Amy L. Orsborn

Hao Fang, Ryan A. Canfield, Tomohiro Ouchi et al.

Brain decoding aims to interpret and translate neural activity into behaviors. As such, it is imperative that decoding models are able to generalize across variations, such as recordings from different brain sites, distinct sessions, different types of behavior, and a variety of subjects. Current models can only partially address these challenges and warrant the development of pretrained neural transformer models capable to adapt and generalize. In this work, we propose RPNT - Robust Pretrained Neural Transformer, designed to achieve robust generalization through pretraining, which in turn enables effective finetuning given a downstream task. In particular, RPNT unique components include 1) Multidimensional rotary positional embedding (MRoPE) to aggregate experimental metadata such as site coordinates, session name and behavior types; 2) Context-based attention mechanism via convolution kernels operating on global attention to learn local temporal structures for handling non-stationarity of neural population activity; 3) Robust self-supervised learning (SSL) objective with uniform causal masking strategies and contrastive representations. We pretrained two separate versions of RPNT on distinct datasets a) Multi-session, multi-task, and multi-subject microelectrode benchmark; b) Multi-site recordings using high-density Neuropixel 1.0 probes. The datasets include recordings from the dorsal premotor cortex (PMd) and from the primary motor cortex (M1) regions of nonhuman primates (NHPs) as they performed reaching tasks. After pretraining, we evaluated the generalization of RPNT in cross-session, cross-type, cross-subject, and cross-site downstream behavior decoding tasks. Our results show that RPNT consistently achieves and surpasses the decoding performance of existing decoding models in all tasks.

Trung Le, Hao Fang, Jingyuan Li et al.

Intracortical Brain-Computer Interfaces (iBCI) aim to decode behavior from neural population activity, enabling individuals with motor impairments to regain motor functions and communication abilities. A key challenge in long-term iBCI is the nonstationarity of neural recordings, where the composition and tuning profiles of the recorded populations are unstable across recording sessions. Existing methods attempt to address this issue by explicit alignment techniques; however, they rely on fixed neural identities and require test-time labels or parameter updates, limiting their generalization across sessions and imposing additional computational burden during deployment. In this work, we introduce SPINT - a Spatial Permutation-Invariant Neural Transformer framework for behavioral decoding that operates directly on unordered sets of neural units. Central to our approach is a novel context-dependent positional embedding scheme that dynamically infers unit-specific identities, enabling flexible generalization across recording sessions. SPINT supports inference on variable-size populations and allows few-shot, gradient-free adaptation using a small amount of unlabeled data from the test session. To further promote model robustness to population variability, we introduce dynamic channel dropout, a regularization method for iBCI that simulates shifts in population composition during training. We evaluate SPINT on three multi-session datasets from the FALCON Benchmark, covering continuous motor decoding tasks in human and non-human primates. SPINT demonstrates robust cross-session generalization, outperforming existing zero-shot and few-shot unsupervised baselines while eliminating the need for test-time alignment and fine-tuning. Our work contributes an initial step toward a robust and scalable neural decoding framework for long-term iBCI applications.