Aditi Jha

Inferring the evolving objectives that organize animal behavior

This paper develops inverse reinforcement learning to infer evolving objectives from animal behavior and shows it can identify time-varying objectives in mice.

Stiefel Manifold Dynamical Systems for Tracking Representational Drift

This paper introduces SMDS, a model that accounts for representational drift in neural data, outperforming LDS in log-likelihood and requiring fewer latent dimensions.

Benchmarking overlapping community detection methods for applications in human connectomics

The paper benchmarks overlapping community detection methods on human connectomes, finding OSLOM best identifies overlapping structures and reveals hierarchical cortical organization.

Modeling Complex Animal Behavior with Latent State Inverse Reinforcement Learning

This paper introduces dDIRL to model complex animal behavior over time, identifying internal states and reward functions from observed navigation in water-starved mice.

Convergent neural dynamical systems for task control in artificial networks and human brains

This study compares task-switching dynamics between artificial neural networks and human EEG data using state-space modeling, revealing a shared strategy of converging to a neutral task state.

Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems

The paper introduces a cell-type dynamical systems model to better capture neural dynamics by considering distinct cell classes, showing improved accuracy and biological interpretability in decoding behavior and simulating perturbations.