Shared spatiotemporal motifs model brain spikes across tissue types
A discrete generative model of neuronal spiking activity on microelectrode arrays
Machine Learning
Summary
Understanding how neurons fire is important for studying brain activity and diseases. This paper presents a way to model neuron spikes recorded by special electrodes, capturing these patterns as reusable motifs that appear across different brain tissues. The authors’ model learns these motifs and predicts where and when they occur, improving the accuracy of recreating and generating neuronal activity patterns. This approach works well on various human brain samples without needing to customize for each experiment.
What this means in practice
- •For neurotechnology developers: Build tools to simulate and analyze neuron spike patterns from dense electrode recordings using a compact motif-based representation.
- •For pharmaceutical data teams: Use consistent spike motifs to compare drug effects on neural tissue activity across multiple experimental conditions without extra customization.
Authors
Md Sayed Tanveer, Mohammed A. Mostajo-Radji, Ge Wang
Abstract
Generative models of neural activity could help characterize tissue dynamics, compare experimental conditions, and simulate population activity for applications ranging from disease and drug-response studies to closed-loop experimentation. Existing approaches, however, typically assume a fixed set of sorted neurons, whereas high-density microelectrode arrays produce extremely sparse, array-wide binary spike volumes in which the observed subset of electrodes varies across assays. We introduce a discrete generative model that represents this activity using a shared vocabulary of spatiotemporal motifs. A residual vector-quantized autoencoder learns the motif vocabulary, while a factorized masked transformer predicts where activity occurs and which motif appears at each active location. We evaluate the model on 31 assays spanning human brain organoids and acute \emph{ex vivo} human hippocampal tissue. The learned motifs are broadly reused: assay identity explains only $9%$ of the entropy in motif use, and motif overlap across tissue types is comparable to overlap within them. When representation quality is evaluated independently of the generative prior, our approach achieves $5.2\times$ the voxel-level reconstruction average precision of a matched flat tokenizer. For masked completion and free generation, the full model achieves $1.4$--$2.6\times$ the site-level average precision of the matched generative baseline and outperforms it across all four families of generation metrics. These results establish a compact, reusable representation for array-wide spiking activity without learned assay-specific parameters, providing a scalable foundation for generative modeling across diverse neural preparations.