State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of large-scale, cellular-resolution neuronal spiking data into an autoregressive spatiotemporal generation problem. Neuroformer is a multimodal, multitask generative pretrained transformer (GPT) model that is specifically designed to handle the intricacies of data in systems neuroscience. It scales linearly with feature size, can process an arbitrary number of modalities, and is adaptable to downstream tasks, such as predicting behavior. We first trained Neuroformer on simulated datasets, and found that it both accurately predicted simulated neuronal circuit activity, and also intrinsically inferred the underlying neural circuit connectivity, including direction. When pretrained to decode neural responses, the model predicted the behavior of a mouse with only few-shot fine-tuning, suggesting that the model begins learning how to do so directly from the neural representations themselves, without any explicit supervision. We used an ablation study to show that joint training on neuronal responses and behavior boosted performance, highlighting the model's ability to associate behavioral and neural representations in an unsupervised manner. These findings show that Neuroformer can analyze neural datasets and their emergent properties, informing the development of models and hypotheses associated with the brain.

翻译：最先进的系统神经科学实验产生大规模多模态数据，这些数据集需要新的分析工具。受视觉和语言领域大型预训练模型成功的启发，我们将大规模、细胞分辨率的神经元尖峰数据分析重新定义为自回归时空生成问题。Neuroformer是一种专为处理系统神经科学数据复杂性而设计的多模态、多任务生成式预训练Transformer（GPT）模型。该模型随特征规模呈线性扩展，可处理任意数量的模态，并适用于预测行为等下游任务。我们首先在模拟数据集上训练Neuroformer，发现它不仅能准确预测模拟神经元回路活动，还能内在推断底层神经回路连接（包括连接方向）。当预训练用于解码神经反应时，该模型仅需少样本微调即可预测小鼠行为，表明模型开始直接从神经表征本身学习如何完成该任务，无需任何显式监督。通过消融研究，我们证明在神经元反应和行为上的联合训练提升了性能，凸显了模型以无监督方式关联行为表征与神经表征的能力。这些发现表明，Neuroformer能够分析神经数据集及其涌现特性，为与大脑相关的模型和假说发展提供启示。