A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
Authors: Georgios Mentzelopoulos
Ioannis Asmanis
Konrad Kording
Eva L Dyer
Kostas Daniilidis
Flavia Vitale
Venue: NeurIPS 2025 poster ()
Keywords: Neural Decoding
Brain Computer Interfaces
Spiking Neural Networks
Energy Efficient Computation
Primary area: neuroscience_and_cognitive_science
TL;DR: Spikachu: Scalable, Causal, and Energy Efficient Neural Decoding Based on Spiking Neural Networks.

Abstract: Brain-computer interfaces (BCIs) promise to enable vital functions, such as speech and prosthetic control, for individuals with neuromotor impairments. Central to their success are neural decoders, models that map neural activity to intended behavior. Current learning-based decoding approaches fall into two classes: simple, causal models that lack generalization, or complex, non-causal models that generalize and scale offline but struggle in real-time settings. Both face a common challenge, their reliance on power-hungry artificial neural network backbones, which makes integration into real-world, resource-limited systems difficult. Spiking neural networks (SNNs) offer a promising alternative. Because they operate causally (i.e. only on present and past inputs) these models are suitable for real-time use, and their low energy demands make them ideal for battery-constrained environments. To this end, we introduce **Spikachu: a scalable, causal, and energy-efficient neural decoding framework based on SNNs**. Our approach processes binned spikes directly by projecting them into a shared latent space, where spiking modules, adapted to the timing of the input, extract relevant features; these latent representations are then integrated and decoded to generate behavioral predictions. We evaluate our approach on 113 recording sessions from 6 non-human primates, totaling 43 hours of recordings. Our method outperforms causal baselines when trained on single sessions using between 2.26× and 418.81× less energy. Furthermore, we demonstrate that scaling up training to multiple sessions and subjects improves performance and enables few-shot transfer to unseen sessions, subjects, and tasks. Overall, Spikachu introduces a scalable, online-compatible neural decoding framework based on SNNs, whose performance is competitive relative to state-of-the-art models while consuming orders of magnitude less energy.

A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
Georgios Mentzelopoulos"
loannis Asmanis! Konrad P. Kording', Eva L. Dyer', Kostas Daniilidis 2, 2,
and Flavia Vitale
University of Pennsylvania, AArchimeess, Athena RC
Abstract
Brain-computer interfaces (BCIs) promise to enable vital functions, such as speech and prosthetic control, for individuals with neuromotor impairments. Central their success are neural decoders, models that map neural activity to intended behavior. Current learning- baaed decoding approaches fall into two classes: sim- ple, causal models that lack generalization, or complex, non-causal models that generalize and scale offline but struggle real-time settings. Both face a common challenge, their reliance on power-hungry artificial neural network backbones, which makes integration into real-world, resource-limited systems difficult. Spik- ing neural networks (SNNs) offer promising alternative. Because they operate causally (i.e. only on present and past inputs) these models are suitable for real- time use, and their low energy demands make them ideal for centrry-oonttrained environments. To this end, we introduce Spikachu: a scalable, causal, and energy- efficient neural decoding framework based on SNNs. Our approach processes binned spikes directly by projecting them into a shared latent space, where spiking modules, adapted to the timing of the input, extract relevant features; these latent representations are then integrated and decoded to generate behavioral predictions. We evaluate our approach on 113 recording sessions from 6 non-human primates, totaling 43 hours of recordings. Our method outperforms causal baselines when trained single sessions using between 2.26x and 418.81x less energy. Further- more, we demonstrate that scaling up training to multiple sessions and subjects improves performance and enables few-shot transfer to unseen sessions, subjects, and tasks. Overall, Spikachu introduces a scalable, online-compatible neural de- coding framework based on SNNs, whose performance is competitive relative state-of- sf-te-ofthhe-- --tthhartt art models while consuming orders of magnitude less energy.
Introduction
Brain-computer interfaces (BCIs) are opening new frontiers in assistive technology, particularly for those affected by severe neuromotor disorders [1-10]. The implantation of miniaturized BCI devices in the brains of patients suffering from debilitating disorders, such as limb loss or ALS, enabled them control computers and smartphones using only their thoughts, vastly improving their quality of life [1 [11313]. Latest advances have even restored speech-based communication in individuals who had lost the ability to speak by reconstructing their intended words directly from neural activity [14, 15).
"Equal contribution. The order of co-first authors was determined by a coin flip and fully interchange- able. Contact: (gment, asmanis) ) @ sessa..upemmccd
'Equal contribution. Contact: (kostas cis, vitalef@ pennmedieine) http:mmdddiiieeuuuneeeediie upenn.edu, Project page and code: sttpa://epikachu git..b.tthub...
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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At the core of every BCI system lies neural decoder, the software that maps neural signals to the user's intended actions, such as moving a cursor on a screen or controlling a prosthetic limb [16-19].
Deep learning has significantly advanced our ability to build powerful neural decoders [14, 15, 20, 21 Unlike traditional approaches that rely on hand-crafted features, artificial neural networks (ANNs) can learn the mapping from neural activity to intended actions directly from data. Despite this progress, designing scalable, perforerformance neural decoders suitable for integration into real-world BCI systems remains an open challenge with constraints along several axes, First, neural decoders need to be energy- inergy-efficient to operate within the tight power budgets of anttery-constrainee implantable devices [22-25]. Second, to enable online operation, models must be causal (i.e. rely only on present and past imputs) [10]. Third, models need scale, since scaling up model complexity and training dataset size has been shown to boost performance in the neural decoding domain [20, 21, 26-28) Finally, models should generalize to new subjects and tasks with minimal training examples to reduce the need for lengthy calibration sessions that hinder the practical deployment of BCIs [29].
While significant progress has been made along each of these axes, to our knowledge, no single framework excels across all of them simultancously. Existing approaches tend to fall into two main categories, each with its own shortcomings. On one hand, simple models, often based on traditional architectures like multi-layer perceptrons (MLPs) or Gated Recurrent Units (GRUs), tend to perform well within individual experiments. These methods are typically causal but require homogeneous input structures, making them difficult to scale or generalize across subjects [30-34]. On the other hand, more sophisticated frameworks that can be trained across datasets have demonstrated strong performance and generalization, particularly at scale [20, 21, 26-28, 35]. However, their lack of causal processing and heavy computational demands challenges their applicability outside the research lab.
Spiking neural networks (SNNs) offer a promising alternative. Their inherent causality supports integration into online systems, and their low computational footprint makes them well-suited for battery-constrained environments. To this end, we introduce Spikachu: a scalable, causal, and energy-effciient framework for multi-session multi-subject neural decoding based on SNNs. Our approach operates on binned spike trains, which are first projected into latent space shared across sessions and subjects. Temporal features are then extracted from the latents using parallel spiking networks. To capture long-range dependencies, the extracted features are processed by spiking self-attention blocks. Finally, the enriched latents are integrated and mapped to behavioral predictions through another set of parallel spiking modules.
We evaluate our approach on 113 neural recording sessions from 6 non-human primates (NHPs) totaling more than 111M spikes and 43 hours of recordings [36, 37]. Our approach outperforms causal baselines while consuming between 2.26x and 418.81x less energy when trained on single sessions. We then build unified models trained on multi-subject data that outperform single-session models, and show they can be transferred to new sessions, subjects, and tasks very efficiently. Overall, this work introduces scalable, compa--c neural decoding framework based on SNNs, whose performance is competitive relative to state-of- ofte--ofthhe-at models while consuming orders of magnitude less energy. This combination of performance and efficiency makes a promising foundation for BCIs designed for edge computing environments.
Our contributions can be summarized as:
A framework for multi-session, multi-subjec neural decoding that is scalable, causal, and energy efficient. By combining SNNs with transformers, our approach offers strong decoding performance while using minimal energy, positioning it as a compelling solution for real-ting time BCI applications.
A causal, architecture agnostic building block for multi-session multi-subject model training- We propose a novel formulation for mapping neural recordings from heterogeneous datasets into shared latent space, enabling scalable training across sessions and subjects.
A fast, architecture agnostic building block for efficient processing across temporal res- olutions. We propose a novel module capable of extracting features at multiple, distinct temporal scales based SNNs.
Pretrained models for neural decoding. We trained a unified model on the combined data from 3 NHPs spanning 99 neural recording sessions, which is transferable to new sessions, subjects, and tasks. We will make the model and code publicly available.
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2 Related Work
Neural decoding. Recent advances in deep learning have significandly improved neural decoding capabilities, with ANNs enabling impressive results across range of applications, including cursor control. prosthetic control, typing, and speech decoding [10, 14, 15, 17-19, 341. These works primarily relied on lightweight, causal architectures such as MLPs and RNNs, which performed well on dingle-sessinn datasets [38]. Relatively more sophisticated approaches based on VAEs and self-supervision have also been used with success by Liu et al. [39] and Peterson et al. [40], respectively. However, their performance degrades on new sessions or subjects due to their reliance on homogeneous input structures and known electrode correspondences across sessions and subjects.
Other studies have focused on improving cross-session generalization. LFADS was among the first to model latent dynamics across sessions and subjects [35], with extensions for large-scale training [41]. CEBRA introduced a contrastive learning approach for learning neural representations that are shared across subjects [21], while POYO used transformers to achieve multi-session, multi- subje,, and multi-task generalization [26]. POYO+ extended those results across distinct cell types and brain regions [20]. The MICrONS foundation model demonstrated that large-scale pretraining on the mouse visual cortex can yield neural representations that generalize across visual stimuli [28]. While these approaches perform impressively and generalize well, they often sacrifice causality and introduce significant computational overhead, which is unsuitable for online applications such as battery-constrained BCIs.
Spiking neural networks. SNNs are a class of neural networks that excel in processing event- driven data sequences with spikes [42]. Unlike conventional ANNs, which rely static activations like ReLU, SNNs employ bio-inspired spiking mechanisms, with the Leaky Integrate-and-Te (LIF) model being the most widely used (see App. A. Key to their success is their remarkably energy-efficient inference compared to ANNs, particularly when deployed on neuromorphic hardware [43-45]. Furthermore, their event-driven, online nature makes them well-suited for processing real-time or asynchronous data streams [46-49].
SNNs have been investigated for their potential in energy energy-efficient neural decoding [50-54]. However, prior efforts have primarily focused on shallow spiking MLPs, typically with four or fewer layers, trained and evaluated within the confines of single recording sessions. The main emphasis of these works has been on deployment feasibility on neuromorphic hardware. In contrast, our work introduces the first SNN-based neural decoding framework that not only leverages the inherent energy efficiency of spiking computation but also seales to large, multi-session, and multi-subject datasets, marking a significant step forward in the applicability of SNNs to real-world neural decoding challenges.
3 Methodology
SNNs offer remarkable energy efficiency across a variety of tasks in computer vision and natural language processing [42, 55, 56], particularly with asynchronous, event-based data [47]. Our work aims to leverage their advantages in the context of neural decoding.
3.1 Harmonizing the neural activity across sessions and subjects
BCI systems rely on microelectrode arrays (MEAs) to record neural activity at a high spatial and temporal resolution. Thanks to their small size and low impedance, individual MEA electrodes can detect extracellular action potentials from single neurons or small groups of neurons (referred to as "units") in their immediate vicinity [57, 58]. These recordings are typically represented as spike trains, which abstract away the waveform of each action potential and encode only the precise times at which spikes occur.
The spikes recorded across the electrodes of a subject are, of course, not independent. Rather, they reflect the coordinated activity of neurons that are part of distributed brain networks [26]. key challenge is interpreting these signals not as isolated events, but as components of a broader neural context [27]. This challenge becomes increasingly complex when integrating data across multiple sessions and subjects. Within a single subject, MEA recordings can drift over time [26, 32, 57, 59]. Across subjects, MEAS inevitably sample from distinct neurons, with no known correspondence between electrodes [60]. Consequently, our decoding approach must be capable of extracting
3

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Intracortical BCI system
Spike Tokens
Learnebe Latent Tokens
Spiies
Units
VU
Cross Attention
Linee
VU
VU,
Spikachu
Membrane Potential Observar Laer
VU
VU3
VU
Multi Scale SNN
Spiking Attention
sMLP
Multi Scale SNN
Lines
Ve V
Behavioral Prediction
VU,
Figure 1 Overview of the Spikachu framework.
meaningful, generalizable representations despite differences in electrode placement, neural dynamics, and recorded populations across sessions and individuals.
Tokenization. Motivated by the heterogeneity of the neural recordings across sessions and subjects, we designed a tokenization scheme that enables multi-session training. At the same time, our approach operates within timepoints to enable online usability. Inspired by Azabou et al. [26], we represent each unit as token via a learnable embedding IR Specifically, for a binned spike window, let L4 fuu, U2 Mg...... Un.. be the multiset of all units recorded during that window. Let UnitEmb(-) denote lookup table that maps each unit to its embedding. We summarize the neural activity of given time window as the sequence X = [x1, X2, X3,........ Xx..) E R where units, na
X; = UnitEmb(u,)) V 21,2. Re We note that if the ith electrode recorded repeats of UnitEmb(u:)) would be present in the sequence X for that window. This sequence summarizes the cumulative neural activity across all electrodes of subject for the given window.
Projecting units to shared latent space. We then project the sequence X into latent space that is shared across sessions and subjects using the Perceiver encoder [61, 62]. Specifically, let Zo 1 Z0.2. zo. Puoxdso be a sequence of no Iearnable latent tokens, where Zo.1 RR V 21, ...,, no). We use cross-attention project the input sequence X into the latent Z₁ E Roaxd whose length n. is independent of the length of the input sequence X. To do so, we linearly project the latent and input sequences into Queries: Q = WQ Keys: K
W K X, and Values: V WyX, and compute,
QK VTT
Z + Cross-Attn( Q, K, V) = Softmax
V.
where dy refers the feature dimensionality of K. For this operation, use the standard transformer block preceded by layer-normalization and followed by feed-forwer network [63].
Obtaining virtual units. We then unroll the latent Z, Ra + R and we project the
-iwddimensooall latent Zg E R"s using a linear layer. We interpret Zg as a new set of n. "virtual" units that are shared across sessions and subjects. We note that the weights of this layer are shared across sessions and subjects- no session or subject specific adaptation is performed.
3.2 Efficient and stateful processing of the latents using spiking neural networks
Having projected the neural activity for each time bin into set of virtual units that is shared across sessions and subjects, we could in principle process the latent Z, with any causal architecture commonly used for neural decoding such as MIP, GRU, any model surveyed by Glaser et al. [38].
Rather than relying on such conventional power-hungry ANNs, we instead leverage SNNs which are considered the third generation of neural network models [64]. SNNs are theoretically as expressive

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as ANNs and, due to their recurrent nature, are particularly well-suited for time-dependent tasks [65]. While their performance advantages remain an open area of research, their energy efficiency when deployed on neuromorphic hardware has been well demonstrated [44, 45]. This makes SNNs particularly attractive for BCI applications, where power constraints are critical due to the limited battery life of the miniaturized BCI implants.
Decomposing the neural activity across temporal streams. Given that the brain operates across multiple intrinsic timescales [66]. we sought to process the data in a similarly multi-timescale fashion. To do this, we pass the latent Ze through p parallel spiking feed forward networks (FFNs), each designed to operate at a distinct temporal resolution. Each network returns a latent Ze,- E R. by projecting Ze though sequences of the following layers.
Z++1 BN(W, (N)(Z)))
(2)
where SN₂ represents spiking activation, W, a linear projection, and BN the batch normalization operation [67]. Each network's spiking activation layers SN are initialized with independent and learnable decay constants (see App. A. effectively allowing each network to extract features that evolve as separate streams of information. We concatenate the latents from each stream into the sequence of latent tokens Zms=
[Z,,1, Z....
Ze.pl E Rexd.
Capturing long-range temporal dependencies. We then proccess the sequence Zms for informa- tional dependencies across timescales using spiking-sen self attention [55]. Specifically, we project the sequence Zms into equally shaped Queries (Q). Keys (K), and Values (V),
Q SSNg(BN((W oZz))) K SN x (BN(W Zm)s)), = WBN
where Q, K. V Rd and compute the spiking self attention matrix as,
SSA' = S. V. +);
where is scaling factor. We then compute Z E <Rpxx by projecting SSA' through the following spiking layers.
Z + SS V) SN ssa SSA))).
(5)
Following Zhou et al. [55], we precede this operation using layer normalization and follow it with spiking feed-forward network. For a primer on the main differences between spiking [55] and vanilla [63] self-attention, please see App. A.2.
Compressing the latents to a compact spatiotemporal representation. Having identified long- term dependencies across the temporal streams, we unroll the latents Z.sa E Rpxd... RP and using a spiking MLP (whose layers follow Eq. 2). we project them to a low dimensional representation Zmip hhir where daip < p- dysa The latent Zuii is a compact representation of the neural activity that is informed by the evolution of the neural code along the spatial (electrode) and temporal axes.
Smoothing predictions using multiple timescales. Having extracted neural representations that capture long-range spatiotemporal dependencies, we process the latent Zmlp with another set of p' parallel spiking MLPs that process the latents at different temporal scales (composed of layers described Eq. 2). This time, we use the parallel MLPs to extract latents Ze' E each at unique temporal scales based on the decay constant of the neurons in that network. We then concatenate the latents to obtain the sequence Zen/ B xd,"
each evolving
Tracking continuous variables through the membrane potential of spiking neurons. In many BCI applications, the behavioral variables of interest, such as the velocity of a computer cursor or the movement of a prosthetic limb, are continuous in nature. To accommodate this, our framework needs to map the spiking activity generated by the SNN layers to continuous-valuee outputs. We achieve this using a membrane potential observer layer, denoted SN obs. that never spikes and whose membrane potential fluctuates over time without resetting [52, 68, 69]. We first unroll the latents Zan' TR" xd,, R d, and then pass them through the observer layer,
Mons f- SN obs (Z
where M.k. E de' denotes the membrane potential of the observer neurons. These accumulated latents are then linearly projected to the target variable's 's output space producing Zoat E Rd the final continuous behavioral prediction of the network.
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Experiments
In this section, we validate the promise of our approach (see Fig. for causal, energy- and scalable neural decoding based on spiking neural networks.
4.1 Experimental setup
Dataset. We utilized two publicly available electrophysiology datasets summarized in Tab. 1. Altogether, the data span 6 NHPs engaged in 4 distinct behavioral tasks, encompassing total of 113 recording sessions [36, 37, 70-72]. In terms of scale, the combined dataset contains over hours of recordings. 10,000 units, 110 million spikes, and 20 million behavioral timepoints, providing a rich and diverse foundation for evaluating the scalability of our approach.
Table
Datasets used in this work. CO: Center-Out, RT: Random Target.
Study
Regions
# Indiv
# Sess
# Units
# In
# Out
Tasks
Perich et al. [36]
MI. PMd
111
10,410
111.39M
20M
CO, RT
Pei et al. [37]
312
5M
9M
RT, Maze
Behavioral tasks. Neural recordings were collected from NHPs that performed motor tasks of various complexities (see Fig. 2A and App. B). In the CO task, the animal executes relatively structured sequence: after receiving go cue, it reaches toward one of eight predefined targets before returning to the center. The RT task presents additional complexity. The animal engages in continuous, self-paced movements, with new targets appearing unpredictably across the workspace. Our goal was to decode the velocity of the cursor controlled by the animal from their neural activity.
Design choices. Throughout all experiments, we bin spikes using a 0.01 sec sliding window on 1 sec segments of data. We do not use the trial structure during model training. We report the decoding performance for CO tasks during reaching movements only, as established in Azabou et al. [26]. Spikachu's 's implementation and training details are described in App. and
Energy estimation. All energy-related results in this work are derived from the number of floating- point operations (FLOPs) required for model inference, which we convert to energy consumption estimates following well-established procedures described in Bal and Sengupta [56] and Zhu et al. [49]. Because FLOPs are hardware- aaadvarr-agnotic,, our analyses do not depend on any specific hardware implementation. All details of the energy calculations provided in App. E.2.
4.2 Performance single sessions
We began by evaluating the performance our modeling approach a single-session setting. Specifically, we trained individual models on 99 recording sessions from three animals (NHPs C, J, and M) from the Perich et al. [36] dataset. Each session ranged from 10 to 106 minutes in duration, comprising 78 sessions of the CO task and 21 sessions of the RT task. These models achieved an average per-session R2 of 0.84 and 0.68 for the CO and RT' tasks, respectively (see Fig. 2B). We also estimated the mean energy required per inference across the single-session models (see App. E.2 for details) and found it to be 5. 5.14 and 5. 5.13 for the CO task and RT tasks, respectively.
Baseline comparisons. We then benchmarked the single session mod- els against other architectures com- monly used for neural decoding, such as MLP, GRU, and POYO [26, 38]. Our approach outperformed all causal baselines (see Fig. 2C and Tab. 2), while narrowing the performance gap with non-causal models. No- tably, Spikachu was the most energy- efficient model, requiring 2.26x less
Table 2: Model performance for Spikachu and baselines. Best performing model is in bold and second best model is underlined.
Decod. Perf. (R²) 1
Energy (μJ).
Model
CO
RT
CO
RT
LSTM
0.4935
0,4214
15.08
14.94
MLP
0,7424
0,5724
12.18
12.06
POYO-causal
0.7961
0.5629
2151.65
2136,82
GRU
0,8336
0,6681
11.65
11.54
POYO
0.8937
0.6785
2151.65
2136.82
Spikachu
0.8398
0.6761
5.14
5.13
energy per inference when compared GRU (the second most efficient model) and 418.8Ix less when

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20192
6.8
Decoding
6.6
EXnTr Per
0.4
Center Out Controller: Mampulandum
101
LSTM
PONO
6A0
10HPu: C,
Bandem Target
Sers:
Merty
Doooding
04
03
Random Target Controlle:: Meniuuandum
0.0
Canora
Splasttie
Time (ted)
Time (ced)
Figure 2: Model performance single sessions. (A) Schematic of CO and RT tasks (reproduced from Azabou et [26], with permission). (B) Examples of true and predicted behavior (x, y velocities) for the CO (top 4 subplots) and RT (bottom 4 subplots) tasks. (C) Mean (= SEM) decoding performance and energy consumption for Spikachu and baselines on sessions from monkeys C, and from Perich et al. [36] dataset.
compared to POYO, which was the best performing model. Importantly, the same trend was observed when comparing the number of FLOPs required for inference for each model (see Tab. 5, App. F.5). These findings highlight the dual benefits our approach: high-performance neural decoding and improved energy efficiency, underscoring its suitability for power-constrane power-constrained applications such as implantable BCls.
We also benchmarked our model against baselines in terms of memory access costs (where Spikachu outperformed all baseline models). We refer the reader to App. F.5 for this analysis. Detailed implementation descriptions of the baseline models and the corresponding energy calculations are provided in App. C.2 and App. F.1, respectively.
4.3 Spikachu-mp: Pretraining on large amounts of data
After demonstrating Spikachu's strong performance when trained on individual sessions, we were interested in investigating whether training more data, despite the heterogeneity, could further enhance Spikachu's 's performance, a strategy that has shown benefits in prior works [21, 26-28]. To this end, developed Spikachu-mp: a multi-session, multi-subject model trained on the complete set of 99 recording sessions from monkeys C, J, and M from the Perich al. [36] dataset. Notably, this model was trained on over 40 hours of neu- ral recordings and more than 110 million spikes. In contrast to previous works (see Azabou et al. [26]), we did not increase our model's size during multi-subject train- ing. This design choice ensured that our model would remain energy energy-efficient efficient even when scaling to larger datasets, a critical consideration for resource-comeee conource-oonstrained im- plantable BCIs. Spikachu-nn per-session test set Rd of 0.80 for CO and 0.57 for the RT task, indicating Spikachu's ability to learn generalized neural representations shared
Decading Perforance
Ener gy Consumption
Lgg Eugumpee
Sattachio t0g (m)) Senzuum Sariin
SessleSession
SingleSeessoon (ju)
Figure 3: Head-to-head comparison between Spikachu-mp + finetuning vs Spikachu trained on single-sessions. (A) Decoding Performance, (B) Energy consumption per inference. Marker size encodes the number of trials available for each session.
achieved a
across sessions and subjects.

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To evaluate the utility of the learned representations, we then finetuned Spikachu- mp on individual sessions. The finetuned models achieved an average test set R of 0.88 and 0.69 for the CO and RT tasks, respectively, outerrorming models trained from scratch on single sessions (A Reo = 3.75% and A Rr = 1.71 %; Fig. 3A). The finetuned models also showed increased energy efficiency compared to single-session models trained from scratch, consuming 5.04p.J and 5.03μ.J per inference for the CO and RT tasks, respectively (see Fig. 3B). This is equivalent to saving 1.91% % and 2.01% % energy per inference for the CO and RT tasks, respectively. Together, these results suggest that leveraging cross-session representations not only improves decoding accuracy but also enhances energy efficiency compared to training single-session models from scratch.
4.4 Transferring Spikachu-mp to new subjects
Having established that pretraining Spikachu on large amounts of data enhances decoding performance and energy efficiency, we sought to determine whether the pretrained model could be effectively transferred to entirely new sessions from a previously unseen subject. To test this, we trained single- session models from scratch on 12 held-out recording sessions (6 CO and 6 RT) from a new animal (monkey T) drawn from Perich al. [36]. These models achieved a mean test set R² of 0.76 and 0.66 for the CO and RT tasks, while consuming 4.97 and 5. 5.14 per inference, respectively.
We then used Spikachu-mp to transfer the learned representations to these new sessions. The transferred models yielded improved decoding performance, with average per-session test set R² of 0.78 and 0.68 for the CO and RT tasks, respectively (Fig. 4A). In addition to higher decoding accuracy, the transferred models were also more energy energy-efficient, consuming 4.79p.J and 4.95p.J per inference (Fig. 4B). This represents a reduction in energy consumption of 3.71% and 3.63% for the CO and RT tasks, respectively, relative to models trained from scratch. Finally, we analyzed the training dynamics and observed that the transferred models converged (reached 90% of their maximum attained R²) on average 3x and 4x faster than their scratch-traid trained counterparts for the CO and RT tasks, respectively (see Fig. 4C, D). These findings suggest that Spikachu mmp learned neural representations that generalize to new subjects. providing a powerful foundation for transfer learning in BCI. In practical terms, this means that new users could benefit from robust BCT performance with minimal calibration, reducing the burden of subject-specific training and enabling faster deployment in world clinical or assistive settings.
Decoding Per Reformance
Energy Comumpttio
Center Out
Rendom Target
Spiaddommp Transfer (Pc)
Decoding Porommati (99)
Decoding Iwformence (P)
Strtch
Tia cloc Otew buseell
GingleGessinn
SingleGession ((4)
Training Epochs
ff Training Epochs
Figure 4: Performance comparison between single-session models trained from scratch vs single- session model transferred from Spikachu-my (A). Decoding Performance, (B) Energy consumption per inference, (C, D) Learning dynamics for the CO and RT tasks.
4.5 Scaling laws of multi-session, multi-subject training
Having shown that pretraining Spikachu learns robust neural representations that generalize across sessions and subjects, we next investigated its scaling behavior, specifically, how (1) decoding performance, and (2) energy efficiency change as function of the amount of data used for pretraining. In addition to Spikachu-mp, 75 sessions drawn from animals C, J, and M in Perich et al. [36], the same subjects used to train Spikachu-mp. To probe generalization and transfer capabilities, we used each pretrained model as the initialization for three finetuning conditions: (1) Seen sessions: finetuning on the same sessions used during pretraining,
trained three multi-session, multi-subject models using 20, 49, and
(2) New sessions: transferring unseen sessions from the same subjects (NHPs C, J. M). (3) New subject: transferring to unseen sessions from a new subject (monkey T).
Decoding performance and energy consumption results for the CO and RT tasks are shown in Fig. 5A, B and Fig. SC, D respectively. For comparison, we overlayed the performance of single-session models trained from scratch (gray bars). Across all conditions, seen sessions, new sessions, and new
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Center Out
Random Target
Center Out
Random Target
544
Energy Pur Infereeee (.J)
24
Decoding Derformeene
Seachh
Eessiors
Figure 5: Benefits of scaling up the training dataset size. (A, B) Decoding performance of finetuned and transferred models (performance of scratch-trained single-session models overlayed in gray) as a function of the number of sessions used for model pretraining for the (A) CO, and (B) RT tasks. Panels (C, D) show the energy consumption per inference for finetuned and transferred models (performance of scratch-trained single-session models overlayed in gray) as function of the number of sessions used for model pretraining for the (C) CO, and (D) RT tasks.
subjects, the pretrained models consistently outperformed their from- scratch counterparts. Moreover, the performance gains from pretraining scaled positively with the number of sessions used during model pretraining (as seen by the growing gap between colored and gray bars in Fig. 5A, B, and Fig. 8A, B). We observed similar trend for energy consumption per inference, reported in Fig. 5C, D. Pretrained models required less energy across all transfer settings when compared to training from scratch, and energy savings improved with the size of the pretraining dataset (Fig. 8C, D). As a bonus, we observed that all pretrained models converged much faster than models trained from scratch (see App. F.I). Overall, these results show that scaling up pretraining yielded consistent improvements in both decoding accuracy and energy efficiency, with larger datasets providing greater gains and faster convergence.
For direct, head-to head-to-head comparisons of decoding performance, energy efficiency, and convergence speed between pretrained and from-scratch models, refer to App. F.1 (Fig. and 10).
4.6 Transferring Spikachu-ng mp to a new animal, setup, and task
In Sec. 4.4 and 4.5, we demonstrated Spikachu's 's ability generalize across sessions and animals not encountered during pretraining. To further assess Spikachu's 's generalization capabilities, evaluated the model on entirely novel conditions: neural recordings from new animal perform- ing a novel task under experimental settings that substantially differed from those described in Perich et al. [36]. Specifically, we applied our framework to the held-out MC-RTT and MC- Maze datasets from the Neural Latents Bench- mark [37] (see Fig. 6A, C for schematics and App. B for task details).
Learning Curve
Decoding
Performance
Sujjer Setu))
Random Target Controller: Touch Screen
Thaniing
259
Training Epochs
Learning Curve
Decoding Performance (R")
We first trained single-session models from scratch. We used a batch size of 128 for the MC-RTT dataset and batch size of 512 for the
1
FSY Soutch
MC-Maze dataset (as this dataset is much larger). Spikachu achieved test set R² and 0.57 on the RIT task and 0.79 on the Maze task, matching the performance of other state-of-ther thet-a-rtt models (see Azabou et al. [26] for comparison). Those models consumed 5.12 and 4.86 μJ per infer- ence for the MC-RTT and MC-Maze tasks, re- spectively. We then used the pretrained weights of Spikachu-mp as basis for transferring to the datasets. The transferred model achieved R² values of 0.56 for RTT and 0.78 for the Maze task while consuming 4.96 and 4.75 μ.J per inference, respectively.
Taaning
Maze Controller: Touch
of Training Epochs
Figure 6: Generalizing to new animals, setup, and tasks (A, C) Schematic (reproduced from Azabou et al. [26], with permission) and (B, D) learning dynamics for the MC-RTT and MC-Maze tasks, respectively.

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While this approach did not yield significant performance gain, it led to markedly faster convergence (2.33x speedup for the MC-RTT and 2.66x for MC-Maze; see Fig. 6B. D) and reduced energy consumption by 3.03% and 2.25% for the MC-RTT and MC-Maze tasks, respectively. This result is partieularly noteworthy given the substantial difference in experimental conditions between the MC- RTT and MC-Maze datasets and those used in pretraining Spikachu-mp. Despite these domain shifts, the neural representations learned through pretraining proved transferable, highlighting Spikachu's s ability to generalize across animals, recording setups, and tasks.
5 Discussion
In this work, we introduced Spikachu: a causal, scalable, and energy-efficient framework for multi- session, multi-subject neural decoding based on SNNs. Unlike existing approaches, it combines the expressivity of transformers with the low-power advantages of SNNs, resulting in a system that is both high-performing and energy-conseee conscious. Our experiments demonstrate that Spikachu performs comparably to ofte-oofthhe-rt neural decoding models while offering large energy savings. We also demonstrate that pretraining our model decoding. To our knowledge, this is the first demonstration that unified models trained on multi- subject, multi-task neural data can yield improved performance in the spiking domain. Moreover, find that pretraining encourages sparsity in network activity, further enhancing energy efficiency, a critical factor for scaling up training across heterogeneous datasets.
large corpus of heterogenous neural data benefits neural
This work provides strong experimental evidence that SNNs are a practical and energy-efficient alternative to ANNs for neural decoding. While the majority of our architecture leverages SNNs to achieve substantial energy savings, it still relies on an ANN-based harmonization module to support large-scale training across datasets and subjects. Although this module represents small fraction of the total model parameters (less than 15% of total parameters), it accounts for a disproportionately high share of the model's energy. Replacing it with fully spiking alternative could yield additional energy savings. We believe that adaptations of spiking attention, such as those introduced by Zhou al. [73] or Li et al. [74], offer promising pathways to develop a fully spiking, end-to-end architecture.
Although our results provide strong evidence for spiking-basee -bsed neural decoding, real-world validation remains a critical next step. Implementing Spikachu on neuromorphic hardware and using it online experiments would ultimately test its usability in the real world. Prior works have demonstrated the fcasibility of simple online SNN decoders [50, 52-54] but scaling to more complex architectures remains formidable challenge. Encouragingly. Spikachu's 'ss resource requirements in this work are modest (fewer than 4M synapses and roughly 10K neurons) and well within the capacity of modern neuromorphic chips such as the Loihi [43] and Darwin 3 [75]. Importantly, the compact form factor of these chips is also well within the physical constraints of fully implantable BCI systems, which typically fit within the area of U.S. quarter. We hope our work inspires collaboration between neuroscience and hardware communities to address implementation barriers.
Overall, this work represents a meaningful step forward in neuroscience by introducing an energy efficient neural decoding framework based on SNNs. We demonstrate that our framework delivers strong decoding performance that scales when trained on large, heterogencous, multi-session datasets. This challenges the conventional notion that spiking networks must sacrifice performance for energy savings and highlights their potential for broader generalization [73]. Given that early demonstrations of BCI therapeutic interventions have already been shown [14, 15, 17-19), our work paves the way towards energy efficient neural decoding therapeutic interventions, bringing them closer to clinical translation.
Acknowledgments and Disclosure of Funding
We would like to thank the following funding sources for their generous support: National Institutes of Health grant ROINS121219 (FV); National Science Foundation CAREER award USS-114607 (ELD); Onassis Foundation graduate student scholarship and A. G. Leventis Foundation graduate student scholarship (GM); Bodossaki Foundation graduate student scholarship (IA).
Authors declare no competing interests.
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Appendix
A Concept Primers
A.1 Spiking neuron models
Overview of spiking model history. The simplest neuron model available for machine learning is the standard ANN neuron, which statelessly aggregates incoming signals with addition and then passes the result through predetermined non-linearity [76]. Bio-inspired models retain more of the complexity of biological neurons at the cost of being more technically involved. Amongst the first to quantify such models, Hodgkin and Huxley [77] developed a multi-parameter model describe squid axon neurons. Other works have provided detailed models to approximate different types of biological neurons. For instance, Migliore et al. [78] and Herrera et al. [79] modeled various aspects of pyramidal neurons, whereas Sherff and Mulloney [80] modeled motor neurons in the swimmeret system of crayfish."
The Leaky Integrate-and-Firt neuron. Between the simplicity of ANN neurons and fully biologi- cal neurons lie several models that trade off between the two, retaining the statefulness of biological systems while greatly simplifying the computational specifics to a much more tractable form. These models, generally termed spiking neurons, include state variables that typically correspond to poten- tials of various ion channels in biological systems. They also contain logic that dictates the evolution of these variables over time, typically expressed as a differential equation, as well as the necessary conditions for firing output spikes. The most ubiquitous such model the Leaky integrate-andTF (LIF) model, which approximates neuron with a spiking RC circuit. In this model, the tracked state is a real, scalar number corresponding to the membrane voltage of the neuron. The state vt) obeys its own recurrent dynamics, modeled integration. The differential equation for the potential with time-varying input current it).
exponential decay, to which inputs are added through
T b(t) = (tt) + R i(t),
can be solved analytically,
I ex/T R i(s) ds u(t) = e (NO + to
1/T
(8)
where the parameter controls the decay rate and R corresponds to an input resistance. A LIF neuron's 's firing behavior is then a simple matter of thresholding,
s()) = ((t))) > Vorl, u(t + 5t) = (u()) + itt) öt
sft =0 if x() 1
(9)
where s() is the spike output and 1 is the indicator function. If the membrane voltage is above the threshold Via, there will be binary spike in the output, and the state will be reset according to the reset logic; one way to implement this would be to set the membrane back to a resting potential value Vra, typically zero. Recently, it has been shown that these parameters can be learned [81]. We utilize this property in the present work to specialize different network components for separate (implicit) portions of the input signal.
Other spiking neurons. Other neuron models have found application in SNNs. In Orchard et al. [43], Resonate-and-Finn (RF) spiking neurons were used. These neurons are very similar to LIF, except that their state is complex, meaning that excitation induces an oscillatory behavior. Spiking only occurs when the imaginary part of the membrane state is zero, and the output is not binary, but instead the instantaneous value of the real part of the state. It is eventually shown that the resulting behavior is mathematically similar to the Short-Time Fourier Transform (STFT). Similarly, Xing al. [82] introduced generalized version of the LIF neuron. With integral outputs, the Generalized Integrate-and and-Fir (GIF) neuron supported the authors' saliency-based spiking large language models. These works showcase the different directions in which spiking neurons can develop.
"A collection of computational neuroscience models can be retrieved https: http:::://oolld..scieeee
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A.2 Spiking vs vanilla self attention
In this work, we use spiking self-attention (SSA) introduced by Zhou et al. [55] (an extension of the vanilla self- (VSA) introduced by Vaswani et al. [63]) which was specifically designed for SNNs. Here, we provide brief overview of the differences between the mechanisms.
Vanilla self attention. Let an input sequence X E ITXxXxD, where T, N, D refer to the number of time steps, the number of tokens in the sequence, and the dimensionality of each token, respectively. VSA relies on three components,
Queries: Q = WoX, Keys: k= WxX, and Values: V = W
(10)
and is computed as,
QK VTl
VSA(Q, K, V) Softmax
V,
(11)
where de refers to the feature dimensionality of K.
VSA can be directly used on spiking sequences. However, it is unsuitable because: (1) the float- point matrix multiplication of Q. K and softmax function (which contains exponent calculation and division operation) do not comply with the calculation rules of SNNs, and (2) computational and space requirements for VSA scale quadratically with the length of the input sequence X which does meet the efficient computational requirements of SNNs [55].
Spiking self attention. Zhou et al. [55] proposed SSA, an alternative to VSA which is more suitable for SNNs. their formulation, the sequence X T TxX is projected into equally shaped Queries (Q), Keys (K), and Values (V),
Q = SNq xX), K SN (BN(WWxx))) == (BN(Wxx) and compute the spiking self-attention matrix as,
(12)
SSA' = SW(QK T V-s).
(13)
where 8 a scaling factor. SSA then computed via the following spiking layers,
SSA(Q, V) SN ssa SSA')).
(14)
This formulation is better suited for spiking sequences than traditional VSA for several reasons. First, SSA operates independently at each time step, aligning naturally with the temporal dynamics of SNNs. Unlike VSA, SSA removes the use of the softmax function to normalize the attention matrix. Instead, it directly computes the attention by multiplying the query, key, and value sequences without additional normalization. This simplification is more efficient as softmax introduces unnecessary computational complexity. Since the spiking neuron layers SN Q and SN K output binary spike trains, the resulting attention maps are inherently non-negative. This removes the need for softmax to enforce non-negativity, as the attention mechanism already emphasizes relevant features and suppresses irrelevant ones through sparse, event-driven computation.
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B
Datasets
To train and evaluate our framework, we leveraged diverse collection of publicly available datasets spanning multiple NHPs and various behavioral paradigms.
B.1 Datasets used for training and validation
To develop model, we leveraged a rich and diverse dataset comprising 99 unique recording sessions from three NHPs (monkeys C. J, and M) engaged in two distinct behavioral tasks. These recordings were collected across four foundational studies [36, 70-72], and were later curated into a unified dataset by Azabou et al. [26]. This comprehensive resource is publicly available through Dandi (used in this work) and via the brainsets platform.
Across all sessions in the dataset, each NHP was seated in a primate chair, and a custom two- dimensional planar manipulandum was used to control a cursor displayed on computer screen. During each recording session, the NHP performed one of two structured motor tasks:
Center-oul Task (CO): In this classic reaching task, the monkey initiated movement from central target toward one of eight peripheral targets arranged uniformly around a circle with 8 cm radius. After holding the center target for variable duration, an auditory "go" cue signaled the monkey to move to designated outer target. This task is widely used to investigate neural processes underlying movement planning, preparation, and execution.
Random Target Task (RT): Similar in design to the CO task, the RT task involved targets that were randomly distributed across the workspace rather than arranged in a circular pattern. The monkey instructed, via auditory cue, to move sequentially between four randomly positioned targets, introducing greater variability in movement direction and distance.
The behavioral sampling rate used across all sessions within this dataset was 100 Hz. We used 70% of the data from within each session for training and 10% for validation; the remaining 20% was held-out for testing.
B.2 Datasets held out for testing
We reserved 20% of the data from each session described above for testing. In addition, fully held out all sessions from Monkey T, comprising 6 CO and 6 RT sessions, to assess the model's ability to generalize across subjects.
To asses the model's ability to generalize to novel tasks, we also held-out two standardized datasets from the Neural Latents Benchmark [37], MC-Maze and MC-RTT, which are described in detail below. In both datasets, the animals (different for MC-Maze and MC-RTT) performed the tasks by swiping on touch screen instead of using manipulandum.
MC Maze: In this task, the monkey performed delayed reaches to visually presented targets while navigating around the boundaries of virtual maze. The dataset includes wide range of behavioral configurations, each defined by unique combinations of target positions, barrier counts, and barrier placements. This diversity results in both straight and curved reach trajectories. With thousands of trials, MC Maze provides rich substrate for analyzing population- level neural dynamics. Moreover, its delayed reaching design facilitates a clear dissociation between neural activity related to movement preparation and execution.
Random Target Task (RTT): In this task, the monkey executes a variant of the RT task described previously. Instead of performing reaches separated via auditory cues, the animal completes continuous, point-to-poin reaching movements between virtually presented targets. The movements begin and end various locations, span variable distances, and include only a few repeated trajectories. This variability makes RTT particularly useful for evaluating model's 's ability generalize across complex and less stereotyped behaviors.
The behavioral sampling rate for the RTT and Maze tasks was 1000 and 100 Hz, respectively. To standardize the sampling rate across all datasets used in this work, we downsampled behavioral samples for the RIT task to 100 Hz.
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C Model Implementation Details
C.1 Spikachu implementation details
Spikachu is general framework that supports various implementations. Here, we outline the one used this work.
Learnable embeddings for units. We assign each unit a unique 32-dimensional learnable embed- ding.
This is the ANN part of our architecture described in Sec. 3.1. The cross-attention block implemented as described in Jaegle et al. [61] and is initialized with 128, 32-dimensional learnable latent queries. The linear layer following the cross-attention block projects the latents to 128-dimensional vector.
Harmonizer.
The 128-dimensional latent vector is passed through 3 parallel spiking MLPss Each MLP composed of 4 layers and projects the 128-dimensional input vector to a 256-dimensional latent vector (all hidden layers and output layer have dimensionality of 256). Each MLP has neurons whose membrane potential dynamics evolve at distinct temporal scales. We initialize the learnable decay constants of the neurons of the parallel networks values of 1.11, 1.46, and 434.79 and allow them to update freely during model training.
Multi Scale SNN-I.
Spiking Sell-Attention. The three 256-dimensional latents extracted by Multi Scale SNN-I are arranged in a sequence and processed using spiking self-attention as described by Zhou et al. [55]. The input is projected to equally shaped dindimenii keys, queries, and values prior to spiking self-attention which is carried out using 8 heads. The latent is then passed through 2-layer spiking feed forward network with hidden size of 256. Residual connections are employed after the attention calculation as well as after the feed forward network, as is employed in Spikformer [55].
Spiking MLP. The three 256-dimensional outputs of the attention operation are unrolled and are projected to dimensionality of 384 via one linear layer followed by spiking activation.
Multi Scale SNN-II. The 184-dimensional latent is passed through 2 parallel spiking MLPs composed of layers each. The hidden size for each MLP is set to 384. The learnable decay constant for the neurons of each of the two parallel MLPs is set to an initial T of 1.11, and 434.79. Following processing, the latents from the 2 parallel MLPs are concatenated, resulting in latent with dimensionality 768.
Membrane potential observer layer. The 768-dimensional latent is then passed through spiking activation. Instead of tracking the output of the neurons of this activation layer (they never fire), we keep track of their membrane potential instead. This results in latent of the same dimensionality of 768.
Readout layer. The 768- -i8demmnnional latent then projected to 2-dimensional vector, which is our network's 's velocity prediction (Ug, vy), using a linear layer.
C.2 Baseline model implementation details
We introduced several baseline architectures for our experimental comparisons, which we describe in detail below.
MLP. Our MLP baseline comprises seven consecutive hidden dense layers of ANN neurons, with input shapes: [100, 256, 512, 1024, 1024, 512, 256). We used the ReLU nonlinearity and dropout probability of 0.1. Since the model is not recurrent, we supplied a rolling window of 10 consecutive binned spike windows during inference.
GRU. Our GRU baseline comprises four sequential blocks, as implemented PyTorch. Each block contains four cells, with hidden dimension of 164. We used dropout probability of 0.1.
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LSTM. Our LSTM baseline comprises four sequential blocks, as implemented in PyTorch. Each block contains four cells, with hidden dimension of 162. We used dropout probability of 0.1.
POYO. Our POYO baseline was implemented using the model coded by Azabou et al. [26], made available at https://github. com/neuro som//euro-galaxy/pyyy We trained the model using N=256 latent tokens each with dimensionality of 128.
Causal POYO. Our causal POYO baseline is implemented using the model coded by Azabou et al. [26]. On top of the provided model, we added causal masking in all the model's transformer blocks. Specifically, for each attention module described in Azabou al. [26], let Q be the set of the set M key vectors with
N latent query vectors with timestamps 1, = I..... N,, and timestamps 1 =1 I,.... M.. We dynamically masked the attention multiplication QKT such that E < L for all pairs i, for which the attention matrix is computed. This is direct generalization of lower-triangulan masking of the attention matrix commonly used in transformers to prevent tokens from accessing future tokens, effectively making the operation causal. The masks used for masking the (I) encoder cross-aties coss-attentionn block, (2) each self-attention block, and (3) the decoder cross-attention block used in causal POYO are shown in Fig. 7.
200
C
Decoder noss-Altention
250-
33
Figure 7: Masking used to make POYO Causal. Mask used (A) encoder gross-attention, (B) self-attention, and (C) decoder cross-ateention blocks.
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D
Model Training Details
All models were coded in Py'Trrch [83]. The spiking components of Spikachu were implemented using SpikingJelly [84].
D.1 Training hyperparameters
All models were trained using LAMB optimizer [85] with weight decay set to 10 The learning rate was initialized at x 10-3 and held constant for the first 75% of training epochs and was decayed to zero using a cosine schedule for the remaining 25%. Single-sessing models were trained for 1000 epochs with batch size of 128, while multi-session models were trained for 400 epochs with batch size of 512.
D.2 Compute
Model training was conducted multi-GPU cluster. Depending on resource availability, single- session models were trained using one of several NVIDIA GPUs, from the following: L40s, L40, A40, 3090, A6000, 2080 Ti, and A10. Training these models required under 1IGB of GPU memory and typically less than an hour, with a few sessions taking up to 5 hours when trained on 2080 Ti graphics card. Finetuning to individual sessions was performed on the same range of GPUs and took approximately the same amount of time as training from scratch.
Training of multi-session models was performed multi-session models, including Spikachu-mp our most computationally intensive model, completed training in under 48 hours.
NVIDIA A40 GPU with 4SGB of memory. All
D.3
Data augmentation
For all model training (both single-session and multi-session) we employed unit dropout augmentation, introduced by Azabou al. [26]. this approach, random subset of the recorded neural population is sampled within time windows and used for training, effectively simulating variability in unit availability. To prevent the augmentation from being overly destructive, we enforced a minimum population size of 30 units per window.
D.4 Training objective
All models were trained by minimizing the MSE loss between the true and predicted hand velocity sequences of the NHPs performing the various behavioral tasks. For the RT task, all behavioral timepoints were weighted equally during training. For the CO task, we increased the weight of the prediction during the reaching segments of the movements by factor of has been done in previous works [26, 86].
D.5 Evaluation details
Although model training is performed arbitrary segments of neural data, evaluation follows standardized protocols established in prior works [26, 37, 87]. For CO and MC-Maze, we report decoding performance only during the reaching phase of successfully completed trials. For RT, which includes a hold period followed by a sequence of 3 to 4 random reaches, we likewise evaluate performance only during the reaching movements. Since movements continuous for MC-RTT, we report decoding accuracy across all available segments.
D.6 Surrogate gradients
Spiking neurons are inherently con-differentiabee due to their discrete activation function (the Heaviside step function) which presents fundamental challenge for gradient-based backpropagation. To overcome this limitation, we adopt a surrogate gradient approach, replacing the non-differentiable Heaviside function with the arctangent (atan) function when calculating the gradients used for backpropagation. This surrogate function has been demonstrated to support the stable training of deep SNNs in prior works [84, 88].
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D.7 Smoothing
Prior to gradient computation, model predictions were smoothed along the temporal axis using moving average with window size of 20 samples. To ensure a fair comparison, identical smoothing was used across all models.
D.8 Finetuning/Transteriing details
The training procedure for finetuning and transferring Spikachu-mp to individual sessions is identical that of training single-session models from scratch, except: models trained from scratch were initialized randomly (using seed 42); finetuned and transferred models were initialized with the pretrained weights from the multi-session pretrained models, In both cases, all learnable parameters were updated throughout training.
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E
Energy Analysis
Estimating energy consumption for any machine learning model consists of approximating the total amount of floating-point operations (FLOPs) necessary to execute the forward pass of the model. These are split into two categories, multiply-accumull MAC operation multiplies two operands and adds the result to an accumulator. Symbolically, if æ, are the operands and a the accumulator, a MAC is defined as: f- a a x y. An AC operation acts on single operand by adding it to accumulator. If is the operand and is the accumulator, an AC defined as: a f- a
(MAC) and accumulate (AC) operations.
Each instance of these operations consumes a certain amount of energy, and by summing, we can estimate the total energy consumption of model's forward pass. As quantified by Horowitz [89], performing ACs is cheaper than MACs in hardware, specifically: EMAC ~ 4.6p./, EAC = 0.9pJ. Therefore, model's total energy becomes: Evaal NAC EAC + NMAC EMAC. The N variables represent the total count of the respective operations in the architecture.
For reference, matrix multiplication of the form A B, A R=" B E Rm requires (n m MAC operations, and vector addition dimension requires AC operations.
E.1 Baseline energy calculation
Here, describe the methodology used to compute the energy required per inference for each baseline model in this work.
E.I. MLP
Each linear layer of an MLP is parameterized by weight matrix W E RR and bias vector R" Given an input x E R" the operation performed is: z = Wx +b. The energy expenditure of an L-layer MLP is therefore,
FIILP Σ
19 n' FMAC + =n! FAC,
(15)
1=1
where I E (1...., L} is the layer index.
E.1.2 GRU
The equations for a GRU cell are described in detail by Chung et [90]. Briefly, each cell can be expressed as,
r. oWint + byr + Worlue-1) + bnr);
22 oW biz + Whsh(t-1) + bnz):
n4 Lanh(W,,)) Wn + bix + re O (Wonll(e-1) bbn)),
h4 (1 2.) n. 2x @ h(-1))>
where x the input, W-variables refer to weight matrices, I-variables to bias vectors, is the hidden state, the timestep index, denotes variables acting directly on the input, and r, 2, are the reset, update, and new gates, respectively. We also denote the sigmoid function with a and the Hadamard product with O. From these equations, we can calculate the total operations performed in each cell as function of its input and hidden state sizes as,
GRU 3 (nx (n. + na) + my) + 4n and G-GRU = 6n
(16)
where n; is the input size and n,, is the hidden size. For the full module comprising multiple cells, we sum the counts of the above operations for each cell to obtain the total energy.
EGRU = Ng
EMAC Σ
N. ANRRCC (c) + EAC Σ CNNGR (c) MAC
GRU
EMLP,
(17)
where e an index over the cells each block, NB is the total number of multi-cell blocks, and EMIP represents the energy of MLP used for readout, with energy given by Eq. 15.
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E.1.3 LSTM
The exact implementations of the LSTM architecture can be found in Sak et al. [91]. Briefly, each cell can be described as,
4 (War, + b + (n + bni),
ft (Wi + b, + Wash + bnt);
9t tanh(Wig + big + (t + bng):
0: aWio. + dio + (hoh + bho);
C f. c(-11) + gt,
h4 0 tanh(c:),
where x the input, W W-variables refer to weight matrices, b-variables to bias vectors, is the hidden state, the timestep index, i, f, are the input, forget, cell, and output gates, respectively, and is cell state. From these equations, the total operations can be counted as,
MAC 4 (na, (n, m4) mh) + 3na; and AC 9m
(18)
where ny is the input size and n, is the hidden size. For the full module comprising multiple cells, we sum each cell's 's counts of the above operations to obtain the total energy,
C
(AST((() (0)
ELSTM = -BB EMAC Σ
L (c) EAC Σ NASTT
EMLP,
(19)
where e is an index over the C' cells in each block, NB is the total number of multi-cell blocks, and EMPP represents the energy of small MLP used for readout, with energy given by Eq. 15.
E.1.4 POYO
Attention. We will first describe how we calculate the energy for a single attention module, based on which POYO is built [26]. To compute the energy requirements the attention operation (see Eq. II), we cache the dimensions of the query, key, and value tensors at runtime. We can then precisely calculate the FLOPs necessary for the matrix multiplications, scaling, and/or softmax operations performed in attention. The following derivation explains the energy requirements for single cross-attention block.
Let L,, L2 be the input and output sequence lengths respectively, E the embedding dimension, Q TR X the query tensor, K R E the key tensor, and V E R the value tensor. The computational requirements following Eq. for a single attention head, are computed as follows,
QKQTT MAC
=EL1L2,
MAC 3L, N DL3 +
N.
L2 ((E + 3) + DL2).
For multi-head attention, we need only multiply the above by the number of heads. Letting be number of heads, the total energy for the cross attention operation is,
ECx = H FMAC M
(20)
Note that the cost for projecting inputs to Q K. V and projecting outputs to the appropriate down- stream shape is computed separately (linear layers, computation follows the logic 1.1.1).
POYO. By iteratively applying Eq. 20, computing the energy requirements per inference for POYO becomes straightforward. For each attention block, we need account for the attention energy, and also the energy necessary for projections, given by Eq. 15. Specifically,
Eproj b + EiKK + EiY + Liproj:
(21)
where the summands correspond to the energy required for the query, key, value, and output projec- tions respectively. The total energy requirement for POYO can now be expressed as,
Erovo (EEA + ) + NsA (ESA + ESA)) + (E + (CA) + EMLP,
(22)
For self-attentie attention, need only set the sequence lengths to be equal; the derivation is otherwise identical.
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where N is the number of consecutive self attenttenion blocks used in the model. We note that one more subcomponent exists for this model, namely positional encoding using RoPE [92]. However, since the operations involved in computing the positional encoding are very limited, we did not take them into account when estimating POYO's energy requirements.
E.2 SNN energy
Computing the energy requirement for SNN follows the same principles used for ANNs. However, SNNs achieve energy savings via two properties of their processing: (a) not all neurons spike at cach point in time, and (b) in the of binary spikes (unit magnitude), matrix multiplications simplify to indexing and accumulation (addition) of elements of the weight matrices. These savings become tangible during inference when deploying specialized neuromorphic hardware [44, 45]. Following [49, 93-95], we calculate these savings while training and running our models on GPUs. To achieve this, careful accounting is performed of the proportion of neurons that spiked each spiking neuron layer for given forward pass of model. That rate subsequently reduces the next layer's 's FLOP count. For example, if the next layer is dense, the FLOP count for that layer reduced proportionately the spiking rate of the given layer. Furthermore, as was explained earlier, ACs in hardware are cheaper than MACs. This another source of savings for SNNs, where each spike's 's forward computation is significantly cheaper than a whole floating-point MAC.
E.2.1 Spiking MLPs
Here we describe the energy required per inference for a spiking MLP. Given a spiking MLP of L layers, let T denote the number of simulation steps used update the state of each neuron. To clarify, T >>> 1 values are commonly used in SNN works to signify that the same, time-independent input (e.g., static image) is presented the SNN T times in succession, allowing neuron dynamics to converge. The result of the network is then obtained by aggregation of the output layer's values over this artificially introduced time dimension. In our case, not need to be repeated, due to our data's 's inherently temporal nature. Furthermore, let PI-1 E [0, 1] be the proportion of the previous layer's neurons that spiked, and E; be the energy consumed by the 1-th layer. Also let Ew denote the energy per algebraic operation for a dense layer with weight matrix W E RR, xno and bias vector b E RR" with n,, n. being the input and output shapes respectively.
since each sample of the input does
Then, for batch size of 1, the equations for the energy consumption of a spiking MLP are as follows,
(1) ESMLP Σ T. PI-1 n n0 Ew + t! EAC.
(23)
where E (I.2....,N? - 1] is the layer index. This is similar Eq. 15, the only differences being that there are spiking rates to help reduce the computational load, and that input values are binary spikes, meaning that: Ew = Exc < EMAC- The latter holds because matrix-vectar multiplication can be expressed purely in terms of indexing and accumulation when the vector is binary.
E.2.2 Spiking self-attention
We calculate spiking self-attention energy based on the formulation in App. A.2. Let L be the sequence length, E the embedding dimension, Q E _ the query tensor, K R2xE the key tensor, and V E R L the value tensor. The computational requirements following Eq. 13, for single attention head, are computed as follows.
NºK E, NMA = L? NY. = L2 AC
NSSSA = 1,2 (F D), and MAA = 1,2
For multi-head attention, we need only multiply the above by the number of heads. Letting be the number of heads, the total energy for the cross attention operation is,
EssA = H (EMAC S.SSA + EAC -NSSA)
(24)
Note that the cost for projecting inputs to Q, K, V and projecting outputs to the appropriate down- stream shape is computed separately (spiking linear layers, computation follows the logic of E.2.
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E.2.3 Spikachu
To compute the total energy consumed by Spikachu during forward pass, we compute the sum of the energy of its modules, described below.
This is CA block followed by a linear layer. Its energy is computed following Eq. 20, with the addition of the energy cost of the projection layers (denoted En EK E ", and the extra linear layer (denoted E.
Harmonizer.
En = EMAC NCA + ER
Ex +
SMLP En
(25)
where each of the summands outside the parentheses is direct application of Eq. 15. Multi Scale SNN-I.
This is a combination of parallel spiking MLPs, based on Eq. 23,
F
Σ
1 nn + + SMLP = FAAC P. ET P1-1 m4
(26)
1=1
where is the number of parallel spiking MLPs and I E {1...., L} is the layer index.
Spiking Sell-Attention. SSA is implemented as described in Sec. A.2, with energy computed based on Eq. 24,
F. = H (FMAC
NSSA + FACC NSSA), + MAC
"SA
+
FK SSA
F +
Fout
"SA
(27)
where the added summands are spiking MLPs used for projections, their energy calculated using Eq. 23.
Spiking MLP. Mixing module, energy given by Eq. 23, denoted Fmm-
Multi Scale SNN-II. Same as Multi Scale SNN II, denoted Eg.2.
Membrane potential observer layer. Layer containing only leaky integrators, energy per time-step is 1 AC operation per neuron,
E. = no EAC:
(28)
where n, is the number of observer neurons.
Readout layer. Single linear layer, equivalent to single layer of Eq. 23, denoted by E,,
Thus, the total energy per inference for Spikachu is given by,
Espikacha En + E..1 E. + Em + Es, + Eo Er.
(29)
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Additional Results
F.1 Scaling laws of multi-session, multi-subject training: Continued
In Sec. 4.5, we demonstrated that pretraining Spikachu on multi-session, multi nulti-subject datasets improves both decoding performance and energy efficiency. Here, we present additional evidence that complements those findings.
Random Target
Center Out
Random Target
Center Out
Differncce (DR) (DR)
One Offfeeti Per Inffeettoo (%)
Perfomrncc
100
Figure 8: Benefits of scaling up training dataset size. (A, B) Performance difference between the single-subject models trained from scratch vs fintuunedtransferred models from pretrained for the RT, and CO tasks. (C, D) Percent difference in energy consumption between the same groups for the same tasks.
Fig. 8A, shows the improvement in per-session test decoding performance achieved by the pretrained models after (1) finetuning on sessions seen during pretraining (monkeys C, J, and M; blue), (2) transferring new sessions from animals seen during pretraining (monkeys C, and M; skyblue), and (3) transferring to new sessions from a new animal, unseen during pretraining (monkey T: orange). Across all conditions, observe consistent performance gains that increase with the number of sessions used for pretraining, with gains possibly saturating beyond 75 sessions.
In Fig. 8C, D, report the relative reduction in energy consumption per inference,
Emns
E.s Egs
AE
where Fimst is the energy required per inference by the pretrained model after finetuning, and Fess the energy used by a single-session model trained from scratch. Across all finetuning conditions, energy savings scale positively with the size the pretraining dataset. Given that typical BCI sessions require tens of thousands of inferences per session, these efficiency gains are highly practical in real-world setting.
# Sessions:
Sessions: 49
# Sessions:
Sessions: 99
Mutii supject Finctune/Traaat Transffer (87
Squlor
Single Session (RO)
Single Session (R))
Single Scssion (RS)
Single Session (RS)
Findtuner Transfer (pa))
Mul Supject
2.5
15
55
Single (pJ)
Single (pJ)
Single (jJ)
Single Session (pJ)
Figure 9: Decoding performance and energy benefits of scaling up. (A, B, C, D) Head-to-head decoding performance comparison between single-session models trained from scratch vs (1) the pretrained models finetuning (seen; blue) and (2) pretrained models transfer (new session: skyblue and new subject; orange). (E, F, G, H) Head-to-head comparison of the energy consumed per inference for the same groups.
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To further illustrate these benefits, Fig. 9A-D presents head-to-head comparisons of decoding performance between finetuned models and their scratch-trained counterparts for pretraining on 20, 49, 75, and 99 sessions. In each case, the finetuned models outperform their baseline equivalents, with the performance gap widening as pretraining data increases. Figs. 9F-H compare energy usage per inference under the same conditions. Here, we observe that finetuned models consistently consume less energy than their scratch-trained counterparts, emphasizing the energy efficiency achieved through pretraining. The energy savings also scale positively with the amount of pretraining data.
Performance (P)
14
82
0.2
04-10
230
407
606
1083
60
430
463
62
600
469
1000
30
353
86
130
Training Epochs
Training Epochs
Training Epochs
Training Epochs
2.
1.0
1.0
Performance (RF
0.0
IT
IT
I1
0T
Training Epochs
Training Epochs
Training Epochs
Training Epochs
Performance (R)
0.2
00
C4
06
04-
20
CD
00
430
830
Training Epochs
8 Training Epochs
Treining Epochs
Treining Epochs
Performance (99)
04
32
200
400
221
300
200
200
430
653
302
1000
30
200
400
630
680
1300
300
400
600
060
1000
Training Epochs
8 Treining Epochs
0 Training Epochs
Training Epochs
Figure 10: Learning speedup achieved through pretraining. (A) Leaning Curves for the CO task for (1) single-session models trained from scratch (gray), (2) the pretrained models finetuning (seen; blue), and (3) pretrained models + transfer (new session; skyblue). (B) Leaning Curves for the RT task for (1) single-session models trained from scratch (gray), (2) the pretrained models finetuning (seen; blue), and (3) pretrained models + transfer (new session; skyblue). (C) Leaning Curves for the CO task for (1) sungle-session models trained from scratch (gray), and (2) pretrained models + transfer (new subject; orange). (D) Leaning Curves for the RT task for (I) single-session models trained from scratch (gray), and (2) pretrained models transfer (new subject; orange).
Finally, Figs. 10A-D show the mean learning curves under the same three finetuning conditions, alongside the baseline learning curves of scratch-traines single-session models (gray). Across all levels of pretraining, finetuning the pretrained models to single sessions converges much faster than training single-session models from scratch. This demonstrates that the pretrained models learn transferable, general-purpott neural representations shared across both sessions and subjects that can be transferred very efficiently.
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F.2
Ablation study
F.2.1 Building blocks
To identify the contribution of each building block of Spikachu to the decoding performance of our models, we performed an ablation study using the 99 recording sessions from monkeys C, J, M drawn from Perich et al. [36]. Specifically, time removing a building block and assessing the model's decoding performance. To ensure fair comparison, training hyperparameters were kept identical across all training runs.
trained variants of proposed architecture, each
The building blocks of our architecture are described in App. C.I (see also Fig. 1). For convenience, we also list them here: Harmonizer (Harm.), Multi scale SNN-I (Multi-Scale I), Spiking Self Attention (SSA), Spiking MLP (sMLP), Multi scale SNN-II (Multi-Scale II), Membrane Potential Observer Layer, Readout Layer.
We note that by ablating the multi-scal SNNs we used one spiking MLP instead of multiple parallel spiking MI.ss. We did not ablate the sMLP because there is straightforward way to project the output of the SSA block to the input dimensionality of Multi Scale SNN-II without For the same reason, we did not ablate the "Readout Layer" (needed to project the output of "Membrane Potential Observer Layer" to the output dimensionality of the cursor velocity). We also did not ablate the "Membrane Potential Observer Layer" because there is no straightforward way to track continuous variables (as is the velocity tracked this work) without this layer when using SNNs.
The results of the ablation study when training Spikachu and variants in single sessions are shown in Fig. 11A, We observed that the spiking components of the architecture ("Multi Scale SNN-I" and "Multi Scale SNN-II"" influence model performance most, indicating that our model did not rely on the ANN part (the harmonizer) to perform. We also observed that the performance difference when ablating the SSA block was negligible. To further investigate the utility of the SSA block, we trained variants of our architecture with the SSA block ablated on multiple sessions. We observed that the SSA block did affect the model's 's performance when scaling up (see Fig. 11C, D for model performance on the validation set when training on sessions).
Deccding Performance (R)
Center Out
6
Random Target
Center Out
D
Random Target
Single Seeston Model
Muhi Seatioon Modal
Decoding
Fdll
Harm.
Motti
scale
Multi-
scate
Full
Herm.
Multi
scale
SSA
Mutti-
scale
Full
SSSA
Full
-SAA
Figure 11: Summary of ablation results. (A, B) Single session training decoding performance (mean + sem) in the validation set for the (A) CO and (B) RT tasks. (C, D) Multi-session training decoding performance (mean + sem) in the validation set for the (A) CO and (B) RT tasks for models trained on 75 sessions from animals.
F.2.2 Spiking mechanism of neurons
To determine whether our model's per- formance was driven by its spiking mechanism or simply by network con-
Table 3: Model performance for Spikachu and ANN Variants.
Decod. Perf. (R²) )
Model Variant
nectivity, we conducted an additional ablation study. In this experiment, we re-implemented Spikachu as an ANN, preserving the exact same connectiv- ity but replacing stateful LIF neurons with stateless ReLU neurons, thereby removing any intrinsic temporal dynamics. We refer to this variant as Spikachu-ANN.
CO
RT
ANN
0.5332
0.3642
ANN + context
0.5348
0.3643
SNN
0.8398
0.6761
We evaluated Spikachu-ANN on the same 99 recording sessions from monkeys C, and M from Perich et al. [36] described Section 4.2, considering two different training conditions: (1) using only the current timebin (identical to the setup for Spikachu; ANN in Tab. 3), and (2) providing
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additional temporal context by including the four preceding timebins as inputs (ANN + context in Tab. 3).
As summarized in Tab. 3, Spikachu (SNN Tab. 3) consistently outperformed its ANN counterpart under both conditions. Importantly, even when explicitly supplied with temporal context, Spikachu- ANN underperformed the spiking model. These findings demonstrate that Spikachu's performance arises from the LIF neuron's spiking dynamcis and is not due network connectivity alone.
F3 Testing the utility of the neural "harmonizer" baseline models
In this work, we introduce the neural harmonizer, a novel, causal method for aligning heterogeneous neural recordings across sessions and sub- jects, detailed in Sec. 3.1. This ap- proach addresses a key barrier to scal- ing neural decoding models to multi-
Table 4: Impact of harmonizer on model performance.
Center Out (R+)
Random Target (R-)
Model
Harm.
+ Harm.
Harm.
+ Harm.
LSTM
0.4935
0.5804
0.4214
0.5919
MLP
0.7424
0.6415
0.5724
0.5229
GRU
0.8336
0.8187
0.6681
0.6110
session and multi-subject datasets: the reliance of traditional architectures, such as MLPs and GRUs, on homogeneous input structures and consistent neural correspondences. By projecting the disparate neural signals from different datasets into unified representation, the harmonizer enables any standard model (see Glaser al. [38] for an overview) to be trained effectively across sessions and subjects, which could enhance the model's generalizability.
Although the homogenizer was developed as part of the Spikachu framework, we demonstrate its broader applicability by integrating it with baseline models (LSTM, MLP, and GRU) and training them across all 99 neural recording sessions from monkeys C, J, and M in the dataset from Perich et al. [36]. The corresponding test set results, reported separately for the CO and RT tasks, are shown in Tab. 4. The results indicate that the standard models perform well when paired with the neural harmonizer and even see performance gains in some cases. We note that we did not perform any hyperparameter tuning for the harmonizer or the baseline models when combining them into single pipeline. Instead, we used the harmonizer configuration optimized for Spikachu and the baseline models as trained in the single-session setting described Sec. 4.2. This highlights the potential of our approach to scale model training across multi- session, multi-subject neural datasets, offering flexible and powerful foundation for generalized neural decoding.
F.4 Representation analysis of the unit embedding space of Spikachu-mp
In this section, investigated whether any structure emerged in the latent space of Spikachu-mp (see Sec. 4.3), the model we trained on 99 recording sessions from monkeys C, J, M performing the CO and RT tasks from Perich al. [36].
Since our model does not have any explicit session-specifi embeddings, for this analysis, we used the unit em-
Clustering per Animal
Clustering per Task
Linear Discrtminant
beddings for each electrode of each
session. To investigate whether mean- ingful structure emerged in the latent space of our trained model, we aggre- gated the unit embeddings within each
session into a 2D matrix and applied PCA. retaining the first five principal components as a summary represen- tation for each session. We then used these session- representations as features in two distinct Linear Dis- criminant Analyses (LDA): one to as- sess separability by subject (monkeys J. and M) and another by task (Center Out vs. Random Target). Visualizing the session embeddings
-2 Linear 2iscriminant
Linear Discriminant
Figure 12: Linear Discriminant Analysis of session embed- dings. Visualization of session- embeddings in the la- tent space when maximizing separability for (A) Subject, (B) Task.
projected into the IDA space revealed clear clustering both by subject (see Fig. 12A) and by task (see Fig. 12B).
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To quantify the separability, we trained an SVM classifier (using scikit-leam's SVC with default parameters) using 5-fold cross-validation with features the summary representations of each session (the first five principal components of the aggregated unit embeddings; same those used in LDA) to predict (1) the subject and (2) the task associated with each session. We achieved an average accuracy of 0.69 = 0.02 for subject classification (chance 0.33) and 0.79 + 0.02 for task classification (chance 0.50), suggesting that Spikachu learned latent representations that reflect high-level structure in the data even though it is not explicitly trained to do so.
F.5 Profiling Spikachu in terms of memory access costs
In Sec. 4.2, we benchmarked Spikachu's performance against various baseline models commonly used for neural decoding. In this section, we provide comparisons between Spikachu and baselines terms of computational and memory-access costs per inference. We note that this comparison is important since the total energy expenditure per inference consists of the energy required for computation, as well as the energy required to load (LOAD Ops) and store (STORE Ops) data in memory.
For this analysis, used the 99 single-session models trained on the recording sessions from monkeys C, J, and M performing the CO and RT tasks from Perich al. [36] (models produced by the experiment in Sec. 4.2). For each model and session, we calculated the number of FLOPs (i.e. MACS, and ACs) required for infer- ence (see Sec. E). Our findings for the number of necessary operations for each model are presented in Tab. 5. Notably, Spikachu has higher proportion of AC operations compared other models, due to the binary output of each spiking neuron layer, which simplifies downstream computation. In conclusion, Spikachu requires the least amount of such operations, thanks to its SNN backbone.
Table 5: FLOP counts for Spikachu and baselines. Best performing model in bold and second best model underlined. M stands for millions. Model
MAC
AC +
Total FLOPs (M)
LSTM
3.27
0.02
3.29
MLP
2.63
0.02
2.65
POYO-causal
466
1.0
467
GRU
2.53
0.01
2.54
POYO
466
1.0
467
Spikachu
0.97
0.78
1.75
Next, we use the aforementioned results to estimate memory access requirements by converting FLOPs to LOAD and STORE operations. As described in Liao al. [52], we assume NI.OAD LOAD and / STORE operation per AC. Therefore, memory operations can be estimated as,
STORE operation per MAC and NLOAAD 2 LOAD and AC
1
LOOAD
L-LLAD, -MMA + -LOAD NNA
(30)
MAC
N STORE
STORE
NMAC + AC
NSTORE -NAC
(31)
MAC
where NAC and NMAC denote the total number of AC and MAC operations for given architecture, respectively.
The results, averaged across all ses- sions and tasks for Spikachu and base- lines, are summarized in Tab. 6. No- tably, Spikachu required the fewest memory access costs across the board, highlighting its energy efficiency not only from reduced computationna de- mands but also from minimized mem- ory costs. This result further strength- ens the promise of energy-efii energy-efficient de- ployment of Spikachu for fully im- plantable BCIs.
Table 6: Memory access counts for Spikachu and baselines. Best performing model is in bold and second best model is underlined. M stands for millions.
Model
Load (M)
Store (M).
Total Ops (M)
LSTM
9.85
3.29
13.14
MLP
7.93
2.65
10.58
POYO-causal
1400
467
1860
GRU
7.61
2.54
10.15
POYO
1400
467
1860
Spikachu
4.47
1.75
6.22
We also note that while this analysis is hardware-agnostic. the practical energy savings are likely substantially greater than what suggested here. The present analysis most directly applicable to von Neumann architectures (e.g., conventional CPUs and GPUs), where further memory overheads
In terms of energy expenditure, AC is significantly cheaper than MAC [89].
35

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arise from maintaining DRAM blocks even when their contents are not accessed during clock cycle [96]. In contrast, neuromorphic hardware (the intended deployment platform for Spikachu) typically relies on local SRAM memory, which incurs negligible keep-alive costs and only needs to supply data on per-core basis. This architecture effectively eliminates the bulk of memory-related costs compared to von Neumann systems. As result, energy savings are expected to be significantly more pronounced on neuromorphic hardware, with total memory costs likely falling below the estimates derived from our hardware-agnostic analysis presented in this section.
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Reviews:
[{"review_id": "xaABJGfm5G", "reviewer": "Reviewer_Bmuc", "summary": "The paper presents Spikachu, a novel neural decoding framework using Spiking Neural Network. It is designed to address scalable, causal, and energy-efficient decoding in brain machine interface. It shows competitive decoding performance and dramatic improvements in energy efficiency.", "questions": "- In Figure 3, why does Spikachu-mp result in lower energy consumption than single-session training, despite involving a larger dataset? \n- In Figure 5B, decoding performance fluctuates rather than improves with additional training data. Can the authors comment on possible causes?\n- In Figure 6, why does transfer learning to a new task yield performance comparable to training from scratch? \n- Are the new-task generalization results cross-validated across sessions or trials? What are the confidence intervals?\n- Have prior works shown neural decoding or BCI devices on neuromorphic hardware? How might Spikachu translate to such platforms?", "limitations": "- Energy consumption estimates mainly focus on FLOPs, ignoring other costly operations such as memory access\n- Lack of real life comparison between Spikachu and its ANN baselines in both accuracy and energy consumption (instead of simulated energy consumption)", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- Offers a compelling perspective to address the limitations of current BCI systems, such as energy efficiency, causality and real time processing\n- Presents a novel method to integrate spiking neural networks with attention mechanism to improve energy efficiency without sacrificing performance\n- Demonstrates promising transfer learning capabilities across session, subjects and task, which is an important step toward scalable neural decoding in real life\n\n**Weaknesses**\n- Lack of justification why ANN harmonizer is used here, instead of using SNN end-to-end, given the paper’s emphasis on efficiency\n- Lack of ablation studies to isolate the performance contributions of the ANN harmonizer versus the SNN components\n- Lack of representation analysis to see if any structure emerges in the learned session embeddings, for example, clustering by subjects or task conditions", "was_revised": true, "final_justification": "The authors’ rebuttal has resolved my concerns through detailed explanations and additional analysis. I have updated my score accordingly.", "rebuttal": "Thank you for your thoughtful feedback. We are excited to read that our approach offers “competitive decoding performance and dramatic improvements in energy efficiency” that represent “an important step toward scalable neural decoding in real life.” \n\n# Weaknesses\n\n> 1. Lack of justification why ANN harmonizer is used here, instead of using SNN end-to-end, given the paper’s emphasis on efficiency.\n\nThank you for the constructive criticism. We agree that an end-to-end SNN architecture would be favorable.\n\nHowever, such a design would limit training to single sessions. To scale to multi-session data (where neural input dimensions vary across sessions and subjects), we require a mechanism to project inputs into a shared, fixed-dimensionality latent space. The ANN harmonizer fulfills this role via cross-attention with learnable queries to map variable inputs to a fixed-dimensional latent.\n\nWe resorted to implementing the harmonizer with ANNs because cross-attention with learnable queries has not been translated to SNNs yet. Fortunately, the ANN-harmonizer can be easily replaced once spiking cross-attention is developed.\n\nWe will make sure to better motivate the ANN harmonizer in section 3.1.\n\n\n> 2. Lack of ablation studies to isolate the performance contributions of the ANN harmonizer versus the SNN components\n\nThank you for the suggestion. Please refer to Appendix F.2 for a comprehensive ablation study. The results of ablating the ANN harmonizer are summarized below:\n\n|Model\\R²            | CO | RT |\n|-|-|-|\n| Spikachu            | 0.8399     | 0.6762     |\n| Spikachu w/o Harmonizer | 0.8344     | 0.6771     |\n\nClearly, our model did not rely on the ANN harmonizer to perform.\n\n> 3. Lack of representation analysis to see if any structure emerges in the learned session embeddings, for example, clustering by subjects or task conditions.\n\nThank you for this constructive suggestion. To address this, we conducted a clustering analysis on the embedding space of pretrained Spikachu-mp. \n\nSpikachu-mp does not have explicit session-specific embeddings; however, it does learn unit embeddings for each electrode of each session. To investigate whether structure emerged in the latent space, we aggregated the unit embeddings of each session into a 2D matrix and applied PCA, retaining the first five PCs as a summary representation for each session. We then used these session-level representations as features in two distinct Linear Discriminant Analyses: one to assess separability by subject (monkeys C, J, and M) and another by task (CO vs RT). Visualizing these projections into the LDA space revealed clear clustering both by subject and by task. \n\nTo quantify the separability, we trained an SVM classifier using 5-fold cross-validation with features being the summary representations of each session (the PCs of the aggregated unit embeddings) to predict (1) the subject and (2) the task associated with each session. We achieved an accuracy of 0.69 ± 0.02 for subject (chance = 0.33) and 0.79 ± 0.02 for task classification (chance = 0.5).\n\nThose results suggest that structure emerges in the latent space of our model. We are prohibited from including figures in the rebuttal but we promise to do so in revisions.\n\n# Questions \n\n> 1. In Figure 3, why does Spikachu-mp result in lower energy consumption than single-session training, despite involving a larger dataset?\n\nThank you for this insightful question. We identified that pretraining across multiple sessions and subjects produces a model with sparser activations which promote energy savings (see sections 4.5 and Appendix F.1). \n\nNote that energy savings refer to inference, not training. In fact multi-session training is more resource intensive than single-session training but yields a model with more sparsely activated neurons, which in turn requires less energy during inference. We will further clarify this in section 4.5 in revisions.\n\n> 2. In Figure 5B, decoding performance fluctuates rather than improves with additional training data. Can the authors comment on possible causes?\n\nThank you for this insightful observation. \n\nWe attribute this to the fact that different levels of pretraining included different recording sessions. Naturally, the decoding performance of our model was better for some sessions than others (simultaneously across all conditions, i.e. training from scratch, finetuning, transferring), which explains why the model performance seems to fluctuate.\n\n_However, this should not be interpreted as a fluctuation in the performance gain offered by pretraining_. Inspection of the difference between the colored bars (finetuning/transferring) and gray bars (training from scratch) in Figure 5B shows that the performance gains correlate with the amount of pretraining data. For enhanced clarity, we plotted the performance gain in Figure 8B.\n\nWe are happy to include Figure 8 in the main text if you think it would help.\n\n> 3. In Figure 6, why does transfer learning to a new task yield performance comparable to training from scratch?\n\nThank you for this insightful question. We note that transferring did boost performance: it accelerated learning by 2.33x and improved energy efficiency by 3.03%. \n\nRegarding the lack of decoding performance improvement, we attribute this to the substantial domain shift between the pretraining and transfer conditions. Spikachu-mp was pretrained on the CO and RT data from Perich et al. (2018), all collected from the same lab under the same experimental conditions. The transfer was performed on MC-RTT from Pei et al. (2021), which was collected in a different lab with a different experimental setup (e.g., touchscreen-based instead of manipulandum). Given this substantial domain shift, the representations learned by pretraining did not exactly match those of the target domain, which explains why decoding performance did not improve.\n\n> 4. Are the new-task generalization results cross-validated across sessions or trials? What are the confidence intervals?\n\nThank you for this thoughtful suggestion. We have now conducted 3-fold cross-validation on transferring to both the MC-RTT and MC-Maze. The results are shown below.\n\n| Task \\ R²   | Split 1 | Split 2 | Split 3 | Mean | 95% CI |\n|-|-|-|-|-|-|\n| MC RTT    | 0.57                   | 0.50         | 0.54         | 0.536   | ±0.0324 (±6.0%)  |\n| MC Maze   | 0.79                   | 0.82         | 0.83         | 0.813   | ±0.0192 (±2.4%)  |\n\nSpikachu-mp transferred consistently across all splits. We will incorporate these results in the revised manuscript.\n\n\n> 5. Have prior works shown neural decoding or BCI devices on neuromorphic hardware? How might Spikachu translate to such platforms?\n\nThank you for this insightful question. As discussed in Section 2 (lines 103–109), prior studies have successfully deployed SNN-based neural decoders in closed-loop BCI experiments. Here we highlight two compelling demonstrations:\n- Dethier et al. (2013) deployed an SNN neural decoder on neuromorphic hardware and used it in closed-loop, online experiments with two monkeys.  \n- Leone et al. (2023) implemented an SNN neural decoder on Intel’s Loihi chip and showed that it matched the performance of larger ANN models.  \n\nConsidering that Spikachu's complexity is on par with the models used in these works, we believe it would be feasible to deploy Spikachu on similar neuromorphic platforms (e.g. Intel's Loihi 2). For quantitative insights, see our response to weakness 5 of reviewer dGKH.\n\n\n# Limitations\n\n> 1.Energy consumption estimates mainly focus on FLOPs, ignoring other costly operations such as memory access.\n\nThank you for raising this insightful concern. Here we will clarify our rationale for focusing on FLOPs as a proxy for energy consumption.\n\n1. Using FLOPs as an energy proxy is a widely accepted practice in literature. Many influential works [Zhu et al. (2024; _Autonomous driving with Spiking Neural Networks_); Lv et al. (2023; SpikeBERT); Zhou et al. (2022; Spikformer)] rely on FLOPs to measure energy costs. By doing the same, we ensure comparability with previous works.\n2. FLOPs provide an implementation-agnostic metric for assessing computational cost, avoiding variability due to specific hardware or software.\n3. FLOPs serve as a proxy not only for computation but also for memory usage since memory access costs for MACs and ACs are linearly related to FLOPs. Based on Liao et al. (2022; _An energy-efficient spiking neural network for finger velocity decoding for implantable brain-machine interface_), standard practices assume a fixed number of memory loads and stores per MAC and AC.\n4. Our target deployment is on neuromorphic hardware (e.g. Loihi 2) which does not rely on Von Neumann hardware. These chips employ local SRAM-based memory, which is far more energy efficient than DRAM used in conventional CPU/GPUs, effectively minimizing memory transfer costs.\n\nFor a quantitative analysis of memory costs for Spikachu and baselines, please refer to question 4 of reviewer dGKH.\n\n\n> 2. Lack of real life comparison between Spikachu and its ANN baselines in both accuracy and energy consumption (instead of simulated energy consumption).\n\nWe appreciate your constructive criticism and echo that real-world validation of Spikachu is a critical next step (as mentioned in lines 358-363).\n\nThat said, a real-life comparison between Spikachu and baselines is extremely challenging as it would require customizing a neuromorphic chip to support both Spikachu and the ANN baselines. Given the limited access to neuromorphic hardware and that their software ecosystems are underdeveloped, this effort would be a separate publication in itself. As such, we prioritized rigorous computational comparisons to ensure fair and reproducible results. However, we hope our work inspires collaboration between neuroscience and hardware communities to address these barriers in future work. We will further expand on this in our discussion (section 5)."}, {"review_id": "wPa87kDhoN", "reviewer": "Reviewer_dGKH", "summary": "The authors introduce Spikachu - a \"spiking\" transformer model for decoding spikes for multi-session, multi-subject from electrophysiology experiments in non-human primates performing a motor control task. The goal is for the network to predict the x and y velocities from binned spike data. They claim substantial (between 2.26-418.81x) energy efficiency advantages over alternative methods, such as POYO, while achieving similar performance. They propose in addition a way to causally map neural recordings from multiple subjects in a common latent space and will release both models and code publicly.", "questions": "- What energy per sample for the tested model on the GPU you are running on?\n- Can you describe the spiking neuron model and gradient computation explicitly for the case (T = 1) that you are using. It should simplify significantly from the description of A.1. \n- Can you estimate the chip size (based on memory requirements) that would be required to implement your model with the energy estimates you are stating? \n- Can you explicitly estimate the cost of memory operations under ideal conditions?", "limitations": "The claimed energy efficiency advantages are estimates that are unrealistic and have no basis in any existing hardware implementation. The authors should clearly discuss the limitations of the energy estimates and complement them with actual measurements.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "While I find the overall motivation of the study compelling, I have some concerns about methodology and purported energy efficiency.\n\nStrength:\n- Cute name\n- Causal attention model that performs about as well as a GRU\n- The method matches prior work in terms of decoding performance\n- Using the accounting performed in the work there is a claim to a significant energy efficiency advantage\n- This is to my knowledge the first attempt to use a \"spiking\" attention mechanism for neural decoding\n\nWeaknesses:\n- Adopting an existing architecture (Spikformer) to a task with an existing transformer based solution (POYO) is has limited novelty.\n- The spiking transformer uses no temporal dynamics: The actual timestep used is (T=1) (see page 26, footnote 3), making most of the background on spiking neurons and bio-inspired neuron models in appendix A redundant. A more straightforward description would be that the authors are training a linear attention model with perceptron activation function. \n- Energy efficiency estimates are unrealistic and should not be stated without qualifications. While I recognize that it is common practice to make claims of energy efficiency for SNN, these claims should be clearly marked as estimates everywhere they are made and the limitations of these estimates should be discussed in the main body of the article. \n- Energy consumption is first and foremost a property of the hardware that the model is run on. The main point of the cited reference (Horowitz), is not that E_AC is 0.9 pJ (the quantity entering the energy estimate for SNN), or that E_MAC is 4.6pJ, but that in a modern processor an addition will typically require 70pJ -- meaning only a fraction of the overall energy for addition is spend on the computation itself. On a typical processor you would therefore expect the difference between a MAC and an AC operation to be more on the order of a few percent, since most energy is expended on control and memory movement. \n- The parameter size of the proposed transformer model make an implementation in a realistic BCI interface questionable.\n- The authors do not measure the actual energy consumption of the models as they run it on the actual hardware they have available. Doing so would make the difference between the estimated energy and the actual energy clear.", "was_revised": true, "final_justification": "Authors sufficiently addressed my concerns.", "rebuttal": "Thank you for your insightful comments and questions, and for pointing out that our work is the “first attempt to use a 'spiking' attention mechanism for neural decoding” with “a significant energy efficiency advantage”.\n\nDue to space constraints, we provide only the beginning of each of your prompts.\n\n# Weaknesses\n\n> 1. Adopting an existing architecture (Spikformer)…\n\nThank you for this thoughtful comment. While it is true that our model incorporates components from the literature, we introduce several novel elements.\n1. We introduce a **multi-scale SNN** module designed to process streaming neural signals across distinct temporal scales. Notably, this component is **not transformer-based**, setting our design apart from other works (i.e. POYO and Spikformer). Our ablation study (Appendix F.2) demonstrates that this component benefits decoding performance most.\n2. We contribute a **causal harmonizer** that enables scalable training across multi-session, multi-subject data. In Appendix F.4, we demonstrate that the harmonizer can also be incorporated into other common neural decoding models, such as MLP and GRU.\n3. Our model uses spiking self-attention, a causal, SNN-specific mechanism that, contrary to vanilla self-attention (used in POYO), operates only across the electrode dimension of the data. Temporal relations are implicitly captured via the internal state of spiking neurons.\n4. Our framework  is **causal**, which is essential for online applications. All other frameworks capable of handling multi-session, multi-subject data (including POYO, LFADS, and CEBRA) are non-causal.\n\nIn summary, we introduce key innovations that expand the scalability and real-time applicability of neural decoding systems.\n\n> 2. The spiking transformer uses no temporal dynamics…\n\nThank you for raising this important point. **We would like to clarify that our model uses temporal dynamics.**  Specifically, it processes spike trains in a streaming fashion, one input bin at a time, without requiring repeated input presentations. By T=1 we indicate that we do not artificially repeat inputs. This contrasts with applications involving static data, where SNNs are typically run on the same input multiple times, implying T>1. **The total number of membrane potential update steps is equal to the number of input spike bins, typically a trial’s worth of data**.\n\nTo demonstrate the utility of temporal dynamics in our model, based on your feedback, we re-implemented Spikachu as an ANN without any dynamics (LIF neurons replaced by ReLU neurons) and tested it on the same data as all baseline models (section 4.2). The results are summarized below.\n| Model \\ R² | CO | RT |\n|-|-|-|\n|Spikachu| 0.8399 | 0.6762|\n|Spikachu-ANN| 0.5332 |0.3642|\n\nThis shows that the temporal dynamics of our model are essential for its performance. We will ensure to clarify this point in Appendix E.2 and add this ANN version of Spikachu as an additional baseline in section 4.2.\n\n\n> 3. Energy efficiency estimates are unrealistic and should not be stated without qualifications…\n\nThank you for this insightful comment. Indeed,  the energy consumption figures presented in our work are estimates. However, we emphasize that they are grounded in rigorous calculations detailed in Appendix E and are based on assumptions drawn from prior works [3, 4]. While we recognize that energy values may vary depending on the hardware, our efficiency claims rely on hardware-agnostic metrics such as FLOPs, ensuring fair comparisons that are unbiased by specific hardware or software.\n\nTo address your feedback, we will label all energy-related claims as estimates, and we will add a paragraph in the discussion (section 5) on this limitation.\n\n> 4. Energy consumption is first and foremost a property of the hardware…\n\nThank you for this important comment. Indeed, in conventional hardware, memory access and data movement dominate energy costs.\n\nTo not lose generality, in our work, we wish to present hardware-agnostic estimates of energy consumption. FLOPs allow us to do that, as they do not depend on hardware specifics. Importantly, as shown in [2], FLOPs are a good proxy for memory costs as well, since they scale proportionally to the number of operations.\n\nAdditionally, our model would see further memory efficiency gains when deployed on neuromorphic hardware, which, unlike conventional CPU/GPUs, has efficient SRAM memory integrated with processing units. The integrated memory, combined with SNNs' compact binary transmission signals, which minimize communication overhead (unlike ANNs, which transmit floats), would amplify our model's energy efficiency gains beyond what is reflected in our FLOP-based calculations.\n\nIn summary, while we acknowledge that energy costs are hardware-dependent, our FLOP-based estimates provide a fair and implementation-agnostic way to capture costs not only associated with computation but also memory movement. We will emphasize this in Appendix E.2.\n\n> 5. The parameter size of the proposed transformer model make an implementation in a realistic BCI interface questionable.\n\nThank you for raising this important concern.\n\nSpikachu contains <4M trainable parameters (therefore, also < 4M synapses) and \\~10K LIF neurons. This is well within the capabilities of current neuromorphic chips. Loihi 2 supports 1M neurons and 120M synapses. Darwin 3 supports 2.35M neurons and 100M synapses. Their die sizes are 31 mm² and 358 mm², respectively, so **Spikachu can fit on a chip < 31 mm²**. Importantly, this form factor is well within the physical constraints of fully implantable BCIs, which fit within the surface area of a U.S. quarter (\\~462 mm²) [7]. \n\nIn addition, prior works have deployed SNN-based neural decoders. The authors of [2] successfully deployed a 4-layer spiking MLP with ~1K neurons on neuromorphic hardware for BCI. Similarly, in [5], a real-time, 20K-neuron SNN decoder was used on two monkeys in closed-loop BCI experiments. \n\nThus, from a size and capacity standpoint, Spikachu can already be accommodated by current neuromorphic platforms. We will add these points to the discussion (section 5) in revisions.\n\n\n> 6. The authors do not measure the actual energy consumption of the models…\n\nThank you for this insightful comment. We attempted this, please see our reply to Question 1.\n\n# Questions\n\n> 1. What energy per sample for the tested model on the GPU you are running on?\n\nThank you for this insightful question. \n\nWe attempted to measure real-time energy using both PyNVML and Zeus packages on NVIDIA 2080Ti and RTX 4090 GPUs. However, we found that the results on both GPUs were inconsistent and not repeatable. This is expected with GPU power profiling, a task complicated by factors such as dynamic power scaling, memory management, and coarse power sampling rates (only 1 Hz for NVML), which introduce significant noise.\n\nIn summary, while we agree that measuring real-world energies would have been insightful, we unfortunately cannot accurately perform these measurements on our available hardware. We are happy to share details of our scripts if it would be helpful.\n\nWe also note that it would be more appropriate to measure these energy costs on neuromorphic hardware, rather than on a GPU (see also our response to limitation 2 of reviewer Bmuc).\n\n> 2. Can you describe the spiking neuron model and gradient computation explicitly for the case (T = 1) that you are using…\n\nWe appreciate your attention to this technical point. \n\nAs explained in our response to Weakness 2, the standard LIF neuron dynamics described in Appendix A.1 apply to our model without simplification. Gradient computations follow the standard  Back-Propagation Through Time (BPTT), as implemented in SpikingJelly [6].\n\n> 3. Can you estimate the chip size (based on memory requirements) that would be required to implement your model…\n\nThank you for this pertinent question. With current technology, a chip < 31mm² is enough. We refer you to our reply to Weakness 5 for more details.\n\n> 4. Can you explicitly estimate the cost of memory operations under ideal conditions?\n\nThank you for this insightful suggestion.\n\nTo address this, we explicitly estimated the cost of memory operations under ideal conditions for Spikachu and baselines on the data described in section 4.2. Following [2], we assumed 3 loads and one store per MAC and two loads and one store per AC. The results are summarized below.\n\n| Model \\ Ops | STORE | LOAD | Total |\n|-|-|-|-|\n| MLP | 7.93 M | 2.65 M | 10.58 M |\n| GRU | 7.61 M | 2.54 M | 10.15 M |\n| LSTM | 9.85 M | 3.29 M | 13.14 M |\n| POYO | 1.40 G | 467 M | 1.86 G |\n| Spikachu | 4.47 M | 1.75 M | 6.22 M |\n\nClearly, Spikachu requires the fewest memory operations across the board. We will include these results in revisions.\n\n# Limitations\n\n> 1. The claimed energy efficiency advantages are estimates…\n\nThank you for this constructive criticism. We will address this in the discussion section and refer you to our reply to Weakness 3.\n\n> 2. The authors should clearly discuss the limitations of the energy estimates…\n\nWe kindly refer you to our replies for Weaknesses 3 and 4 and Question 4, and reiterate our pledge to clarify the text to reflect the estimated nature of our energy findings.\n\n# Bibliography\n[1] Azabou et al., _A Unified, Scalable Framework for Neural Population Decoding_, 2023\n\n[2] Liao et al., _An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface_, 2022\n\n[3] Zhu et al., _Autonomous Driving with Spiking Neural Networks_, 2024\n\n[4] Lv et al., _SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation_, 2023\n\n[5] Dethier et al., _Spiking neural network decoder for brain-machine interfaces_, 2011\n\n[6] Fang et al., _SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence_, 2023\n\n[7] Musk et al., _An Integrated Brain-Machine Interface Platform With Thousands of Channels_, 2019"}, {"review_id": "wcTwWwsSQ7", "reviewer": "Reviewer_QBku", "summary": "The paper describes a neural decoding framework, that is, a spiking model designed to map brain signals to some appropriate action.  Inputs are taken from a cortical implant.  Various architectures from the literature are implemented using spiking resulting in a causal framework for inference.  Experiments are done using data from an animal controlling a cursor; the goal is to predict the cursor velocity.  Results show that the model performs well whilst being the most energy efficient amongst a sensible choice of competitors.", "questions": "It may be in the paper, but does one of the baseline approaches in table 2 try to mimic the architecture in figure 1?", "limitations": "I don't see the limitations addressed explicitly, but I don't see a need, other than perhaps ethical points.  There are clearly ethical issues associated with brain implants and animal testing, but the datasets appear to be independent of the study.", "rating": 2, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\nThe paper is very clearly written.  The use of spiking nets makes sense for BCI signals where the original signal is itself spiking, and the application is itself quite persuasive.\nThe work shows that a spiking model can perform well against good alternatives for decoding brain signals.  Further, it is by far the most (potentially) energy efficient.\nThe processing pipeline is sensible.\nSpikachu is a cool name.\n\nWeaknesses:\n\nMy main worry is that, however well intended, the work is essentially mainly engineering.  The use of spiking is really only to achieve energy efficiency in a future neuromorphic architecture, and the component techniques are otherwise known.  Of course the demonstration that it works is interesting, but I'm less certain whether that contribution is novel enough for NeurIPS.\nThe energy calculations are presumably hypothetical, with the actual implementation being on conventional hardware.\nOther solutions appear to perform better than the spiking (although I fully appreciate that energy efficiency is an important goal).", "was_revised": true, "final_justification": "My original negative opinion was based quite heavily on my belief that the work is mainly engineering and not so novel.  After the review process I find myself simply disagreeing with the authors (and hesitant to just argue) and in conflict with the other reviewers (two of whom have bumped up scores significantly).\nWhilst I stand by the negative opinion I have lowered the confidence score reflecting the fact that I have probably missed something; it seems the right answer in a Bayesian sense.", "rebuttal": "Thank you for your thoughtful feedback and suggestions. We are excited to read that our \"paper is very clearly written\" and that our \"model can perform well against good alternatives for decoding brain signals\" while being \"by far the most (potentially) energy efficient\". \n\nPlease find our answers to your questions/comments below.\n\n# Weaknesses\n\n> 1. My main worry is that, however well intended, the work is essentially mainly engineering.\n\nThank you for this thoughtful comment. We appreciate your concern regarding the balance between engineering and conceptual contributions in our work. We respectfully emphasize that our primary contributions are conceptual in nature. Our work addresses longstanding challenges in the field, particularly the need for causal, scalable, and energy-efficient models that generalize across sessions and subjects, and introduces several key innovations to advance neural decoding to this end.\n\nBelow, we highlight the main conceptual contributions of our work:\n\n- **Causal harmonizer for multi-session, multi-subject neural decoding**.  We are the first to use cross-attention with learnable queries to integrate multi-session, multi-subject data in a causal manner. This is a significant step forward, as it enables models that were previously limited to single-session training to operate across larger, more diverse datasets, reaping the well-known benefits of scale. Importantly, in Appendix F.4, we show that the harmonizer can be integrated with widely used neural decoding models such as MLP, GRU, and LSTM.\n- **Multi-scale SNN module**.  We introduce a multi-scale SNN module designed to process streaming neural signals at distinct temporal resolutions. This approach enables our framework to capture rich representations of neural dynamics without relying on transformer-based architectures, distinguishing it from prior works such as Spikformer and POYO. Our ablation study (Appendix F.2) reveals that this module is the most critical component for overall decoding performance.\n- **First causal framework for multi-session, multi-subject neural decoding**. To our knowledge, this is the first neural decoding framework that is simultaneously causal and trainable across multiple sessions and subjects. While many existing models (e.g., POYO, LFADS, CEBRA) can generalize across sessions and subjects, they rely on non-causal architectures (i.e. require past and future context to make a prediction), limiting their applicability outside offline research settings. Our framework addresses this gap directly.\n- **SNN pretraining across sessions, subjects, and tasks**. We are also the first to scale SNN-based neural decoders to multi-session, multi-subject data. We show that the learned neural representations generalize to unseen sessions, subjects, and tasks. While this has been demonstrated in ANN-based models (e.g., POYO, CEBRA), it was not previously known whether similar transferability would hold for SNNs given their different training dynamics and representational capacities.\n- **Energy-constrained neural decoding at scale**. We show that it is possible to build a multi-session, multi-subject neural decoder that respects the energy constraints required for deployment on edge computing platforms. Although this is not the only path toward practical BCI systems, we believe it is a viable and underexplored one, especially for scenarios where continuous tethering to a high-performance computer is infeasible.\n\nIn summary, our contributions are primarily conceptual, and in this work we establish several key firsts required for causal, scalable, and energy-efficient neural decoding.\n\n> 2. The use of spiking is really only to achieve energy efficiency in a future neuromorphic architecture, and the component techniques are otherwise known.\n\nThank you for this thoughtful comment.\n\nFirst, as outlined in our response to weakness 1, we introduce multiple novel components, most notably: (1) the neural harmonizer that facilitates causal multi-session, multi-subject training, and (2) the multi-scale SNN block that processes streaming inputs at distinct temporal scales to build rich latent representations.\n\nSecond, while energy efficiency is a compelling advantage of SNNs, it is not the sole reason for adopting them in our framework. SNNs offer other key properties that align with the demands of real-time neural decoding systems:\n1. They are inherently causal, enabling streaming inference without relying on future data or long context windows, since context is implicitly encoded in the neuron’s membrane potential.  \n2. Their bio-inspired design makes them particularly well-suited for interpreting real neural signals.\n\nBased on your comment, we will ensure to better motivate the choice of SNNs in the Introduction (section 1) of our revised manuscript.\n\n> 3. Of course the demonstration that it works is interesting, but I'm less certain whether that contribution is novel enough for NeurIPS\n\nWe are excited to hear that \"the demonstration that it works is interesting\". Please find the main contributions of this work in our response to your weakness 1. We hope that based on those you will reconsider.\n\n>  4. The energy calculations are presumably hypothetical, with the actual implementation being on conventional hardware\n\nThank you for this insightful comment.\n\nWe agree that the energy consumption figures presented in our work are estimates. However, we emphasize that they are grounded in rigorous calculations detailed in Appendix E and are based on assumptions drawn from well-established prior works [i.e. Zhu et al. (2024, _Autonomous Driving with Spiking Neural Networks_), Lv et al. (2023, _SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation_)]. While we recognize that absolute energy values may vary depending on the hardware, our efficiency claims rely on hardware-agnostic metrics such as FLOPs, ensuring fair comparisons that are not biased due to any specific hardware or software.\n\nBased on your feedback, we will revise the manuscript to explicitly label all energy-related claims as estimates, and we will add a paragraph in the discussion section (section 5) expanding on this limitation.  \n\n> 5. Other solutions appear to perform better than the spiking (although I fully appreciate that energy efficiency is an important goal).\n\nThank you for this thoughtful observation.\n\nAs you correctly noted, POYO does achieve slightly higher decoding performance than our model in its standard configuration (while requiring 2 orders of magnitude more energy per inference). However, we would like to emphasize that this comparison is not entirely fair. POYO is a non-causal model (i.e. it utilizes both past and future neural activity to make predictions). In contrast, Spikachu is causal (i.e. relies only on past inputs). This distinction is critical, as POYO’s access to future data is not feasible in online BCI applications.\n\nTo enable a fair comparison, we benchmarked Spikachu against a causal variant of POYO (described in Appendix C.2; “POYO-causal in Table 2). Under this causal constraint, Spikachu outperforms POYO-causal by a notable margin, with ΔR² = 4.37% on the CO task and ΔR² = 11.32% on the RT task.\n\n\n# Questions\n\n\n> 1. It may be in the paper, but does one of the baseline approaches in table 2 try to mimic the architecture in figure 1?\n\nThank you for this insightful question. In the original submission, we did not include a direct comparison between our model and an equivalent ANN variant.\n\nTo address this, we implemented an ANN variant of our model (referred to as Spikachu-ANN), in which we replaced all spiking LIF neurons with stateless ReLU neurons, while keeping all other architectural components and training procedures identical. We then evaluated Spikachu-ANN on the same dataset that we used for comparing against other baselines (see section 4.2 and Table 2). \n\nWe summarize the results below.\n\n| Model \\ R² | CO | RT |\n|-|-|-|\n|Spikachu| 0.8399 | 0.6762|\n|Spikachu-ANN| 0.5332 |0.3642|\n\nClearly, Spikachu-ANN consistently underperformed relative to its SNN version. This is not surprising, as the spiking neurons in the SNN-based implementation implicitly retain temporal information through membrane potential dynamics. In contrast, the ReLU-based neurons in Spikachu-ANN are stateless and therefore incapable of capturing temporal dependencies across time steps. \n\nBased on your feedback we will include Spikachu-ANN as a baseline in section 4.2 and Table 2 of our revised manuscript.\n\n# Limitations\n\n> 1. I don't see the limitations addressed explicitly, but I don't see a need, other than perhaps ethical points. There are clearly ethical issues associated with brain implants and animal testing, but the datasets appear to be independent of the study.\n\nThank you for raising this important concern. We fully acknowledge the ethical considerations surrounding animal research and the use of brain implants. While, as you correctly noted, we did not collect the datasets ourselves, we hold ourselves accountable for ensuring that any data we use comes from studies that adhere to the highest ethical standards. To that end, we would like to emphasize that all datasets used in this work were collected by accredited research institutions and received approval from the relevant institutional ethical committees. We are committed to using only data from studies that prioritize animal welfare and follow established ethical guidelines.\n\nIf you feel it would be helpful, we would be happy to add a dedicated paragraph in the discussion section addressing the ethical considerations of brain implants and animal research. We appreciate your input on this."}, {"review_id": "pwmSk0omMw", "reviewer": "Reviewer_WoPc", "summary": "This paper splits uses the neurosymbolic framework to make neural network (SNNs) behave more bayesian neural networks in practice. This can happen due to the universal computability of both neural networks and traditional modes of AI which are considered turing complete. The specific approach is called Spikachu which combines traditional computing paradigms with neural network computing paradigms. The primary benefit is energy efficiency for commodity computing.", "questions": "How should this work be evaluated with respect to other more theoretical works?", "limitations": "N/A", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\nGood use of neurosymbolic framework.\ngood validation\n\nWeaknesses:\nsee questions.", "was_revised": true, "final_justification": "See updated review below for justification. No increase in score after author rebuttal.", "rebuttal": "Thank you for taking the time to review our manuscript. We are excited to read that our approach makes \"good use of the neurosymbolic framework\" and that you found our experimental validation comprehensive. We are also very happy to see that you marked our work as \"excellent\" (4/4) in terms of clarity, significance, and originality, and \"good\" (3/4) in terms of quality. Those scores, combined with the absence of weaknesses or unaddressed limitations in your report, speak to the high caliber of our work. We do hope that based on those, you will reconsider your overall score (3/6) which corresponds to “borderline reject”.\n\nBelow, we provide our response to your comments and questions. If you would like any further clarifications, we will be happy to provide them during the discussion period.\n\n# Weaknesses\n\nWe are proud to report that the reviewer identified no weaknesses in our work.\n\n# Questions \n\n> 1. How should this work be evaluated with respect to other more theoretical works?\n\nThank you for this insightful question. We agree that evaluating applied work such as ours alongside more theoretical research requires careful consideration of differing goals and contributions.\n\nOur work primarily aims to address practical, real-world challenges in neural decoding, such as causality, scalability, and energy efficiency, all of which are essential for model integration into online BCI systems. Rather than proposing new theoretical learning principles or formal guarantees, we focus on extending the more theoretical works such as POYO [Azabou et al. (2023)], CEBRA [Schneider et al. (2023)], and LFADS [Sussillo et al. (2016)] towards real-world utility in the context of BCIs.\n\nWe believe that the value of this work lies in its conceptual contributions to simultaneously causal, scalable, and energy-efficient neural decoding, particularly through the introduction of the following key innovations:\n\n-   **Causal harmonizer for cross-session and cross-subject generalization**.  To the best of our knowledge, we are the first to adapt cross-attention with learnable latents queries (similar to the Perceiver encoder) into a _causal_ harmonizer capable of integrating neural activity across multiple sessions and subjects. This is a significant step forward, as it enables models that were previously limited to single-session training to operate across larger, more diverse datasets, reaping the well-known benefits of scale. We further demonstrate in Appendix F.4 (lines 1126–1149) that this harmonizer is a modular and general-purpose component that can be integrated not only into our proposed architecture but also into other widely used neural decoding models such as MLPs, GRUs, and LSTMs.\n\n-   **Multi-scale SNN module for temporal feature extraction**.  We introduce a multi-scale SNN module designed to process streaming neural signals at distinct temporal resolutions. This approach enables our framework to capture rich representations of neural dynamics without relying on transformer-based architectures, distinguishing it from prior works such as Spikformer and POYO. Our ablation study (Appendix F.2, lines 1079–1103; Figure 11) reveals that this module is the most critical component for overall decoding performance.\n\n-   **First causal framework for multi-session, multi-subject neural decoding**. To our knowledge, this is the first neural decoding framework that is simultaneously causal and trainable across multiple sessions and subjects. While many existing models (e.g., POYO, LFADS, CEBRA) can generalize across sessions and subjects, they rely on non-causal architectures (i.e. require past and future context to make a prediction), limiting their applicability outside offline research settings. Our framework addresses this gap directly.\n\n-   **SNN pretraining across subjects, sessions, and tasks**: We are also the first to show that SNN-based neural decoders can be pretrained on large multi-session,  multi-subject datasets and learn neural representations that generalize to unseen sessions, subjects, and tasks. While this has been demonstrated in ANN-based models (e.g., POYO, CEBRA), it was not previously known whether similar transferability would hold for SNNs given their different training dynamics and representational capacities.\n\n- **Energy-constrained neural decoding at scale**:  Finally, we show that it is possible to build a multi-session, multi-subject neural decoder that respects the energy constraints required for deployment on edge computing platforms. Although this is not the only path toward practical BCI systems, we believe it is a viable and underexplored one, especially for scenarios where continuous tethering to a high-performance computer is infeasible.\n\nThese contributions aim to bridge the gap between theoretical developments and practical applicability, which we believe is a necessary step toward translating real-world BCI systems from the research lab to the real world. As such, we suggest that this work be evaluated on how effectively it translates theoretical ideas into robust, deployable models, and on whether it will inspire further theoretical and empirical research on causal, scalable, and efficient models for neural decoding.\n\n\n# Limitations\n\nWe are proud to report that the reviewer identified no unaddressed limitations in our work."}]

Author rebuttal:

Meta-review: The paper introduces Spikachu, a causal, scalable, and energy-efficient spiking-neural-network (SNN) framework for neural decoding in BCIs. It processes binned spikes directly, learns shared latent representations with spiking modules (including spiking self-attention over electrodes), and decodes behavior online.

Key components and clarifications made during review:
A multi-scale SNN module (non-Transformer) is highlighted as the largest contributor to accuracy; a causal “harmonizer” enables scalable multi-session training and is shown to plug into MLP/GRU baselines; spiking self-attention operates across electrodes (temporal relations handled by neuron state). 
Authors stress causality vs popular non-causal decoders (POYO/LFADS/CEBRA). Against a causalized POYO, Spikachu wins by 4.37% (CO) and 11.32% (RT). 
An ANN ablation (Spikachu-ANN) shows large drops (CO 0.84 to 0.53; RT 0.68 to 0.36), indicating performance relies on spiking dynamics rather than architecture alone. 
Energy numbers are estimates (hardware-agnostic, FLOP-based) and will be labeled as such.

Strength
1) Clear motivation for causality and online use; wide coverage across animals/sessions/tasks.
2) Authors provide a store/load ops table to reason about memory movement (dominant in energy), not just MACs.
3) Feasibility argument for neuromorphic deployment. Model has <4M parameters and ~10k LIF neurons; fits well within Loihi-2 capabilities and even within a practical implant form factor (<31 mm^2).

Weakness
1) All energy results are estimates; the attempted GPU profiling was unstable, and no neuromorphic measurements are reported. This tempers the otherwise strong efficiency story.
2) In standard offline settings, POYO can outperform Spikachu; while the causalized comparison is fair for real-time BCIs, the paper should more clearly delineate offline vs online tradeoffs
3) The initial write-up confused some reviewers about temporal dynamics; the rebuttal clarifies streaming-bin dynamics, but the camera-ready must make this explicit.
4) While the representation clustering is promising, the paper could better connect learned features to neurophysiological structure and include closed-loop experiments to demonstrate end-to-end BCI utility (acknowledged as future work).

Recommendation
I recommend Accept (poster). The paper addresses an important practical gap: causal, efficient neural decoding with substantive algorithmic contributions and broad empirical evidence. The authors engaged constructively during rebuttal, adding causalized baselines, crucial ablations, representation analyses, and memory-ops accounting. Although energy is not directly measured, the hardware-agnostic framing, large performance gaps over causal baselines, and strong scaling/transfer results make this a valuable contribution to both SNNs and BCI decoding. Two reviewers gave an accept and one reviewer provided a reject.
Reviewer QBku (reject) Concerns: views work as engineering with hypothetical energy savings; wants real measurements and stronger novelty beyond assembling known pieces.
My weighing: While the request for measured energy is fair, the paper’s causal framing, multi-scale SNN design, spiking-attention, scaling/transfer, and clear causal-baseline wins constitute meaningful scientific contribution. With the rebuttal clarifications and added analyses, I find the evidence sufficient for acceptance, acknowledging the limitation on measurement.
Decision: Accept (poster)