Publications (10)
Grand Challenges for Global Brain Sciences
Joshua T. Vogelstein, Katrin Amunts, Andreas Andreou +59
The next grand challenges for society and science are in the brain sciences. A collection of 60+ scientists from around the world, together with 10+ observers from national, privat…
Compiling molecular ultrastructure into neural dynamics
Konrad P. Kording, Anton Arkhipov, Davy Deng +22
High-resolution brain imaging can now capture not just synapse locations but their molecular composition, with the cost of such mapping falling exponentially. Yet such ultrastructu…
Applied Astrobiology: An Integrated Approach to the Future of Life in Space
Robin Wordsworth, Collin Cherubim, Shannon Nangle +13
Searching for extraterrestrial life and supporting human life in space are traditionally regarded as separate challenges. However, there are significant benefits to an approach tha…
Gaussian Gated Linear Networks
David Budden, Adam Marblestone, Eren Sezener +3
We propose the Gaussian Gated Linear Network (G-GLN), an extension to the recently proposed GLN family of deep neural networks. Instead of using backpropagation to learn features,…
Signal-to-pump back-action and self-oscillation in Double-Pump Josephson Parametric Amplifier
Archana Kamal, Adam Marblestone, Michel Devoret
We present the theory of a Josephson parametric amplifier employing two pump sources. Our calculations are based on Input-Output Theory, and can easily be generalized to any couple…
Towards an integration of deep learning and neuroscience
Adam Marblestone, Greg Wayne, Konrad Kording
Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend…
Product Kanerva Machines: Factorized Bayesian Memory
Adam Marblestone, Yan Wu, Greg Wayne
An ideal cognitively-inspired memory system would compress and organize incoming items. The Kanerva Machine (Wu et al, 2018) is a Bayesian model that naturally implements online me…
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Anthony Zador, Sean Escola, Blake Richards +24
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in…
NeuroAI for AI Safety
Patrick Mineault, Niccolò Zanichelli, Joanne Zichen Peng +12
As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general…
The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions
Gal Haspel, Ben Baker, Isabel Beets +34
Just like electrical engineers understand how microprocessors execute programs in terms of how transistor currents are affected by their inputs, neuroscientists want to understand…