The geometry of abstraction in artificial and biological neural networks

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. We characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility

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Speed trick
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The repository of the course in GitHub.

There's a recent @Radiolab mini-series that I highly recommend, especially to scientists (regardless of your area of study). The series is called "G" and focuses on intelligence research. I'll preview the episodes then explain why I think you (scientists) should listen.

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The 1st episode is noscriptd "The Miseducation of Larry P" and chronicles the history of IQ testing in education & how poorly normed tests ended up...well...screwing over minority and low SES students. If you care about equity, this one's a tearjerker.

Link: wnystudios
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https://pedramardakani.wordpress.com/android-ninja-1

@ashoftegah #blog #tips

I am an applied mathematician in the Department of Mathematics at the Vrije Universiteit Amsterdam, and a member of Inria's MathNeuro Team.
I work on spatio-temporal patterns in biological and physical models, which I study using numerical and analytical methods.
My research interests include: numerical bifurcation analysis, mathematical neuroscience, multi-scale dynamics, numerical methods, localised states, coherent structures, nonlinear media, reaction-diffusion systems, and nonlocal models.

Page: Daniele Avitabile

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ML4Sci is a weekly newsletter highlighting applications of artificial intelligence and machine learning to scientific and engineering problems.
In this newsletter, I’m exploring machine learning for science (ML4Sci) and how rapid advances in machine learning, most prominently in deep learning, are revolutionizing the way we do science. If you’re a scientist who wants to learn more about how the deep learning revolution can help you in the lab, a machine learning expert who wants to work on something more substantive than chat-bots or self-driving cars, or just someone who wants to learn more about how AI will change our world, this is the newsletter for you!

Link: ML4Sci

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Many people have been in touch with questions about how to pursue further education or careers in computational neuroscience. With the BRAIN initiative in the news and a number of companies launching into neural technologies, this is certainly an area of current opportunity. I wanted to give some perspective on possible trajectories, as the field is very diverse, spanning academic study in quantitative approaches to systems neurophysiology to robotics and industrial engineering.

Link: fairhulllab

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Join us for the FENS 2020 Virtual Forum, where scientists will present the newest scientific and technological advances in understanding the various nervous systems, and where you'll have the opportunity to meet, chat and connect!

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Link: Medium
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