标题： NLP4Neuro：神经种群解码的序列到序列学习 显示英文标题

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

摘要： 阐明动物行为如何从神经活动产生是神经科学的基础目标。 然而，由于行为背后的计算在整个大脑的数千个个体神经元网络中展开，这给在行为过程中研究大型、密集连接的哺乳动物大脑中的神经作用和计算机制带来了挑战。 Transformer 是现代大型语言模型（LLMs）的核心，已成为从较小神经种群中解码神经活动的强大工具。 这些现代 LLM 已从广泛的预训练中受益，它们的序列到序列学习已被证明可以推广到新的任务和数据模态，这可能也对从更大范围的脑区活动记录中进行神经解码具有优势。 在这里，我们对现成的 LLM 进行了系统评估，以从全脑种群中解码行为，称为 NLP4Neuro，我们使用它来测试在暴露于视觉运动刺激的幼年斑马鱼中同时进行的钙成像和行为记录。 通过 NLP4Neuro，我们发现 当 LLM 使用从文本自然语言数据中学习的预训练权重时，它们在神经解码方面表现得更好。 此外，我们发现一种最近的专家混合 LLM，DeepSeek Coder-7b，显著提高了行为解码的准确性，能够预测长时间尺度上的尾部运动，并提供了解剖上一致的高可解释性神经元显著性读数。 NLP4Neuro 证明了 LLM 在阐明全脑神经回路方面具有高度能力。 显示英文摘要

摘要： 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.