurally inspired hierarchical perceptual control.

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