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PODCAST

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

science natural sciences

All Episodes

00:49:34

BI 015 Terrence Sejnowski: How to Start a Deep...

Brain Inspired ·

2018/10/25

Show notes: His new book, The Deep Learning Revolution: His Computational Neurobiology Laboratory at the Salk Institute. His faculty page at UCSD. His first book, The Computational Brain. His online course with Barbara Oakley, Learning How To Learn. Steven Johnson’s book Where Good Ideas Come From.

en-US

00:41:34

BI 014 Konrad Kording: Regulators, Mount Up!

Brain Inspired ·

2018/10/18

Show Notes: Follow Konrad on Twitter: @KordingLab. Konrad’s lab website. The paper we discuss: Bioscience-scale automated detection of figure element reuse.

en-US

00:52:27

BI 013 Dileep George: Vicarious Robot AI

Brain Inspired ·

2018/10/11

Dileep’s homepage. Dileep on Twitter: @dileeplearning Vicarious, the general AI robotics company Dileep cofounded. Vicarious on Twitter: @vicariousai. The papers we discuss: A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs. Cortical Microcircuits from a Generative Vision Model Probabilistic graphical models. Hierarchical temporal memory. HTM School on Youtube. Dileep’s earlier paper with Jeff Hawkins of Numenta:Towards a

en-US

01:06:25

BI 012 Niko Kriegeskorte: Black Box, White Box

Brain Inspired ·

2018/10/05

Mentioned in the show: Follow Niko on twitter @KriegeskorteLab. Visit his lab website. The Cognitive Computational Neuroscience Conference. The review papers we base the conversation on: Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing. Cognitive computational neuroscience. Deep Neural Networks in Computational Neuroscience. Want a thorough breakdown of recent research articles? Try his blog. Not mentioned in the show, but check

en-US

00:50:30

BI 011 Grace Lindsay: Visual Attention in CNNs

Brain Inspired ·

2018/09/27

Mentioned in the show: The paper we discuss:How biological attention mechanisms improve task performance in a large-scale visual system model. Follow Grace on Twitter: @neurograce. She co-hosts the AI/Neuroscience/Science podcast, Unsupervised Thinking. Her excellent blog Neurdiness. The CNN blog post I highly recommend for a deeper dive:Deep Convolutional Neural Networks as Models of the Visual System: Q&A. Or listen to my interview with Dan Yamins. The podcast that inspired

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01:04:46

BI 010 Adam Marblestone: Brain Cost Functions

Brain Inspired ·

2018/09/20

Mentioned in the show Adam’s Website. Follow him on Twitter. He made Technology Review’s 35 Innovators Under 35. The paper we discuss: Toward an Integration of DeepLearning and Neuroscience Some of the peripheral things we discussed: Cortical microcircuits: here’s an example paper. Autoencodersare unsupervised learning methods to reconstruct their input. A nice introduction to Generative Adversarial Networks. Marvin Minsky’s classicSociety of Mind. Some of his talks

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01:11:00

BI 009 Blake Richards: Deep Learning in the Brain

Brain Inspired ·

2018/09/13

Mentioned in the show Follow Blake on twitter: @tyrell_turing Blake’s Learning in Neural Circuits (LiNC) Laboratory. He’s a Fellow with the Learning in Machines and Brains Program of the Canadian Institute for Advanced Research (CIFAR). The paper we discuss: Towards Deep Learning With Segregated Dendrites. Code to run the model on Github. If you’d rather watch a talk, here’s the same topic in a great talk by Blake. The idea of approaching neuroscience from the perspective there are

en-US

00:53:34

BI 008 Joshua Glaser: Supervised ML for Neuroscience

Brain Inspired ·

2018/09/07

Mentioned in the show The two papers we discuss: The Roles of Supervised Machine Learning in Systems Neuroscience Machine learning for neural decoding Kording lab, where Josh did his PhD work

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01:01:48

BI 007 Daniel Yamins: Infant AI and CNNs

Brain Inspired ·

2018/09/02

Mentioned in the show: Dan’s Stanford Neuroscience and Artificial Intelligence Laboratory: The 2 papers we discuss Performance-optimized hierarchical models predict neural responses in higher visual cortex Learning to Play with Intrinsically-Motivated Self-Aware Agents ImageNetas one of the most important things to stimulate research in AI, developed by these folks. Ventral visual stream (as opposed to the Dorsal stream). Retinotopy Convolutional neural networks were inspired

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00:37:16

BI 006 Ryan Poplin: Deep Solutions

Brain Inspired ·

2018/08/25

[bctt tweet=”Check out episode 6 of the Brain Inspired podcast: Deep learning, eyeballs, and brains” username=”pgmid”] Mentioned in the show Ryan Poplin What is a convolutional neural network? Here’s a good summary. Here’s another good summary. Deep learning on eye images: CV risk factors can be predicted from retinal fundus images DeepVariant: Deep learning on genomics The paper:Creating a universal SNP and small indel variant caller with deep neural

en-US

00:45:39

BI 005 David Sussillo: RNNs are Back!

Brain Inspired ·

2018/08/02

Mentioned in the show: David’s Twitter account Papers we discuss: Sussillo, D.S. & Abbott, L. F. (2009). Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron 63(4). Sussillo, D. (2014) Neural circuits as computational dynamical systems.Current Opinion in Neurobiology25:156-63. Sussillo, D. & Barak, O. (2013). Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks.Neural Computation25(3). Sussillo, D., Jozefowicz, R.,

en-US

00:41:39

BI 004 Mark Humphries: Learning to Remember

Brain Inspired ·

2018/08/02

Mentioned in the show: Mark’s lab The excellent blog he writes on Medium The paper we discuss: An ensemble code in medial prefrontal cortex links prior events to outcomes during learning The code to replicate their findings Dynamical networks: Finding, measuring, and tracking neural population activity using network science

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00:42:47

BI 003 Blake Porter: Effortful Rats

Brain Inspired ·

2018/08/02

Mentioned during the show: The paper we discuss: Hippocampal place cell encoding of sloping terrain. Blake’s website, where he writes his blog Noise mystery story Deepmind released a package to design your own bots for Starcraft OpenAI and Dota2:Blake says they hadn’t beat pro teams, but we agreed it’s inevitable. And now they have…

en-US

01:11:23

BI 002 Steven Potter Part 2: Brains in Dishes

Brain Inspired ·

2018/08/02

Find out more about Steve at his website. Things mentioned during the show: Papers we talked about: Publishing negative results! Wagenaar, D. A., Pine, J., & Potter, S. M. (2006). Searching for plasticity in dissociated cortical cultures on multi-electrode arrays. Journal of Negative Results in BioMedicine 5:16.Download Solving the bursting neurons problem: Wagenaar, D. A. Madhavan, R. Pine, J. and Potter, S. M. (2005) Controlling bursting in cortical cultures with closed-loop

en-US

00:41:58

BI 001 Steven Potter: Brains in Dishes

Brain Inspired ·

2018/08/02

Find out more about Steve at hiswebsite. I discovered him when I found his book chapter “What Can AI Get from Neuroscience?” in the following: “50 Years of Artificial Intelligence: Essays Dedicated to the 50th Anniversary of Artificial Intelligence,” M. Lungarella, J. Bongard, & R. Pfeifer (eds.) (pp. 174-185). Berlin: Springer-Verlag.Download the chapter.Link tothe whole bookat Springer. These days Steve is semi-retired, but is an active consultant for high-tech startups, companies,

en-US

235 results

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