Title: SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
Venue: NeurIPS 2025
Accepted: yes
Keywords: intracortical brain-computer interfaces
Abstract: Intracortical Brain-Computer Interfaces (iBCI) decode behavior from neural population activity to restore motor functions and communication abilities in individuals with motor impairments. A central challenge for long-term iBCI deployment is the nonstationarity of neural recordings, where the composition and tuning profiles of the recorded populations are unstable across recording sessions. Existing approaches 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 address the problem of cross-session nonstationarity in long-term iBCI systems and 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. 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.

SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding

Abstract

Intracortical Brain-Computer Interfaces (iBCI) decode behavior from neural population activity to restore motor functions and communication abilities in individuals with motor impairments. A central challenge for long-term iBCI deployment is the nonstationarity of neural recordings, where the composition and tuning profiles of the recorded populations are unstable across recording sessions. Existing approaches 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 address the problem of cross-session nonstationarity in long-term iBCI systems and introduce SPINT a S patial P ermutationI nvariant N eural T ransformer 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 fewshot, gradient-free adaptation using a small amount of unlabeled data from the test session. 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.

1 Introduction

Motor behavior arises from the complex interplay between interconnected neurons, each possessing distinct functional properties [1]. Deciphering the highly nonlinear mapping from the activity of these neural populations to behavior has been a major focus of intracortical Brain-Computer Interfaces (iBCI), whose applications have enabled individuals with motor impairments to control external devices [2, 3], restore communication abilities through typing [4, 5], handwriting [6, 7], and speech [8, 9, 10, 11, 12].

Despite the remarkable capabilities, iBCI systems suffer from performance degradation over extended periods of time, largely attributed to the nonstationarities of the recorded populations [13]. Sources of

nonstationarities include shifts in electrode position, tissue impedance changes, and neural plasticity [14, 15, 16]. These nonstationarities lead to changes in the number and identity of neural units picked up by recording electrodes over time. Such changes in population composition alter the learned mapping from neural activity to behavior, preventing decoders trained on previous sessions from maintaining robust performance in new sessions. To ensure robust behavior decoding over future recording sessions, one approach has focused on training deep networks using many sessions, attempting to achieve decoders that are robust to cross-session variability [17, 18]. This zero-shot approach requires months of labeled training data, necessitating extensive data collection from the user. While recent methods targeting cross-subject generalization may alleviate some of this burden [19, 20, 21, 22], degradation over long-term use still remains, advocating for the adoption of adaptive methods [14, 23, 24, 25, 26]. These adaptive methods leverage the low-dimensional manifold underlying population activity, which has been shown to preserve a consistent relationship with behavior over long periods of time [14, 27]. Depending on the use of labels at test time, they can be categorized into supervised [21, 28, 22], semi-supervised [7], or unsupervised [24, 25, 29], with varying levels of success and practical utility in real-world iBCI [26].

Despite the variety of technical approaches, these works share a common design philosophy: they adopt a fixed view of the neural population, assigning fixed identities and order for neural units during training. While this treatment achieves high decoding performance on held-in sessions (withinsession generalization), the decoders suffer from out-of-distribution performance degradation when evaluated on held-out sessions with different sizes and unit membership (cross-session generalization). To enable transfer of the pretrained model to novel sessions, explicit alignment procedures with gradient updates to adapt model parameters are necessary, imposing disruptive and costly computation overhead for iBCI users. With these limitations of existing approaches, we advocate for the view that an ideal, universal iBCI decoder should be invariant to the permutation of the neural population by design, and should be able to seamlessly handle inference of a variable-sized, unordered set of neural units with minimal data collected from the new setting.

In this work, we address the problem of cross-session nonstationarity in long-term iBCI systems and introduce SPINT - a permutation-invariant framework that can decode motor behavior from the activity of unordered sets of neural units. We contribute toward an iBCI design that can predict behavior covariates from continuous streams of neural observations and adapt gradient-free to novel sessions with few-shot unlabeled calibration data. At the core of our methods is a permutation-invariant transformer with a novel context-dependent positional embedding that allows flexible identification of neural unit identities on-the-fly. We evaluate our approach on three movement decoding datasets from the FALCON Benchmark [26], demonstrating robust cross-session generalization on motor tasks in human and non-human primates. Our model outperforms zero-shot and few-shot unsupervised baselines, while not requiring any retraining or fine-tuning overhead.

In summary, the contributions of this work include:

We present a transformer-based permutation-invariant framework with a novel context-dependent positional embedding for few-shot unsupervised behavioral decoding. Our flexible, lightweight model enables ingestion of unordered sets of neural units during training and facilitates out-of-thebox inference on unseen neural populations.

We evaluate our model on three motor behavioral decoding datasets in the FALCON Benchmark, showing robust gradient-free generalization to unseen sessions in the presence of cross-session nonstationarities.

2 Related Work

Decoding motor behavior from intracortical population activity : A variety of computational models have been proposed to model population spiking dynamics and decode motor behavior, starting with linear models such as population vectors [1], linear regression [30], Kalman filter [31, 32, 33]. Nonlinear methods have also been developed; a non-exhaustive list includes generalized linear models [34, 35, 36, 37], latent variable models [38, 39, 40, 41], leveraging deep neural networks including recurrent neural networks (RNN) [27, 42, 43] and transformers [44, 21, 28, 45, 46, 22, 47, 48, 49]. Most of these works assume a fixed view of the population across time and have limited direct transferability to unseen populations. We adopt the view of neural population as an unordered set of neural units, offering more flexibility in transferring across population compositions.

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Cross-session neural alignment methods : Neural nonstationarities pose challenges for maintaining decoder performance over long periods of time. To tackle this issue, alignment techniques were proposed to align the testing sessions to match the distribution of a training session, usually performed in the latent space. These techniques vary from linear methods using Canonical Correlation Analysis (CCA) [14, 20], linear stabilizer [23], linear distribution alignment [24], to nonlinear using generative adversarial networks (GAN) [50, 29], RNN [25, 7, 51], and diffusion models [52]. All these approaches, however, still require explicit alignment procedures with model parameter updates and test-time labels in some cases to adapt the pretrained decoder to unseen populations. On the other hand, our proposed context-dependent positional embedding scheme allows flexible, gradient-free adaptation in new sessions without any labels.

Foundation models for neuroscience : Recent advances in foundation models for neural decoding demonstrate promising transferability across sessions, subjects and tasks through pretraining on large and diverse corpora of neural recordings [21, 28, 22, 47, 48, 53, 49]. While these approaches aim to build generic pretrained models that are then adapted to diverse downstream settings via parameter finetuning, SPINT instead focuses on reducing calibration burden in real-world iBCI systems by achieving gradient-free cross-session generalization under the limited data regime. SPINT, however, also ingests data from multi-session recordings during training to achieve this goal. Architecturally, SPINT employs the cross-attention mechanism to integrate information across a variable number of tokens similar to POYO-style models [22, 47, 48], while also operates on binned spike counts similar to NDT-style frameworks [21, 28, 53, 49] and POYO+ in spirit [47]. Unlike these models, SPINT adopts a much shallower design-using only a single cross-attention layer-and tokenizes data by treating each neural unit's temporal context window as a token, analogous to the spatial tokenization in [46, 54], and distinct from the temporal tokenization in [44, 55], patch-based tokenization in [21], or spike-based tokenization in [22, 48].

Permutation-invariant neural networks for set-structured inputs : While conventional neural networks are designed for fixed dimensional data instances, in many set-structured applications such as point cloud object recognition or image tagging, the inputs have no intrinsic ordering, advocating for a class of models that are permutation-invariant by design [56, 57]. One such work, DeepSets, introduced a set average pooling approach serving as a universal approximator for any set function [56]. Follow-up works [57, 58] extended this pooling method to include max-pooling and attention mechanisms [59]. We take inspiration from these works to design our universal neural unit identifier and permutation-invariant behavior decoding framework, leveraging the permutationinvariant property of the cross-attention mechanism [60].

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3 Approach

3.1 A permutation-invariant framework for few-shot continuous behavioral decoding

We study the problem of real-time, cross-session iBCI decoding, where behavior needs to be decoded in a causal manner from a continuous stream of neural observations. Concretely, within a single session s , let X i,t denote the binned spiking activity of neural unit i at time t , X i, : denote all the activity of unit i , and X : ,t denote the activities of all units at time t . Given a past observation window of population activity X : ,t -W +1: t ∈ R N s × W , where N s is the number of recorded neural units in session s and W is the length of the observation window, we aim to estimate the corresponding last time step of behavior output Y t ∈ R B , where B is the dimensionality of behavior covariates. Model parameters are fitted with gradient descent using labeled data from k training (held-in) sessions and evaluated on k ′ testing (held-out) sessions without gradient updates or labels. At our disposal on each held-out session is a short calibration period X i, [ C ] ∈ R T ′ consisting of M -shot variable-length trials lasting for T ′ timesteps, to be used for cross-session adaptation.

Traditional approaches consider the population activity at each timestep as a "token" and decode behavior by modeling temporal dynamics of population activity [39, 27, 44]. By treating temporal snapshots of population activity X : ,t ∈ R N s as input vectors, these approaches assume a fixed number and order of neural units, requiring explicit spatial re-alignment when applied to another session with a different size and order [27, 25, 29, 20, 24]. Recent methods incorporating factorized spatialtemporal modeling [46, 61, 45] face similar challenges, while approaches with explicit spatiotemporal tokens [21, 28, 22, 47, 48] still require fine-tuning unit identity in novel sessions. These design choices hinder out-of-the-box generalizability of neural decoders across sessions, as a universal decoder should ideally be invariant to the permutation and size of the input population.

To realize this goal, we treat windows of individual neural units X i,t -W +1: t ∈ R W as an unordered set of tokens and aggregate information from these units to decode behavior using the cross-attention mechanism [59]. To compensate for the loss of consistent order that the decoder can leverage for behavior decoding, we embed a notion of neural identity to each unit based on its spiking signature during a few-shot, unlabeled calibration period X : , [ C ] in the same session. X : , [ C ] is either provided in limited amount in held-out sessions at test time, or is artificially sampled from held-in sessions

during training. This context-dependent neural identity is inferred by a universal neural identity encoder that is shared across units and sessions, enabling gradient-free adaptation to novel population compositions at test time.

3.2 Encoding identity of neural units

Let X C i ∈ R M × T be the trialized version of X i, [ C ] ∈ R T ′ . X C i is the collection of M calibration trials of neural unit i interpolated to a fixed length T . We infer neural identity E i ∈ R W of unit i by a neural network IDEncoder :

where ψ and ϕ are multi-layer fully connected networks and pool is the mean pooling operation across M trials. Due to the permutation invariant nature of the mean operation and the fact that ψ and ϕ are applied trial-wise, IDEncoder is invariant to the order of M calibration trials by design [56].

3.3 Decoding behavior via selective aggregation of information from neural population units

After inferring the identity for each unit from its calibration period, we add E i to all X i windows to form identity-aware representations Z i . Z i contains the time-varying activity of each unit while also being informed of the unit's stable identity within one session. In matrix form:

where Z i 's, X i 's, E i 's constitute rows of the Z, X, E matrices.

We leverage the cross-attention mechanism to selectively aggregate information from population units and decode behavior outputs:

where K = ZW K , V = ZW V ∈ R N s × W are projections of the identity-informed neural activity Z , and Q ∈ R B × W is a learnable matrix to query the behavior from Z. Q is initialized at random and learned during training. In future work, SPINT can be extended to accommodate multi-task setup by having multiple Q matrices, one for each behavior task, as in [47]. We use the standard cross-attention module with pre-normalization and feedforward layers (see details in Appendix).

Proposition 1. Cross-attention with identity-informed neural activity (Equation 3) is invariant to the permutation of neural units, i.e.,

where P R is the row permutation matrix. (See proof in Appendix).

E in Equations 2 and 3 can be understood as a special kind of positional embedding for attention mechanism, where E is equivariant to the order of tokens (neural units), i.e., permuting the rows in X also permutes the rows in E accordingly. Hence, unlike traditional positional embeddings in the transformer literature where positional embeddings are fixed entities, our proposed E is context dependent . This context-dependent positional embedding enables cross-session generalization by design, as E is stable for all samples within the same context (session), and can readily adapt in a gradient-free manner to new populations with arbitrary size and order.

After cross-attention, we project down Y by a fully connected layer to a one-dimensional vector representing the predicted behavior covariates at the last timestep, based on which we compute the mean squared error (MSE) between the predicted and the ground truth behavior covariates. The IDEncoder and cross-attention module are trained in an end-to-end manner using this MSE objective.

3.4 Encouraging model robustness to inconsistent population composition

Neural population distributes its computation among many neural units, allowing us to effectively decode behavior even though we can only record neural activity with a limited number of electrodes. Leveraging this insight and in order to encourage model robustness to different compositions of neural membership across recording sessions, we employ dynamic channel dropout to avoid overfitting to the population composition seen during training. Unlike classical population dropout methods where only a fixed fraction of neurons/timesteps [62, 63, 44] or a variable fraction of neurons/timesteps sampled from a conservative range [64, 22] is removed during training, we uniformly sample a dropout rate between 0 and 1 each training iteration and remove population units with the sampled dropout rate. With dynamic channel dropout, we not only encourage the model to be robust to the unit membership but also encourage it to be robust to the size of the population, leading to improved cross-session generalization without additional tuning of dropout rates (see Section 4.8 and Figure 4).

3.5 Gradient-free, few-shot cross-session adaptation in unseen neural populations with variable size and order

The overall framework is depicted in Figure 2. We use labeled data from all training sessions to train all model parameters following the above pipeline. The model naturally digests populations of arbitrary size and order in all sessions without any need of session-specific alignment layer or fixed positional embeddings for neural units, hence having the potential to scale up to a large amount of data. When testing on a held-out session, we reuse the trained IDEncoder and only need a few unlabeled calibration trials to infer identities of neural units in the test session, without the need of gradient descent updates to fine-tune session-specific alignment layers or unit/session embeddings. With these benefits, our proposed model removes the time and computation overhead usually required for re-calibrating neural decoders before each session, and facilitates its applicability in real-world iBCI settings where test-time labels are inherently unavailable.

4 Experiments

4.1 Datasets and evaluation metrics

We evaluate our approach on three continuous motor decoding tasks from the Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark [26]. Specifically, we evaluate SPINT on the M1, M2, and H1 datasets. In M1, a monkey reached to, grasped, and manipulated an object in a variety of locations (4 possible objects, 8 locations), while neural activity was recorded from precentral gyrus and intramuscular electromyography (EMG) was recorded from 16 muscles [65, 66, 67, 68]. In M2, a monkey made finger movements to control a virtual hand and acquired cued target positions while neural activity from the precentral gyrus and 2-D actuator velocities were captured [69]. In H1, a human subject attempted to reach and grasp with their right hand according to a cued motion for a 7-degree-of-freedom robotic arm control [3, 70, 71]. Each dataset comprises multiple labeled held-in sessions used to train the decoder (spanning 4, 4, and 6 days for M1, M2, and H1, respectively), and multiple held-out sessions for model evaluation (spanning 3, 4, and 7 days for M1, M2, and H1, respectively). Each held-out session provides a few public calibration trials (with optional labels) used for decoder calibration, after which the decoder is evaluated on a private test split. Cross-session performance is quantified by the mean and standard deviation of R 2 between the predicted and ground truth behavior covariates across all held-out sessions. All evaluation results were obtained on the held-out private split by submitting models to the EvalAI platform [72].

4.2 Baselines

We compare SPINT with zero-shot (ZS) and few-shot unsupervised (FSU) baselines, since SPINT is the intersection of these two approaches. Similar to FSU approaches, SPINT makes use of a few unlabeled calibration samples in the held-out sessions; however, unlike conventional FSU approaches, SPINT does not require gradient updates for model parameters at test time, therefore bearing resemblance to ZS methods in terms of practical utility. We call this new class of model gradient-free few-shot unsupervised (GF-FSU).

ZS Wiener Filter and ZS RNN : Wiener Filter is a linear model that predicts the current behavior as a weighted sum of previous timesteps [73]. In addition to the Wiener Filter, we also compare with a

simple RNN baseline (implemented as an LSTM [74]). The WF and RNN models were fitted using a single held-in session and evaluated zero-shot on the held-out sessions.

CycleGAN [29] : An FSU method where a Generative Adversarial Network (GAN) is trained using calibration data from a held-out session (day K) to transform day K's population activity to a form resembling activity from a held-in session (day 0), allowing decoders pretrained on day 0 to be reused on day K.

NoMAD [25] : Another FSU method where a dynamical model and a decoder are trained on day 0 to predict behavior from the inferred dynamics. Then on day K, an alignment network is trained to match the distribution of neural latent states to that of day 0, allowing the fixed model and decoder to transfer to day K.

Wiener Filter, RNN and Transformer Oracles (OR) : We include the Wiener Filter, RNN, and NDT2 - a transformer for neural data [21], trained on private held-out labeled data to serve as upper bounds for model performance.

NDT2 Multi (FSS) [21] : Similar to NDT2 Multi OR, but only trained on held-in and held-out few-shot calibration data with supervision.

4.3 SPINT outperforms zero-shot and few-shot unsupervised baselines on continuous motor decoding tasks

We show in Table 1 the performance of SPINT in comparison with ZS and FSU approaches. SPINT outperforms all ZS and FSU baselines across all three datasets, while requiring no retraining or fine-tuning of model parameters. Improvement is most prominent in M1, where the amount of training data is the largest ( ∼ 5 × more data than H1 and ∼ 6 × more data than M2 in terms of recording time). Notably, SPINT surpasses Wiener Filter oracles in all datasets, which were trained with access to the private labeled data. SPINT even outperforms the FSS method NDT2 Multi on M1 dataset while unlike NDT2, it does not require access to test-time labels or model parameter updates. As we focus on cross-session transferability, all our experimental results show the cross-session performance. We include comparison on within-session performance in the Appendix.

4.4 SPINT requires only a minimal amount of unlabeled data for adaptation

To gauge the data efficiency of our model at test time, we trained and tested the model with varying number of calibration trials used to infer the neural unit IDs. We show in Figure 3(A) that SPINT could achieve reasonable cross-session generalization with a small number of few-shot trials. In M1 dataset, the model could even achieve similar performance as the best model (which uses all available calibration trials) with only one single trial. This study demonstrates the practical utility of SPINT in online iBCI, relieving the burden of data collection and label collection on users at test time.

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4.5 SPINT performance scales well with the amount of training data

Thanks to the flexible permutation-invariant transformer network and the context-dependent positional embeddings, SPINT can ingest populations with arbitrary sizes and orders. These design choices give SPINT the ability to scale naturally with large amounts of training data. We demonstrate this scaling ability in Figure 3(B), where we observe a clear trend in cross-session performance when we use data from more held-in days to train the model, with the best performance achieved when using all available training data on each dataset. This ability suggests potentials of SPINT as a large-scale pretrained model for iBCI when trained on larger datasets beyond the FALCON benchmark.

4.6 SPINT is robust to variable population composition

Our proposed dynamic channel dropout encourages robustness of SPINT to variable input population size and membership. We test this robustness by training SPINT on the full held-in populations with dynamic channel dropout and evaluating on variable-sized held-out populations (Figure 3C). At each evaluation batch, we randomly sample a subset of the original population and measure the R 2 obtained when the model makes predictions based on this limited subset. We observe robust performance in M1 with reasonable performance drop when the population gets increasingly smaller, with the model still achieving a mean held-out R 2 of 0 . 52 when only 20% of the original population remains, outperforming other ZS and FSU baselines with the full population.

4.7 SPINT maintains low-latency inference for iBCI systems

A critical consideration in iBCI system deployment is the ability of the system to perform behavior decoding in real time. We designed SPINT with this consideration in mind, using only one layer of cross-attention and two three-layer fully connected networks for IDEncoder. In Table 2, we report the latency achieved by SPINT as compared to other methods. Latency is defined as the amount of time a method requires to process the evaluation data divided by the duration of the evaluation data [72]. The ratio less than 1 signifies the approximation to real-time iBCI inference. SPINT achieves 0 . 13 latency on M1 and M2, and 0 . 14 latency on H1, matching or outperforming transformer baselines, while being significantly below 1 . In practice, SPINT could be potentially faster in terms of deployment time, as it eliminates the need for an explicit alignment step required by conventional iBCI systems.

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4.8 Ablation Study

We perform ablation studies to demonstrate the benefits of our context-dependent positional embeddings and dynamic channel dropout techniques. In Figure 4A, we compare our context-dependent positional embeddings with fixed (absolute) positional embeddings used in the vanilla transformer [59], and with no positional embeddings. The conventional fixed positional embeddings break the permutation-invariance property of the cross-attention mechanism, thus are not able to generalize to populations with different compositions in held-out sessions. With no positional embeddings, the model is permutation-invariant by design; however, the loss of information about neural unit functional identities hinders the model's ability to decode the behavior these units encode. We achieve the best of both worlds by our proposed context-dependent positional embeddings, being both permutation-invariant while retaining neural identities for behavior decoding.

To demonstrate the effectiveness of our proposed dynamic channel dropout technique, we compare the cross-session performance of SPINT with dynamic channel dropout and without dropout. We show in Figure 4B that dynamic channel dropout serves as an effective regularization technique by preventing the model from overfitting to the population composition in training sessions.

5 Discussion

In this work, we introduce SPINT, a permutation-invariant transformer designed for cross-session intracortical motor decoding. SPINT features a context-dependent neural ID embeddings that dynamically infers unit identities, allowing the model to handle unordered, variable-sized populations

across sessions. These components enable SPINT to adapt to new sessions using only a small amount of unlabeled calibration data, with no need for test-time labels or gradient updates. We demonstrate SPINT's cross-session generalization ability on three continuous motor decoding tasks from the FALCON Benchmark, where it consistently outperforms existing zero-shot and few-shot unsupervised baselines, even surpassing few-shot supervised and oracle models in some instances. SPINT's light-weight design also enables low-latency inference, making it well-suited for real-world iBCI applications.

Our study represents an initial attempt at a flexible, gradient-free framework for consistent behavioral decoding and opens up several promising avenues for future research. Within the framework of the FALCON Benchmark, we demonstrated that our approach scales effectively with increasing amount of training data, albeit from a single subject and behavior task. Exploring the applicability of our approach under more diverse settings (cross-task, cross-region, cross-subject) and on non-motor behaviors is an important direction for future research [44, 22, 47, 48, 49]. On the technical side, our end-to-end training for the unit identifier together with the rest of the network ties the identification of the neural identities to task-specific behavioral decoding and thus requires access to labels to train the unit identifier, which might be limited in real-world iBCI settings. Disentangling the training of the unit identifier with behavioral decoding by means of self-supervised approaches such as contrastive or predictive learning can potentially alleviate the reliance on behavior labels for this stage, albeit at the cost of additional training overhead and potential loss of behavior-relevant neural features essential for downstream decoding.

Lastly, although our study shows robust and low-latency behavioral decoding in silico , its performance remains to be evaluated in vivo , where sensory feedback from closed-loop control may induce modulation in the neural activity inputs and hardware-specific constraints could affect the online inference latency [75]. To this end, our preliminary assessment represents an initial step toward a light-weight, plug-and-play decoding framework for long-term iBCI systems.

6 Acknowledgment

The authors acknowledge the partial support of HDR Institute: Accelerated AI Algorithms for Data-Driven Discovery (A3D3) National Science Foundation grant PHY-2117997 (TL, HF, JL, AO, ES) and EFRI-BRAID-2223495 (ES, TL). The authors also acknowledge the partial support by the Departments of Electrical Computer Engineering, Applied Mathematics and Bioengineering. The authors are thankful to the Center of Computational Neuroscience, the eScience Center at the University of Washington and the Allen Institute for Brain Science. TL and US wish to thank the founder of the Allen Institute, P.G. Allen, for his vision, encouragement, and support. The authors also thank the FALCON organizers Joel Ye, Brianna Karpowicz, Chethan Pandarinath, Thiago Scodeler, and the EvalAI team for their technical support with the datasets and submissions.

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A Appendix

A.1 Within-session performance comparison

We include the within-session performance comparison between SPINT and baselines in Table A1. This table is similar to Table 1 in the main paper, but with metrics obtained on EvalAI's private splits within the held-in sessions. As observed from the table, SPINT also consistently outperforms ZS and FSU baselines on the held-in splits.

A.2 Proof of SPINT's permutation-invariance

Let P R , P C be the row and column permutation matrices of the same permutation π ( P C = P ⊤ R = P -1 R and P C P R = I ). Also let X ′ = P R X and ( X C ) ′ = P R X C be the row-permuted neural windows and row-permuted calibration trials.

Since the ID embedding of each neural unit i is computed individually from the set of calibration trials for that unit:

permuting the neural units in the original population (neural windows X or calibration trials X C ) will permute the embedding matrix E in the exact same order, i.e., E ′ = P R E .

It follows that:

In other words, Z is equivariant to the permutation of neural units.

Cross-attention performed on Z ′ then becomes:

where softmax ( QW ⊤ K Z ⊤ P C √ d k ) = softmax ( QW ⊤ K Z ⊤ √ d k ) P C because an element is always normalized with the same group of elements in the same row regardless of whether column permutation is performed before or after softmax .

Equation A3 concludes Proposition 1 in the main paper.

We note that multi-layer perceptron (MLP), layer normalization, and residual connection are applied row-wise and hence do not affect the overall permutation-invariance property of our SPINT framework.

A.3 Correlation of attention scores and firing statistics

We ask whether the attention scores SPINT assigns for each neural unit are correlated with its firing statistics. To answer this question, in each held-out calibration window, we measure the average attention scores over B behavior covariates, and its firing statistics (mean/standard deviation) over the held-out calibration trials, then calculate the Pearson's correlation between these two quantities using all held-out calibration windows. We show the results in Table A2.

We observe that the attention scores correlate moderately with the mean and the standard deviation of the neural unit's firing rates, with higher correlation for the standard deviation than the mean, suggesting that SPINT might be extracting neural units that are active (having high mean firing rates) and behaviorally relevant (having high variance throughout the calibration periods where behavior is varied) to pay attention to in behavioral decoding.

Table A2: Pearson's correlation between attention scores for each neural unit and that unit mean/standard deviation of firing rates during the held-out calibration periods. Results are reported as the mean correlation ± standard deviation across held-out sessions. All p -values are less than 0 . 05 .

A.4 Implementation details

A.4.1 Data preprocessing

For neural activity, we use the binned spike count obtained by unit threshold crossing with the standard bin size of 20ms as set forth by the FALCON Benchmark. We follow FALCON's continuous decoding setup for all three M1, M2, and H1 datasets, where rather than decoding trialized behavior from the trialized neural activity (often performed in a non-causal manner), we decode behavior at the last step of a neural activity window, mimicking the online, causal iBCI decoding. To construct the lengthW neural window at the beginning of each session, we pre-pad the session neural time series with ( W -1) zeros. We discard the windows whose last time step belongs to a non-evaluated period as defined by FALCON, e.g., inter-trial periods where there is no registered kinematics.

Our IDEncoder infers neural unit identity from trialized calibration trials. As calibration trials vary in length, we interpolate all calibration trials to the same length T , where T = 100 for M2 and T = 1024 for M1 and H1. We use the Python library scipy.interpolate.interp1d with a cubic spline for interpolation. Note that we only perform interpolation for neural calibration trials to synchronize their trial lengths. We still use the raw spike counts for the neural windows, conforming with the continuous decoding setup.

A.4.2 Behavior output scaling

For M2 and H1, since values of behavior covariates are relatively small, during training we scale the network behavior predictions by a factor of 0 . 2 and 0 . 05 for M2 and H1, respectively, effectively asking the model to predict 5 × and 20 × the original behavior values. The MSE loss and R 2 metrics are computed between the scaled predicted outputs and the original ground truth values.

Table A3: SPINT's cross-session performance against dynamic dropout and different choices of fixed dropout rates. Results are reported as mean ± standard deviation across held-out calibration sessions. DD [0,1] stands for dynamic dropout with variable dropout rates between 0 and 1 .

DD range

R

2

0

.

[0, 0.1]

59

±

0

.

07

0

.

[0, 0.2]

59

±

0

.

07

0

.

[0, 0.3]

61

±

0

.

10

0

.

[0, 0.4]

62

±

0

.

07

0

.

[0, 0.5]

63

±

0

.

07

0

.

[0,1]

64

±

0

.

10

Table A4: SPINT's cross-session performance across different ranges of dynamic dropout. Results are reported as mean ± standard deviation across held-out calibration sessions.

Table A5: SPINT's cross-session performance for different cross-attention head counts. Results are reported as mean ± standard deviation across held-out calibration sessions.

Table A6: SPINT's cross-session performance for different number of self-attention layers. Results are reported as mean ± standard deviation across held-out calibration sessions.

# cross-attention layers

R

2

1

2

3

4

5

0 . 64 ± 0 . 10 0 . 65 ± 0 . 10 0 . 65 ± 0 . 10 0 . 64 ± 0 . 11 0 . 62 ± 0 . 13

Table A7: SPINT's cross-session performance for different number of cross-attention layers. Results are reported as mean ± standard deviation across held-out calibration sessions.

Table A8: SPINT's cross-session performance for different context window sizes. Results are reported as mean ± standard deviation across held-out calibration sessions.

A.4.3 Inferring neural unit identity

We follow the permutation-invariant framework in [56] for inferring identity E i of neural unit i :

where M is the number of calibration trials, X C j i is the neural activity of the j th calibration trial of neural unit i , MLP 1 and MLP 2 are two 3-layer fully connected networks. MLP 1 projects the lengthT trials to a hidden dimension H , and MLP 2 projects the lengthH hidden features to lengthW neural unit identity output.

A.4.4 Behavioral decoding by cross-attention

After neural identity for all units E is inferred, we add it to the neural window input X to form the identity-aware neural activity Z , i.e., Z = X + E . We then use the cross-attention mechanism in the latent space to decode last step behavior covariates. Specifically:

A.4.5 Hyperparameters

We include the notable hyperparameters used to optimize SPINT in Table A9. We train and evaluate models for each M1, M2, and H1 dataset separately. We train the models using all available held-in sessions and evaluate on all available held-out sessions. We use Adam optimizer [76] for all training.

We include representative hyperparameter sweeps demonstrating SPINT's robustness to hyperparameter choices in Tables A3, A4, A5, A6, A7, A8. This robustness allows SPINT to effectively capture long-range context while maintaining a minimalist architecture without compromising generalizability. All sweep results were obtained on 20% of calibration trials held out from each session of the M1 dataset rather than on the EvalAI test split.

A.4.6 Computational resources

SPINT was trained using a single A40 GPU, consuming less than 2GB of GPU memory with batch size of 32 and taking around 12 hours, 5 hours, and 8 hours to finish 50 training epochs for M1, M2, and H1, respectively. We select checkpoints for evaluation at epoch 50 in all M1, M2, and H1 datasets.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAuthors introduce SPINT, a neural decoder that is invariant to unit activity, thanks to a context dependent positional encoding. They show improved performance compared to other zero or few-shot methods on the FALCON dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: It is a very good paper, building on a great idea (being invariant to unit ID), with sound design choices and good evaluation. \n\nWeaknesses: I don't see no major weakness to this paper, but as the authors mentioned in the Discussion section, it will be important for future work to evaluate SPINT in vivo, where the noise and perturbations to unit assignment and spike detection can be much more complex.\n\nQUESTIONS:\nFirst, i find section 3.5 a bit unclear. From the rest of the paper and the architecture design, I would have expected that IDEncoder is fine-tuned, while the cross attention layer remains fixed in the few shot experiment. Lines 190-192 seems to indicate the contrary? I am not sure I understand this part correctly and believe this could be clarified further.  \n\nAlso, authors claim that this is an attempt at a gradient-free framework. I believe that their method could be applicable in more complex datasets as a few-shot decoder (i.e. not gradient free), but don't believe that it will work as a zero-shot method. I would appreciate if authors can explain why they claim their method to be a step towards a gradient-free method, or remove this claim (I don't believe it's a drawback of the paper since few-shot is already sufficient in my opinion, but I would appreciate a further explanation if the method is truly zero-shot). To clarify this further, in Table 1, it would help to show which methods are OR, FSS, FSU, and ZS (you could have four \"rows\" for each of them and add it in the table). \n\nIn Figure 3A, it is very surprising that the method works on M1 with 1 calibration trial only. Could the authors give some intuition behind this result? \n\nThe authors claim that the neural population can be of arbitrary size. Isn't it bounded by a maximum population size? How do you deal with populations of increasing size? The problem of having \"disappearing neurons\" is adressed, but neurons can appear in the recordings as well (it can also be an artefact of the spike sorting / preprocessing pipeline). \n\nFinally, in in-vivo recordings, often neurons get \"mixed up\" together, rather than \"permuted\", in the sense of spikes from one neuron get assigned to the other and vice versa, as a result of the spike sorting pipeline. Do you think the method will be robust to such noise, and could you augment your permutations (maybe add such a perturbation on top of the channel dropout) to make it robust to these artefacts?\n\nLIMITATIONS:\nYes, the authors have discussed the limitations. \nAs mentioned, two important challenges are to align across task, regions and subjects, and to extend the method to in vivo recordings.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper tackles cross‐session drift in intracortical BCIs caused by shifting neural signals. It introduces SPINT, a transformer that treats neurons as unordered tokens and infers their identities from a small set of unlabeled calibration data. SPINT uses context‐dependent position embeddings and dynamic channel dropout to adapt without gradient updates. It outperforms zero‐ and few‐shot baselines on multi‐session motor decoding benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The paper is well written and organized, with diagrams and pseudo-code that make the approach easy to follow.  \n2. The experiments are comprehensive and rigorously compare SPINT to zero-shot, few-shot, and oracle baselines on three primate and human datasets, showing clear performance gains.  \n3. SPINT adapts with only a small amount of unlabeled calibration data and requires no gradient updates, making it practical for real-world BCIs.  \n4. Combining context-dependent position embeddings with dynamic channel dropout is a novel solution for session drift without explicit alignment.\n\n**Weaknesses**\n1. No failure cases are shown, so it’s unclear how SPINT handles extreme signal drift or very limited calibration data.  \n2. Hyperparameter choices—such as dropout rates and attention head counts—are not well justified, making sensitivity hard to assess.  \n3. Evaluation is limited to motor decoding, leaving its generality to other BCI tasks untested.\n\nQUESTIONS:\n1. How well does SPINT handle very large shifts in neural signals between sessions?  \n2. Can SPINT adapt with only one or two calibration trials?  \n3. Could this method work for non-motor tasks (e.g., speech decoding)?  \n4. Are there any privacy or ethical concerns with long-term neural recording?  \n\nI will take into account other reviewers’ opinions when evaluating the paper further.\n\nLIMITATIONS:\nNo.\n\nThe authors do outline methodological limitations in the Discussion section, but they omit any consideration of potential negative or dual-use societal impacts.\n\nSuggestions for improvement:\n1. Add a “Broader Impacts” subsection that discusses risks of misuse (e.g., privacy breaches, uneven access to iBCI technologies).\n2. Reflect on ethical concerns around long-term neural recording and consent, and outline safeguards (e.g., data anonymization, IRB oversight).\n3. Consider potential downstream harms (e.g., neuro-surveillance) and propose mitigation strategies.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 1, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces SPINT, a spatial permutation-invariant transformer architecture for behavioural decoding from neural population activity, in order to tackle the problem of dynamically inferring neural unit identities on new sessions or days and adapting to non-stationarities over time in intracortical brain-computer interface applications. SPINT uses a neural unit identity encoder that is equivariant under permutation of neural unit/channel orderings in the dataset/recording source, and invariant to the ordering of the calibration trials. While this encoder is trained on the pretraining dataset, it is not finetuned when generalising to subsequent sessions or days and is thus used in a zero-shot parameter update-free manner (only the unit identity embeddings are updated using the calibration trials), which the authors refer to as their few-shot unsupervised and gradient-free unit identification scheme. The authors also use a dynamic dropout scheme to improve robustness to variable neural population sizes. Post identity encoding of a time chunk of neural activity, behaviour is decoded using a cross-attention readout. The authors demonstrate results on 3 motor decoding datasets of the FALCON Benchmark for spiking activity-based neural decoders (2 nonhuman primate and 1 human dataset), and demonstrate better performance compared to other unsupervised baseline methods, while also showing some improvements with increasing training data and calibration trials, and with comparable latency to some Transformer-based methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n* The writing and figures are clear and easy to understand.\n* The authors have performed experiments on a recently introduced standard benchmark for neural decoding and have included important ablations and few-shot + data scaling results.\n* The method works with variable-sized neural populations thanks to the cross-attention mechanism.\n* The method is simple and can be used for lightweight, update-free adaptation – a key asset – across days in single subject and single task setups.\n\n**Weaknesses**\n\n* All experimental results are on single subject evaluation setups, with no experiments on cross-subject transfer for heldout subjects. While this is a consequence of the FALCON benchmark not having multi-subject data (all of M1, M2, and H1 are single subject datasets), it would be important to study how well SPINT performs in the multi-subject setting using other datasets as done in prior work [1-3] without which it is hard to evaluate the claims of it being a \"plug and play\" decoding method. The lack of this evaluation also diminishes the significance of the few-shot finetuning paradigm: the model cannot take advantage of multi-dataset pretraining and would have to be retrained for every new subject from scratch, limiting the finetuning scheme to the single subject setting across sessions/days. Baselines discussed in the paper, such as NDT-2 [1], and other key methods such as POYO [2] and NDT-3 [3] (latter is concurrent work) have studied this multi-subject setting, and some of these works demonstrate strong zero-shot or few-shot performance on new days too [1]. Furthermore, in my opinion, the \"plug and play\" argument for SPINT vanishes because quite a good amount of pretraining data is needed for good performance in the single subject case (ref. Fig. 3B, especially middle and right panels where multiple days of data are needed to get decent decoding performance), and there is no amortization of these costs through pre-training.\n* On a related note, while there are three datasets considered here, all are from a single benchmark while several of the mentioned baselines including the NDT models [1,3] have been tested on other public datasets [4-6]. Evaluating SPINT on these datasets would allow for better comparison to a wider range of existing approaches and improve the evaluations in this paper.\n* Another key point to me is the lack of comparison to POYO/POYO+ [2,7] (although the former is cited), given several architectural similarities. POYO can also accept unordered neural units as inputs, where it would learn a channel/unit embedding for each neuron, although through supervised learning for each new dataset. It is thus equivariant to the ordering of units. While SPINT takes in spike counts, where there is one token per unit, POYO is generally provided individual spike tokens, although it may also be used with spike counts [2,7,8] ([8] is concurrent work). While SPINT doesn't use self-attention layers, POYO uses a cross-attention encoder and decoder, and the setup in SPINT is very similar to POYO's behaviour queries and the overall Perceiver IO [9] framework. Note also that POYO uses a unit dropout scheme to improve robustness to population variability, so that is yet another similarity. Given these similarities, and the public availability of POYO code through `torch_brain` (along with a checkpoint) [10], I think it would be important for authors to consider this as a baseline on other public datasets (if not the FALCON benchmark, which POYO has not been submitted to). I do acknowledge that each approach has its own assets and pitfalls: POYO _requires_ supervised finetuning but excels at cross-task and cross-subject transfer, SPINT doesn't require parameter finetuning at all but has not been evaluated in a multi-subject setting.\n* The proposed approach which considers fixed $W$-length windows of neural activity may not work for attempted speech decoding in the FALCON H2 dataset, since decoding is from a fixed context window and there is no history as you would have in a recurrent architecture (unlike concurrent work [8]). This dataset only contains trial-level sentence annotations and not dense labels, so chunking up trials (which can be 10s+ long) and decoding phonemes from chunks would probably not yield good performance (as such, H2 only has recurrent baselines on the FALCON leaderboard).\n* The unit embedding scheme used here is tied to a single, specific behavioural task on which the model is trained. This limitation is acknowledged in the discussion, but it is a key obstacle to building cross-task models (currently, models would have to be retrained for each task). The authors could consider a readout scheme like POYO+ [7] to allow for multi-task decoding, but the advantages are limited without large-scale multi-dataset pretrainability. I do agree with the authors' suggestion that SSL approaches are the way forward for this problem.\n* This is relatively minor compared to my other concerns, but the channel dropout scheme introduced here with variable dropout rates is not entirely novel, and I'm curious about the exact setup used in the ablation study shown in Fig. 4B. To begin with, standard dropout [12] with a fixed dropout rate $p$ would itself dropout a variable number of units in each forward pass, just with a mean fraction of $p$ units dropped out. Secondly, models like POYO [2] and MYOW [11] have used unit dropout schemes with a minimum threshold for the number of units already. Thirdly, adaptive and variable dropout schemes are already prevalent in the deep learning literature [13,14], but have not been as widely adopted as standard dropout, potentially due to limited gains at large scale. Finally, about Fig. 4B, when the authors mention \"without\" dynamic dropout, do they mean there is no dropout at all, or just standard, fixed $p$ dropout? Of course I would expect consistent gains with (any type of) dropout as opposed to not having dropout at all, but given that the authors list the \"dynamic\" dropout as a contribution, I would expect an ablation to show performance comparisons with standard unit dropout [2,11] ideally with different values of $p$ in the ablation study. Could the authors clarify this?\n\n**References**\n\n1. Ye, Joel, et al. \"Neural data transformer 2: multi-context pretraining for neural spiking activity.\" Advances in Neural Information Processing Systems 36 (2023): 80352-80374.\n2. Azabou, Mehdi, et al. \"A unified, scalable framework for neural population decoding.\" Advances in Neural Information Processing Systems 36 (2023): 44937-44956.\n3. Ye, Joel, et al. \"A Generalist Intracortical Motor Decoder.\" bioRxiv (2025): 2025-02.\n4. Perich, Matthew G., Juan A. Gallego, and Lee E. Miller. \"A neural population mechanism for rapid learning.\" Neuron 100.4 (2018): 964-976. (https://dandiarchive.org/dandiset/000688?search=000688&pos=1)\n5. O’Doherty, Joseph E., et al. \"Nonhuman primate reaching with multichannel sensorimotor cortex electrophysiology.\" Zenodo (2017). (https://zenodo.org/records/3854034)\n6. Churchland, Mark M., et al. \"Neural population dynamics during reaching.\" Nature 487.7405 (2012): 51-56. (https://dandiarchive.org/dandiset/000070?search=churchland&pos=2)\n7. Azabou, Mehdi, et al. \"Multi-session, multi-task neural decoding from distinct cell-types and brain regions.\" The Thirteenth International Conference on Learning Representations.\n8. Ryoo, Avery Hee-Woon, et al. \"Generalizable, real-time neural decoding with hybrid state-space models.\" arXiv preprint arXiv:2506.05320 (2025).\n9. Jaegle, Andrew, et al. \"Perceiver io: A general architecture for structured inputs & outputs.\" arXiv preprint arXiv:2107.14795 (2021).\n10. Code: https://github.com/neuro-galaxy/torch_brain/tree/main/examples/poyo; checkpoint: https://github.com/neuro-galaxy/poyo\n11. Azabou, Mehdi, et al. \"Mine your own view: A self-supervised approach for learning representations of neural activity.\"\n12. Srivastava, Nitish, et al. \"Dropout: a simple way to prevent neural networks from overfitting.\" The journal of machine learning research 15.1 (2014): 1929-1958.\n13. Ba, Jimmy, and Brendan Frey. \"Adaptive dropout for training deep neural networks.\" Advances in neural information processing systems 26 (2013).\n14. Li, Zhe, Boqing Gong, and Tianbao Yang. \"Improved dropout for shallow and deep learning.\" Advances in neural information processing systems 29 (2016).\n\nQUESTIONS:\n* Could the authors clarify details on the dropout ablation?\n* What if you were to train or finetune the `IDEncoder` MLPs with the calibration data since labels are available for those trials? Does it max out performance/lead to considerable gains?\n* The model has no self-attention or other intermediate layers, the embedded units seem to be directly used to decode behavior by a fairly small cross-attention layer. Were additional parameters not helpful or necessary to see better performance? Did you try scaling up the model size? I anticipate that this may not help much in the single dataset/subject setting, but in the large-scale multi-dataset/subject pretraining case, I anticipate that smaller models will underperform larger overparameterised models.\n* Details on the learnable behaviour queries seem sparse. Could the authors specify additional details on the behaviour queries? Given that only one behaviour at the final timestamp is decoded per context window of neural activity, is there only one behaviour query (initialised at random and learnt during pretraining)? Could there be multiple queries for multiple task readouts?\n* The approach currently uses binned spike counts but especially given the cross-attention's ability to handle variable number of input tokens, binning isn't necessary and the authors could use individual spike tokenisation à la POYO. Have the authors tried this scheme? It could potentially yield slightly better performance since there is no loss of information from the binning component, and spike timings could be important [15].\n* I see from Table S3 that quite a bit of context is needed for some tasks (M1: $W$ = 100 = 2s, M2: $W$ = 50 = 1s – which is fine, i.e., on par with NDT-2 and POYO, H1: $W$ = 700 = 14s – a lot!), I understand this can work in a sliding window but I'm just curious. Have the authors tried varying these values/doing a search for the best values? Why is so much context needed for some of these tasks?\n\n15. Xie, Weizhen, et al. \"Neuronal sequences in population bursts encode information in human cortex.\" Nature (2024): 1-8.\n\nLIMITATIONS:\nYes, the authors have discussed the limitations of their work. There is no discussion on negative societal impact. While powerful iBCI decoders may generally have a positive impact on society, it may be worth recognising that a lot of these advances have been born out of animal research and may be misused as well; in addition there are concerns about the privacy of what is being decoded and when e.g. in decoding speech in humans.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a framework to address the variability and ordering in the placement of electrodes in the context of real-time, cross-session iBCI decoding and develop a spatial-permutation invariant neural transformer for decoding motor behavior from intracortical spikes. Spike windows are turned into tokens and aggregated via cross-attention.To compensate for the loss of positional information a neural ID encoder derives context-dependent embeddings from a handful of unlabeled \"calibration\" trials collected at the start of each new session. Next these embeddings are regularized by channel-dropout that removes an arbitrary fraction of units during training. The method has state of the art performance in three multi-day datasets from the FALCON benchmark. It also maintains real time inference without test-time gradients.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Permutation invariant and unit agnostic pipeline: Removes the rigid channel ordering assumption enabling training and testing on different population of different size and composition.\n2. Context-dependent neural id embeddings. \n3. light weight , low-latency architecture: This is particularly significant for closed loop BCI. \n4. Consistently outperforms previous approaches on FALCON without test-time gradients. \n\nWeaknesses:\n1. Shallow architecture limits long range modeling. Moreover, Neural ID Encoder completely forgets the within-trial structure. \n2. Restricted evaluation to 1 human and 2 monkey and behavio limited to arm/hand tasks \n3. Gradient -free adaptation still needs session specific calibration data.\n\nQUESTIONS:\n1. Could contrastive or predictive objectives remove the need for labelled data for the neural ID encoder? \n2. Would a lightweight temporal transformer on top of the cross-unit attention benefit decoding behaviors with longer latencies?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-06"}