Bubblewrap: Online tiling and real-time flow prediction on neural manifolds

TL;DR

This paper introduces Bubblewrap, a scalable online method for real-time neural population dynamics analysis that combines dimensionality reduction with probabilistic tiling of neural manifolds, enabling fast, accurate predictions in noisy regimes.

Contribution

Bubblewrap is the first method to efficiently combine online manifold tiling with probabilistic modeling for neural dynamics, scalable to thousands of channels and suitable for closed-loop experiments.

Findings

Outperforms existing methods in noise-dominated regimes

Scales to tens of thousands of tiles

Operates at kiloHertz data rates and predicts submillisecond dynamics

Abstract

While most classic studies of function in experimental neuroscience have focused on the coding properties of individual neurons, recent developments in recording technologies have resulted in an increasing emphasis on the dynamics of neural populations. This has given rise to a wide variety of models for analyzing population activity in relation to experimental variables, but direct testing of many neural population hypotheses requires intervening in the system based on current neural state, necessitating models capable of inferring neural state online. Existing approaches, primarily based on dynamical systems, require strong parametric assumptions that are easily violated in the noise-dominated regime and do not scale well to the thousands of data channels in modern experiments. To address this problem, we propose a method that combines fast, stable dimensionality reduction with a soft tiling of the resulting neural manifold, allowing dynamics to be approximated as a probability flow between tiles. This method can be fit efficiently using online expectation maximization, scales to tens of thousands of tiles, and outperforms existing methods when dynamics are noise-dominated or feature multi-modal transition probabilities. The resulting model can be trained at kiloHertz data rates, produces accurate approximations of neural dynamics within minutes, and generates predictions on submillisecond time scales. It retains predictive performance throughout many time steps into the future and is fast enough to serve as a component of closed-loop causal experiments.