David M. Zoltowski

The paper develops improved models for inferring latent neural states from calcium imaging data by integrating spiking and calcium dynamics directly into latent variable frameworks.

The paper develops a model integrating time and reward to explain foraging decisions and identifies frontal cortex ramping dynamics as a mechanism for solving patch foraging problems.

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

This paper introduces a neuromodulated RNN model that uses synaptic gain scaling to improve training and generalization on canonical tasks, and shows how it implements gating mechanisms found in artificial RNNs.

This paper introduces a probabilistic model to analyze multi-region neural dynamics and communication, showing it outperforms existing models on mouse neural datasets.

The paper compares discrete stepping and nonlinear ramping dynamics in LIP neuron spiking responses during decision-making and finds that stepping dynamics better describe most neurons, though an extended ramping model accounts for a similar fraction.