Bibliography:

This is a simple and abbreviated list of the main scientific papers and/or books discussed in each episode. For more links, papers, and information, click on the episode title.

 

 

BI 001 & 002 Steven Potter: Brains in Dishes

• What Can AI Get from Neuroscience?
• Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation.

BI 003 Blake Porter: Effortful Rats

• Hippocampal place cell encoding of sloping terrain.

BI 004 Mark Humphries: Learning to Remember

• An ensemble code in medial prefrontal cortex links prior events to outcomes during learning.

BI 005 David Sussillo: RNNs are Back!

• Neural circuits as computational dynamical systems.
• Opening the Black Box: Low-dimensional dynamics in high-dimensional recurrent neural networks.
• LFADS – Latent Factor Analysis via Dynamical Systems.

BI 006 Ryan Poplin: Deep Solutions

• CV risk factors can be predicted from retinal fundus images.
• Creating a universal SNP and small indel variant caller with deep neural networks.

BI 007 Daniel Yamins: Infant AI and CNNs

• Performance-optimized hierarchical models predict neural responses in higher visual cortex.
• Learning to Play with Intrinsically-Motivated Self-Aware Agents

BI 008 Joshua Glaser: Supervised ML for Neuroscience

• The Roles of Supervised Machine Learning in Systems Neuroscience.
• Machine learning for neural decoding.

BI 009 Blake Richards: Deep Learning in the Brain

• Towards Deep Learning With Segregated Dendrites.

BI 010 Adam Marblestone: Brain Cost Functions

• Toward an Integration of DeepLearning and Neuroscience.

BI 011 Grace Lindsay: Visual Attention in CNNs

• How biological attention mechanisms improve task performance in a large-scale visual system model..

BI 012 Niko Kriegeskorte: Black Box, White Box

• Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.
• Cognitive computational neuroscience.
• Deep Neural Networks in Computational Neuroscience.

BI 013 Dileep George: Vicarious Robot AI

• A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs.
• Cortical Microcircuits from a Generative Vision Model.

BI 014 Konrad Kording: Regulators, Mount Up!

• Bioscience-scale automated detection of figure element reuse.

BI 015 Terrence Sejnowski: How to Start a Deep Learning Revolution

• The Deep Learning Revolution.

BI 016 Ryota Kanai: Artificial Consciousness

• A unified strategy for implementing curiosity and empowerment driven reinforcement learning.

BI 017 Jeff Hawkins: Location, Location, Location

• A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex.

BI 018 Dean Buonomano: Time in Brains and AI

• The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions.

BI 019 Julie Grollier: Spintronic Neuromorphic Nano-Oscillators!

• Vowel recognition with four coupled spin-torque nano-oscillators.
• Neuromorphic computing with nanoscale spintronic oscillators.
• Spintronic Nanodevices for Bioinspired Computing.

BI 020 Anna Wexler: Stimulate Your Brain?

• Recurrent themes in the history of the home use of electrical stimulation: Transcranial direct current stimulation (tDCS) and the medical battery (1870–1920).
• The Social Context of “Do-It-Yourself” Brain Stimulation: Neurohackers, Biohackers, and Lifehackers.
• Who Uses Direct-to-Consumer Brain Stimulation Products, and Why? A Study of Home Users of tDCS Devices.
• Mind-Reading or Misleading? Assessing Direct-to-Consumer Electroencephalography (EEG) Devices Marketed for Wellness and Their Ethical and Regulatory Implications.

BI 021 Matt Botvinick: Neuroscience and AI at DeepMind

• Learning to reinforcement learn.
• Prefrontal cortex as a meta-reinforcement learning system.
• Machine Theory of Mind.

BI 022 Melanie Mitchell: Complexity, and AI Shortcomings

• Complexity: A Guided Tour.

BI 023 Marcel van Gerven: Mind Decoding with GANs

• Generative adversarial networks for reconstructing natural images from brain activity.

BI 024 Tim Behrens: Cognitive Maps

• What is a cognitive map? 
• Organizing conceptual knowledge in humans with a grid-like code.

BI 025 John Krakauer: Understanding Cognition

• Neuroscience Needs Behavior: Correcting a Reductionist Bias.

BI 026 Kendrick Kay: A Model By Any Other Name

• Bottom-up and top-down computations in word- and face-selective cortex.
• Principles for models of neural information processing.
• Appreciating diversity of goals in computational neuroscience.

BI 027 Ioana Marinescu & Konrad Kording: Causality in Quasi-Experiments

• Quasi-experimental causality in neuroscience and behavioral research

BI 028 Sam Gershman: Free Energy Principle & Human Machines

• What does the free energy principle tell us about the brain?
• Building machines that learn and think like people.

BI 029 Paul Humphreys & Zac Irving: Emergence & Mind Wandering

• Emergence: A Philosophical Account. (book by Paul)
• The Oxford Handbook of Philosophy of Science.
• Mind-Wandering is Unguided Attention.
• The Philosophy of Mind-Wandering.
• The Neuroscience of Spontaneous Thought

BI 030 Jay McClelland: Mathematical Reasoning and PDP

• Parallel Distributed Processing by Rumelhart and McClelland.
• Complimentary Learning Systems Theory and Its Recent Update.

BI 031 Francisco de Sousa Webber: Natural Language Understanding

• Semantic Folding Theory And its Application in Semantic Fingerprinting.

BI 032 Rafal Bogacz: Back-Propagation in Brains

• Theories of Error Back-Propagation in the Brain.
• An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity.
• A tutorial on the free-energy framework for modelling perception and learning.

BI 033 Federico Turkheimer: Weak Versus Strong Emergence

• Conflicting emergences. Weak vs. strong emergence for the modelling of brain function.
• From homeostasis to behavior: balanced activity in an exploration of embodied dynamic environmental-neural interaction.

BI 034 Tony Zador: How DNA and Evolution Can Inform AI

• A Critique of Pure Learning: What Artificial Neural Networks can Learn from Animal Brains.

BI 035 Tim Behrens: Abstracting & Generalizing Knowledge, & Human Replay

• Generalisation of structural knowledge in the hippocampal-entorhinal system.
• Inference in replay through factorized representations.

BI 036 Roshan Cools: Cognitive Control and Dopamine

• The costs and benefits of brain dopamine for cognitive control.

BI 037 Nathaniel Daw: Thinking the Right Thoughts

• Prioritized memory access explains planning and hippocampal replay.

BI 038 Máté Lengyel: Probabilistic Perception and Learning

• Statistically optimal perception and learning: from behavior to neural representations.