Kimberly Stachenfeld

This paper introduces DataDIVER, an automated method for discovering computational models from data, which reveals novel insights into human and animal learning.

This study shows how rapid learning in mice involves structured changes in motor cortex connectivity, rerouting preparatory activity to control a conditioned neuron.

The paper develops a factorized model of temporal abstraction to study how abstract knowledge is learned and represented in the brain during a decision task, showing that hippocampus, entorhinal cortex, and orbitofrontal cortex support this process.

LLM-driven evolutionary program synthesis (CogFunSearch) discovers interpretable Python cognitive models that predict bandit-task choices of humans, rats and fruit flies better than leading hand-designed models.

SIMPL optimizes neural representations of behaviour by iteratively refining latent variables and tuning curves, revealing more precise spatial encoding than traditional methods.

RatInABox generates synthetic locomotion and neural data for studying spatial navigation models in rodents.

This paper demonstrates that spike-timing dependent plasticity (STDP) on theta sweeps can rapidly learn a successor representation, explaining hippocampal place cell properties and spatial prediction mechanisms.

This paper introduces a biologically plausible generative model of the hippocampal formation that uses theta-band oscillations to train a network for spatial and non-spatial memory tasks.

This paper introduces HTFA, a Bayesian hierarchical matrix factorization model, to discover dynamic full-brain functional connectivity patterns from fMRI data and shows it can decode cognitive states during storytelling and TV watching.

This paper proposes that the hippocampus uses a predictive representation for spatial navigation, challenging the traditional cognitive map metaphor and suggesting grid cells support hierarchical planning.