Scalable inference of functional neural connectivity at submillisecond timescales

TL;DR

This paper introduces scalable, high-temporal-resolution methods for inferring neural connectivity from spike train data, surpassing traditional binned models in accuracy and efficiency, and applicable to large-scale recordings.

Contribution

It develops Monte Carlo and polynomial approximation techniques with orthogonal basis functions for continuous-time neural modeling, improving inference accuracy and scalability.

Findings

Superior accuracy over traditional binned GLMs

Enhanced scalability for large neural datasets

Effective in real hippocampal spike data

Abstract

The Poisson Generalized Linear Model (GLM) is a foundational tool for analyzing neural spike train data. However, standard implementations rely on discretizing spike times into binned count data, limiting temporal resolution and scalability. Here, we develop Monte Carlo (MC) methods and polynomial approximations (PA) to the continuous-time analog of these models, and show them to be advantageous over their discrete-time counterparts. Further, we propose using a set of exponentially scaled Laguerre polynomials as an orthogonal temporal basis, which improves filter identification and yields closed-form integral solutions under the polynomial approximation. Applied to both synthetic and real spike-time data from rodent hippocampus, our methods demonstrate superior accuracy and scalability compared to traditional binned GLMs, enabling functional connectivity inference in large-scale neural recordings that are temporally precise on the order of synaptic dynamical timescales and in agreement with known anatomical properties of hippocampal subregions. We provide open-source implementations of both MC and PA estimators, optimized for GPU acceleration, to facilitate adoption in the neuroscience community.