LatenZy, non-parametric, binning-free estimation of latencies from neural spiking data

Abstract

Precisely estimating the onset of neural spiking responses and the timing at which activity begins to diverge between conditions is crucial for understanding temporal dynamics in brain information processing. Conventional methods require arbitrary parameter choices such as bin widths and response thresholds, limiting reproducibility and comparability. Here, we present latenZy and latenZy2, two non-parametric, binning-free methods that directly analyze spike times using cumulative statistics and iterative refinement, without assumptions about response shape. LatenZy estimates neuronal response onset latency, while latenZy2 detects when spiking activity diverges between conditions. We validate these methods on electrophysiological datasets from mouse and macaque visual cortex, and show that they outperform standard approaches in precision, robustness, sensitivity, and statistical power. LatenZy captures contrast-dependent latency shifts and hierarchical timing across visual areas, and latenZy2 reveals earlier attentional modulation in higher visual cortex consistent with top-down feedback. Together, they offer scalable, parameter-free tools for reliable latency estimation in large-scale neural recordings. Open-source implementations are available in Python and MATLAB.