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Brain Inspired
Brain Inspired episodes
- BI 010 Adam Marblestone: Brain Cost FunctionsMentioned in the showAdam’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 NeuroscienceSome of the peripheral things we discussed:Cortical microcircuits: here’s an example paper.Autoencoders are unsupervised learning methods to reconstruct their input.A nice introduction to Generative Adversarial Networks.Marvin Minsky’s classic Society of Mind.Some of his talks online:A Billion-Year-Old Information Technology.What sets the exponent of neuroscience progress?1 hr 5 min
- BI 009 Blake Richards: Deep Learning in the BrainMentioned in the showFollow Blake on twitter: @tyrell_turingBlake’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 general principles of computation applicable to both brains and AI:Geoffrey Hinton.Cybernetics.McCullough and Pitts artificial neuron: Their original paper and a nice tutorial.Frank Rosenblaut.Demis Hassabis, who founded Deepmind, wrote a great review of how AI and neuroscience can work together.Donald Hebb of the famed Hebbian Learning in his famous book The Organization of Behavior: A Neuropsychological Theory.Konrad Kording’s 2001 paper articulating the same idea we discuss: Supervised and Unsupervised Learning with Two Sites of Synaptic Integration.MNIST dataset of handwritten digits – used to train and test a lot of machine learning networks.Eliminative Materialism, the idea our common sense conception of the mind is false.1 hr 11 min
- BI 009 Blake Richards: Deep Learning in the Brain
Mentioned in the show
Follow Blake on twitter: @tyrell_turingBlake’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 general principles of computation applicable to both brains and AI:Geoffrey Hinton.Cybernetics.McCullough and Pitts artificial neuron: Their original paper and a nice tutorial.Frank Rosenblaut.Demis Hassabis, who founded Deepmind, wrote a great review of how AI and neuroscience can work together.- Donald Hebb of the famed Hebbian Learning in his famous book The Organization of Behavior: A Neuropsychological Theory.
Konrad Kording’s 2001 paper articulating the same idea we discuss: Supervised and Unsupervised Learning with Two Sites of Synaptic Integration.- MNIST dataset of handwritten digits – used to train and test a lot of machine learning networks.
Eliminative Materialism, the idea our common sense conception of the mind is false.1 hr 11 min - BI 008 Joshua Glaser: Supervised ML for NeuroscienceMentioned in the showThe two papers we discuss:The Roles of Supervised Machine Learning in Systems NeuroscienceMachine learning for neural decodingKording lab, where Josh did his PhD work54 min
- BI 008 Joshua Glaser: Supervised ML for NeuroscienceMentioned in the showThe two papers we discuss:The Roles of Supervised Machine Learning in Systems NeuroscienceMachine learning for neural decodingKording lab, where Josh did his PhD work54 min
- BI 007 Daniel Yamins: Infant AI and CNNsMentioned in the show:Dan’s Stanford Neuroscience and Artificial Intelligence Laboratory:The 2 papers we discussPerformance-optimized hierarchical models predict neural responses in higher visual cortexLearning to Play with Intrinsically-Motivated Self-Aware AgentsImageNet as one of the most important things to stimulate research in AI, developed by these folks.Ventral visual stream (as opposed to the Dorsal stream).RetinotopyConvolutional neural networks were inspired by Kunihiko Fukushima’s Neocognitron2 modern of approaches to solve the ImageNet database:Google’s NASNet architecture, with about 12 layersMicrosoft’s super deep ResNet.Object permanence and some video examplesThe distinction between the intrinsic motivation of Dan and colleagues’ AI agent and the reinforcement learning motivation of the OpenAI 5 team1 hr 2 min
- BI 007 Daniel Yamins: Infant AI and CNNs
Mentioned in the show:
Dan’s Stanford Neuroscience and Artificial Intelligence Laboratory:The 2 papers we discussPerformance-optimized hierarchical models predict neural responses in higher visual cortexLearning to Play with Intrinsically-Motivated Self-Aware AgentsImageNet as one of the most important things to stimulate research in AI, developed by these folks.Ventral visual stream (as opposed to the Dorsal stream).RetinotopyConvolutional neural networks were inspired by Kunihiko Fukushima’s Neocognitron2 modern of approaches to solve the ImageNet database:Google’s NASNet architecture, with about 12 layersMicrosoft’s super deep ResNet.Object permanence and some video examplesThe distinction between the intrinsic motivation of Dan and colleagues’ AI agent and the reinforcement learning motivation of the OpenAI 5 team1 hr 2 min - BI 006 Ryan Poplin: Deep SolutionsCheck out episode 6 of the Brain Inspired podcast: Deep learning, eyeballs, and brainsClick To TweetMentioned in the showRyan PoplinWhat 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 imagesDeepVariant: Deep learning on genomicsThe paper: Creating a universal SNP and small indel variant caller with deep neural networksA nice, less technical summary of the workThe code on Github38 min
- BI 006 Ryan Poplin: Deep Solutions[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 networks
- A nice, less technical summary of the work
- The code on Github
38 min - BI 005 David Sussillo: RNNs are Back!David and I cover recurrent neural networks (RNNs), his work using RNNs to study motor brain processes, how dynamical systems theory is a useful approach to brains and AI, and more. Click the episode for the show notes.46 min
About Brain Inspired
From the publisher's feed
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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