Core brain functions—perception, attention, decision-making—emerge from complex patterns of neural activity coordinated within local microcircuits and across brain regions, with dynamics down to milliseconds. Recently, massively-parallel technologies enabled activity recordings from many neurons simultaneously, offering the opportunity to investigate how activity is orchestrated across neural populations to drive behavior. To reveal dynamic features in these large-scale datasets, computational methods are needed that can uncover neural population dynamics and identify how individual neurons contribute to the population activity. Existing methods rely on fitting ad hoc parametric models to data, which often leads to ambiguous model comparisons and estimation biases, limiting the potential of these methods for scientific discovery. To push these limits, our BRAIN project team develops a broadly applicable, non-parametric inference framework for discovering population dynamics directly from the data without a priori model assumptions. Our non-parametric methods explore the entire space of all possible dynamics in search of the model consistent with the data, leading to a conceptual shift from model fitting to model discovery. This is achieved by extending latent dynamical models to a general form, where the latent dynamics are governed by arbitrary dynamical-systems equations, in which driving forces are directly optimized. Our framework reconstructs population dynamics with millisecond precision on single trials and infers idiosyncratic relationships between single-neuron firing-rates and the population dynamics, revealing heterogeneous contributions of single neurons to circuit-level computations. With our methods, we examine large-scale physiological recordings during decision-making, to reveal how neural activity is coordinated to drive decisions and how functional heterogeneity of single-neuron responses aligns with anatomical organization of decision-making circuits.
A python package is available on GitHub: https://github.com/engellab/neuralflow