Laura N. Driscoll

The paper introduces CtDToolkit, a framework providing synthetic datasets and metrics to evaluate and improve neural dynamics models of neural computation.

This paper identifies dynamical motifs in recurrent neural networks that enable flexible multitask computation through modular reuse across different tasks.

The paper investigates how synaptic connectivity in the posterior parietal cortex supports decision-making by identifying opponent inhibition motifs between excitatory and inhibitory neurons.

This paper introduces SCA, an unsupervised method for identifying interpretable latent factors in neural data, and shows its effectiveness across different datasets.

The paper shows that neural population drift in sensorimotor cortex is systematically constrained, allowing for stable task information despite apparent instability.