Title: A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neural Decoding

Brain Computer Interfaces

Spiking Neural Networks

Energy Efficient Computation
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
Good use of neurosymbolic framework.
good validation

Weaknesses:
see questions.

QUESTIONS:
How should this work be evaluated with respect to other more theoretical works?

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-06-04

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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).\n\nQUESTIONS:\nIt may be in the paper, but does one of the baseline approaches in table 2 try to mimic the architecture in figure 1?\n\nLIMITATIONS:\nI 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.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nWhile 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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\n- 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)", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}