SpikeDiT: Generative neural system identification at spike-train resolution

In populations of biological neurons, repeated presentations of the same stimulus evoke different spike trains, making neural responses fundamentally a conditional distribution. Yet, neural encoding models typically predict only the stimulus-conditioned mean response, either ignoring trial-to-trial variability or imposing it by an assumed noise distribution. Here, we introduce SpikeDiT: a stimulus-conditioned generative model of population activity that combines a frozen spike tokenizer with joint flow matching to generate spike trains at a native 1 ms resolution. Conditional mean responses, marginal firing statistics, fine temporal structure, and shared noise correlations all emerge from training and sampling from a single model. The framework is modular with respect to the underlying stimulus encoder and makes no a priori distributional assumptions about the form of neural variability. We evaluate the approach on two datasets that differ in sensory modalities, species, and recording technologies. Existing models capture either the conditional mean or trial-to-trial variability well whereas SpikeDiT matches or exceeds the strongest baselines on both and, without retraining, uses observed neurons to improve single-trial prediction of unobserved ones. By modeling population responses directly in spike space with a flexible generative framework, our approach provides a unified and scalable foundation for predicting and generating complete neural population responses.