The Life Sciences Departments in Harvard Faculty of Arts and Sciences seek to recruit a tenure-track professor whose research is at the interface of AI and life sciences, […]

The Life Sciences Departments in Harvard Faculty of Arts and Sciences seek to recruit a tenure-track professor whose research is at the interface of AI and life sciences, from methodological developme...

Animals must generalize from limited experience, yet behavioral experiments in the laboratory setting rarely assess whether or how rapidly they generalize. This contrasts with machine learning systems...

The Marr–Albus framework describes learning in Purkinje cells (PCs) through supervised plasticity associated with complex spikes. However, complex spikes occur with low probability in individual PCs f...

A paper by Kempner Institute researchers has received an Outstanding Paper Honorable Mention at ICML 2026, the 43rd International Conference on Machine Learning, held July 6–11, 2026, in Seoul, South ...

This workshop will convene researchers from artificial intelligence, neuroscience, cognitive science, and related disciplines to examine the principles governing learning and training dynamics across ...

We find that adversarial prompt-injection attacks on large language models can amplify attack success rate from the slow polynomial growth observed without injection to exponential growth with the num...

Large nonlinear recurrent neural networks with random couplings generate high-dimensional, potentially chaotic activity whose structure is of interest in neuroscience, machine learning, ecology, and o...

Why do the smell circuits of flies, mice, and humans look so remarkably alike? A new study from Harvard researchers offers a possible explanation: this shared design may be the […]

Two Kempner Institute community members have received AI2050 Fellowships from Schmidt Sciences, a nonprofit organization aimed at accelerating scientific knowledge and breakthroughs. The AI2050 Progra...

The structure of brain connectivity predicts collective neural activity, with a small number of connectivity features determining activity dimensionality, linking circuit architecture to network-level...

SciPost Journals Publication Detail SciPost Phys. Lect. Notes 105 (2025) Simplified derivations for high-dimensional convex learning problems

Welcome to the OpenReview homepage for NeurIPS 2025 Workshop DBM Findings

How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical phy...

The architecture of early olfactory processing is a striking example of convergent evolution. Typically, a panel of broadly tuned receptors is selectively expressed in sensory neurons (each neuron exp...

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Simons Foundation Launches Collaboration on the Physics of Learning and Neural Computation on Simons Foundation

The Kempner brings leading, early-stage postdoctoral scientists to Harvard to work on projects that advance the fundamental understanding of intelligence.

Simons Foundation Launches Collaboration on the Physics of Learning and Neural Computation on Simons Foundation

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Attention-based architectures are a powerful force in modern AI. In particular, the emergence of in-context learning enables these models to perform tasks far beyond the original next-token prediction...

We introduce in-context denoising, a task that refines the connection between attention-based architectures and dense associative memory (DAM) networks, also known as modern Hopfield networks. Using a...

Transformers have a remarkable ability to learn and execute tasks based on examples provided within the input itself, without explicit prior traini...

JTN - 2025

A major challenge in neuroscience is reconciling idealized theoretical models with complex, heterogeneous experimental data. We address this challenge through the lens of continuous-attractor networks...

A major challenge in neuroscience is reconciling idealized theoretical models with complex, heterogeneous experimental data. We address this challenge through the lens of continuous-attractor networks...