Publications

A neuronal least-action principle for real-time learning in cortical circuits

Walter Senn

Dominik Dold

Akos F. Kungl

Benjamin Ellenberger

Jakob Jordan

Yoshua Bengio

João Sacramento

Mihai A. Petrovici

One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal lea… (see more)st-action principle for cortical processing of sensory streams to produce appropriate behavioural outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimize the local somato-dendritic mismatch error within individual neurons. For motor output neurons, it implies minimizing an instantaneous behavioural error. For deep network neurons, it implies a prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory inputs and the motor feedback during the whole sensory-motor trajectory. Ongoing synaptic plasticity reduces the somato-dendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic dynamics for global real-time computation and learning in the brain and in physical substrates in general.

2024-09-23

bioRxiv (preprint)

doi.org

Protein Language Models: Is Scaling Necessary?

Quentin Fournier

Robert M. Vernon

Almer van der Sloot

Benjamin Schulz

Sarath Chandar

Christopher James Langmead

2024-09-23

bioRxiv (preprint)

doi.org

A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models

Stephen R. Pfohl

Heather Cole-Lewis

Rory A Sayres

Darlene Neal

Mercy Nyamewaa Asiedu

Awa Dieng

Nenad Tomašev

Qazi Mamunur Rashid

Shekoofeh Azizi

Negar Rostamzadeh

Liam G. McCoy

L. A. Celi

Yun Liu

Mike Schaekermann

Alanna Walton

Alicia Parrish

Chirag Nagpal

Preeti Singh

Akeiylah Dewitt

P. A. Mansfield … (see 10 more)

Sushant Prakash

Katherine Heller

Alan Karthikesalingam

Christopher Semturs

Joelle Barral

Greg C. Corrado

Yossi Matias

Jamila Smith-Loud

Ivor Horn

Karan Singhal

2024-09-23

Nature Medicine (published)

doi.org

arxiv.org

What Are They Doing? Joint Audio-Speech Co-Reasoning

Yingzhi Wang

Pooneh Mousavi

Artem Ploujnikov

Mirco Ravanelli

2024-09-22

ArXiv (preprint)

doi.org

arxiv.org

AI content detection in the emerging information ecosystem: new obligations for media and tech companies

Alistair Knott

Dino Pedreschi

Toshiya Jitsuzumi

Susan Leavy

D. Eyers

Tapabrata Chakraborti

Andrew Trotman

Sundar Sundareswaran

Ricardo Baeza-Yates

Przemyslaw Biecek

Adrian Weller

Paul D. Teal

Subhadip Basu

Mehmet Haklidir

Virginia Morini

Stuart Russell

Yoshua Bengio

2024-09-21

Ethics and Information Technology (published)

doi.org

ToxiSight: Insights Towards Detected Chat Toxicity

Zachary Yang

Domenico Tullo

Reihaneh Rabbany

We present a comprehensive explainability dashboard designed for in-game chat toxicity. This dashboard integrates various existing explainab… (see more)le AI (XAI) techniques, including token importance analysis, model output visualization, and attribution to the training dataset. It also provides insights through the closest positive and negative examples, facilitating a deeper understanding and potential correction of the training data. Additionally, the dashboard includes word sense analysis—particularly useful for new moderators—and offers free-text explanations for both positive and negative predictions. This multi-faceted approach enhances the interpretability and transparency of toxicity detection models.

2024-09-21

EMNLP/2024/Workshop/BlackBoxNLP (accepted)

openreview.net