Owen Traubert | Searcharxiv

arXiv2026

Optimal photostimulation selection for iterative activity maps

All-optical two-photon holographic optogenetics enables causal circuit mapping by stimulating defined neurons or ensembles while imaging population activity. Yet exhaustive connectivity mapping remains experimentally prohibitive because of combinatorial complexity, tissue heating, photodamage, and experimental time. We present OPhELIA (Optimal Photostimulation sElection for Iterative Activity maps), a Bayesian framework for selecting informative perturbations under limited trial budgets. OPhELIA combines Beta-Bernoulli connectivity inference with an ambiguity-based acquisition heuristic and learned priors derived from pre-stimulation neural activity, augmenting active learning and compressed sensing. In standalone simulations and in vivo larval zebrafish visuomotor experiments, OPhELIA with active learning improves trial-efficient approximation of exhaustive functional connectomes. In combinatorial in vivo experiments, OPhELIA with compressed sensing most closely recovers an exhaustive connectome using only 5% of trials. These results establish OPhELIA as a sample-efficient framework for causal connectomics.

q-bio.NC↗

arXiv2025

NLP4Neuro: Sequence-to-sequence learning for neural population decoding

Delineating how animal behavior arises from neural activity is a foundational goal of neuroscience. However, as the computations underlying behavior unfold in networks of thousands of individual neurons across the entire brain, this presents challenges for investigating neural roles and computational mechanisms in large, densely wired mammalian brains during behavior. Transformers, the backbones of modern large language models (LLMs), have become powerful tools for neural decoding from smaller neural populations. These modern LLMs have benefited from extensive pre-training, and their sequence-to-sequence learning has been shown to generalize to novel tasks and data modalities, which may also confer advantages for neural decoding from larger, brain-wide activity recordings. Here, we present a systematic evaluation of off-the-shelf LLMs to decode behavior from brain-wide populations, termed NLP4Neuro, which we used to test LLMs on simultaneous calcium imaging and behavior recordings in larval zebrafish exposed to visual motion stimuli. Through NLP4Neuro, we found that LLMs become better at neural decoding when they use pre-trained weights learned from textual natural language data. Moreover, we found that a recent mixture-of-experts LLM, DeepSeek Coder-7b, significantly improved behavioral decoding accuracy, predicted tail movements over long timescales, and provided anatomically consistent highly interpretable readouts of neuron salience. NLP4Neuro demonstrates that LLMs are highly capable of informing brain-wide neural circuit dissection.

q-bio.NC↗