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Brain Inspired

Brain Inspired

Paul Middlebrooks

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.
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Top 10 Brain Inspired Episodes

Goodpods has curated a list of the 10 best Brain Inspired episodes, ranked by the number of listens and likes each episode have garnered from our listeners. If you are listening to Brain Inspired for the first time, there's no better place to start than with one of these standout episodes. If you are a fan of the show, vote for your favorite Brain Inspired episode by adding your comments to the episode page.

Brain Inspired - BI 171 Mike Frank: Early Language and Cognition
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07/22/23 • 84 min

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

My guest is Michael C. Frank, better known as Mike Frank, who runs the Language and Cognition lab at Stanford. Mike's main interests center on how children learn language - in particular he focuses a lot on early word learning, and what that tells us about our other cognitive functions, like concept formation and social cognition.

We discuss that, his love for developing open data sets that anyone can use,

The dance he dances between bottom-up data-driven approaches in this big data era, traditional experimental approaches, and top-down theory-driven approaches

How early language learning in children differs from LLM learning

Mike's rational speech act model of language use, which considers the intentions or pragmatics of speakers and listeners in dialogue.

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Brain Inspired - BI 077 David and John Krakauer: Part 1
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07/14/20 • 93 min

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.

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Brain Inspired - BI 074 Ginger Campbell: Are You Sure?
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06/16/20 • 82 min

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.

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Brain Inspired - BI 079 Romain Brette: The Coding Brain Metaphor
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07/27/20 • 79 min

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).

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Brain Inspired - BI 078 David and John Krakauer: Part 2
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07/17/20 • 74 min

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.

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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.

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Brain Inspired - BI 076 Olaf Sporns: Network Neuroscience
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07/04/20 • 105 min

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.

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What is creativity? How do we measure it? How do our brains implement it, and how might AI?Those are some of the questions John, David, and I discuss. The neuroscience of creativity is young, in its "wild west" days still. We talk about a few creativity studies they've performed that distinguish different creative processes with respect to different levels of expertise (in this case, in jazz improvisation), and the underlying brain circuits and activity, including using transcranial direct current stimulation to alter the creative process. Related to creativity, we also discuss the phenomenon and neuroscience of insight (the topic of John's book, The Eureka Factor), unconscious automatic type 1 processes versus conscious deliberate type 2 processes, states of flow, creative process versus creative products, and a lot more.

Timestamps 0:00 - Intro 16:20 - Where are we broadly in science of creativity? 18:23 - Origins of creativity research 22:14 - Divergent and convergent thought 26:31 - Secret Chord Labs 32:40 - Familiar surprise 38:55 - The Eureka Factor 42:27 - Dual process model 52:54 - Creativity and jazz expertise 55:53 - "Be creative" behavioral study 59:17 - Stimulating the creative brain 1:02:04 - Brain circuits underlying creativity 1:14:36 - What does this tell us about creativity? 1:16:48 - Intelligence vs. creativity 1:18:25 - Switching between creative modes 1:25:57 - Flow states and insight 1:34:29 - Creativity and insight in AI 1:43:26 - Creative products vs. process

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Brain Inspired - BI 122 Kohitij Kar: Visual Intelligence
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12/12/21 • 93 min

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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.

0:00 - Intro 3:49 - Background 13:51 - Where are we in understanding vision? 19:46 - Benchmarks 21:21 - Falsifying models 23:19 - Modeling vs. experiment speed 29:26 - Simple vs complex models 35:34 - Dorsal visual stream and deep learning 44:10 - Modularity and brain area roles 50:58 - Chemogenetic perturbation, DREADDs 57:10 - Future lab vision, clinical applications 1:03:55 - Controlling visual neurons via image synthesis 1:12:14 - Is it enough to study nonhuman animals? 1:18:55 - Neuro/AI intersection 1:26:54 - What is intelligence?

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Brain Inspired - BI 075 Jim DiCarlo: Reverse Engineering Vision
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06/24/20 • 76 min

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.

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FAQ

How many episodes does Brain Inspired have?

Brain Inspired currently has 213 episodes available.

What topics does Brain Inspired cover?

The podcast is about Natural Sciences, Podcasts, Technology and Science.

What is the most popular episode on Brain Inspired?

The episode title 'BI 073 Megan Peters: Consciousness and Metacognition' is the most popular.

What is the average episode length on Brain Inspired?

The average episode length on Brain Inspired is 83 minutes.

How often are episodes of Brain Inspired released?

Episodes of Brain Inspired are typically released every 10 days, 2 hours.

When was the first episode of Brain Inspired?

The first episode of Brain Inspired was released on Aug 2, 2018.

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