Title: SPICED: A Synaptic Homeostasis-Inspired Framework for Unsupervised Continual EEG Decoding
Venue: NeurIPS 2025
Accepted: yes
Keywords: Synaptic homeostasis; Brain-inspired; Continual EEG decoding
Abstract: Human brain achieves dynamic stability-plasticity balance through synaptic homeostasis, a self-regulatory mechanism that stabilizes critical memory traces while preserving optimal learning capacities. Inspired by this biological principle, we propose SPICED: a neuromorphic framework that integrates the synaptic homeostasis mechanism for unsupervised continual EEG decoding, particularly addressing practical scenarios where new individuals with inter-individual variability emerge continually. SPICED comprises a novel synaptic network that enables dynamic expansion during continual adaptation through three bio-inspired neural mechanisms: (1) critical memory reactivation, which mimics brain functional specificity, selectively activates task-relevant memories to facilitate adaptation; (2) synaptic consolidation, which strengthens these reactivated critical memory traces and enhances their replay prioritizations for further adaptations and (3) synaptic renormalization, which are periodically triggered to weaken global memory traces to preserve learning capacities. The interplay within synaptic homeostasis dynamically strengthens  task-discriminative memory traces and weakens detrimental memories. By integrating these mechanisms with continual learning system, SPICED preferentially replays task-discriminative memory traces that exhibit strong associations with newly emerging individuals, thereby achieving robust adaptations. Meanwhile, SPICED effectively mitigates catastrophic forgetting by suppressing the replay prioritization of detrimental memories during long-term continual learning. Validated on three EEG datasets, SPICED show its effectiveness. More importantly, SPICED bridges biological neural mechanisms and artificial intelligence through synaptic homeostasis, providing insights into the broader applicability of bio-inspired principles.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a Synaptic Homeostasis-Inspired framework for Unsupervised Continual EEG Decoding (SPICED), which is a biologically inspired continual learning method.   This methodology involves three aspects: i) synaptic consolidation to reinforce critical memory traces, ii) critical memory reactivation to select and replay important traces, and iii) synaptic renormalization to allow learning from new sequences and reduce the effects of detrimental memory traces.\n\nSTRENGTHS AND WEAKNESSES:\n+ The results for this method show better performance to the other methods compared to.\n\n+ Datasets used in the work seem well selected.\n\n- Tests of statistical significance for this method versus the other methods would add strength to the results.\n\n- The methodology seems extremely similar to growing neural gas (a variant of self organizing maps), especially in terms of how nodes are connected and and the proposed “critical connection consolidation” is performed. The paper should be related to work in self organizing maps as much of this appears quite similar.\n\n- The paper claims that this is unsupervised continual learning, however they state that models (for new neurons) are pre-trained on data from new individuals - this is not unsupervised (as it does not appear that new individuals are not detected automatically, rather this information is provided to the methodology), nor does it match standard continual learning scenarios as new individuals are pre-trained on existing data.  It would be good to relate this work to existing continual learning scenarios (task incremental, class incremental, etc)\n\n- It seems that the dataset partitioning is in a sense cherry-picking the problem.  From the paper, each individual’s data is partitioned into a pre-training set and then an incremental set (to use after pretraining), this means the framework has already seen data from all individuals (which also doesn’t line up with continual learning).  Entire individuals should be held out for incremental learning so the performance of the framework could be shown in an actual continual learning scenario.  If individuals were being held out for the incremental version this should be more clearly stated.\n\nQUESTIONS:\nQuestions relate to the strengths and weaknesses above.  In particular:\n\n1. How does this work relate to existing methodologies, especially growing neural gas and self organizing maps?\n\n2. Can you clarify how this relates to standard continual learning scenarios (class incremental, task incremental, etc)?\n\n3. How was the individual data partitioned? Was data for individuals entirely left out for incremental learning?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a biologically inspired method for continually learning framework called SPICED from EEG signals that balances learning new information, preserving important past memories. This framework has three key processes: critical memory reactivation that replays relevant past knowledge, synaptic consolidation that strengthens important connections, and finally synaptic renormalization that weakens and reduces the plasticity of less useful connections, enabling adaptability and memory retention. It represents each individual as a node in a dynamic synaptic network, connecting new individuals to similar past ones based on EEG feature similarity by fusing information from multiple relevant models and trains on pseudo-labelled data, The framework is validated on three diverse EEG tasks: sleep staging, emotion recognition, and motor imagery.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n•SPICED framework is based on synaptic homeostasis demonstrating both synaptic consolidation (strengthening) and renormalization (forgetting), supporting selective replay, model fusion, and adaptive forgetting, without modifying the core model architecture.\n\n•The framework introduces a synaptic network, where each node represents an individual, edges reflecting similarity and memory strength, allowing to selectively reactivate and replay task relevant past individuals based on importance scores combining similarity and synaptic strength.\n\n•It initializes new models by fusing multiple past models using importance-weighted aggregation and simulates forgetting using time dependent synaptic decay, stimulating biologically plausible memory dynamics.\n\n•The framework simulates biologically plausible forgetting through time-dependent synaptic decay and provides interpretable outputs like synaptic strength trajectories, network visualizations that  help understand what the model remembers and forgets during continual learning.\n\n•The framework has been validated on three EEG datasets: ISRUC (sleep), FACED (emotion), and PhysioNet-MI (motor imagery) with ablation studies, hyperparameter analysis, and visualizations of memory dynamics.\n\nWeaknesses:\n\n•Although SPICED is presented as an unsupervised CL method, it relies on labelled source-domain individuals for initialization and pretraining. This makes it semi-supervised in practice, and the unsupervised claim is misleading without further clarification.\n\n•The paper does not report forward transfer (FWT) or backward transfer (BWT) metrics, which are widely used in continual learning to quantify learning and forgetting. Their inclusion would offer deeper insight into SPICED’s stability plasticity behavior.\n\n•Although biologically inspired, the implementation simplifies complex neural phenomena as an example, by using exponential decay for forgetting, model fusion as weighted averaging. The biological claims are not validated against neuroscience literature.\n\n•The effectiveness of SPICED depends on the quality and diversity of the labelled source individuals and the performance under low-resource or poor initialization scenarios is not deeply analyzed.\n\n•Most baselines are either EEG-specific or older CL methods like EWC and MMD. The paper does not compare against recent strong replay-based or dynamic CL methods, including more recent continual learning techniques (e.g., replay-based or dynamic architectures) would strengthen claims.\n\nQUESTIONS:\n•Can you report forward transfer (FWT) and backward transfer (BWT) which are standard metrics in continual learning for quantifying positive transfer and forgetting? Including these would strengthen your empirical claims and enable fairer comparison with existing literature. Additionally, can you evaluate SPICED against more recent continual learning methods? \n\n•The paper uses mechanisms like exponential decay and weighted averaging to simulate synaptic forgetting and fusion. How closely do these mechanisms align with actual neural processes? Can you clarify which aspects of synaptic homeostasis are modelled vs abstracted, and cite supporting neuroscience literature. \n\n•Can the authors provide explanations or visualizations of the synaptic trace evolution, model fusion weights, or importance scores? Additionally how does SPICED model perform when the source individual pool is small, noisy, or low quality?\n\n•Can SPICED generalize to non-EEG domains such as sensor data, speech, or time-series forecasting? Please discuss how the core mechanisms such as synaptic network, consolidation, renormalization might transfer to other modalities, and what adaptations might be required. Addressing this would broaden the significance and applicability of your approach.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a biologically inspired continual learning framework for EEG decoding, motivated by synaptic homeostasis principles. The model incrementally adapts by: (1) initializing a base network, (2) incorporating new neurons based on pairwise similarity with existing representations, (3) consolidating new and old knowledge through model fusion, and (4) applying renormalization for adaptability. These steps are clearly explained and visually illustrated. The method is evaluated on three EEG datasets and compared against several recent baselines (e.g., MMD, EWC, UCL-GV, ReSNT, BrainUICL) using metrics like accuracy and macro-F1. The results demonstrate competitive performance, and the authors include ablation studies to support design choices.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper is very well-written and organized, with each methodological step presented clearly in logical subsections.\n2. The main algorithm is biologically intuitively explained and supported by a clear diagram that improves understanding.\n3. The evaluation is comprehensive: the proposed approach is compared against several relevant baselines and tested across three EEG datasets.\n4. The proposed method is more real-world oriented and this makes the work practically meaningful and applicable beyond standard benchmarks.\n\nWeaknesses:\n\n1. The related work section does not contextualize the compared baselines (e.g., BrainUICL, ReSNT, UCL-GV); instead, these are only introduced in the experiments. Briefly describing them earlier would better highlight the novelty and motivation of the proposed method. As in what is the gap in the other methods that this work is filling.\n2. The performance metrics (accuracy and macro-F1) are not defined anywhere. A brief explanation would be helpful for readers unfamiliar with these metrics.\n3. The main comparison table against the recent benchmarks is shown only as bar plots, without numerical values, standard deviations, or statistical significance reporting, which limits interpretability and reproducibility.\n4. It seems like a lof of tunable hyperparameters according to the appendix. It's unclear whether all the 10-15 hyperparameters mentioned are tuned exhaustively or selectively.\n\nQUESTIONS:\n1. Could you briefly describe the baseline methods (e.g., BrainUICL, UCL-GV, ReSNT) in the related work section to better frame your contribution? A comparison of what their identified gaps are and how your method takes it forward.\n2. Are all hyperparameters (including learning rates, weight terms, and consolidation thresholds) tuned individually for each dataset? If so, how feasible is this setup for real-world deployment?\n3. Could you provide a main table with the exact numerical values for accuracy and macro-F1, including standard deviations or confidence intervals, instead of only bar plots?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents SPICED, a neuromorphic framework designed for unsupervised continual EEG decoding in dynamic, real-world scenarios where new individuals with distinct EEG patterns are continuously introduced. Inspired by the synaptic homeostasis mechanism in the human brain, SPICED addresses the long-standing stability-plasticity dilemma in continual learning.\n\nBy bridging biological principles with neuromorphic continual learning, SPICED opens new directions for bio-inspired unsupervised learning systems, particularly in brain–computer interface (BCI) and non-stationary signal decoding applications.\n\nSTRENGTHS AND WEAKNESSES:\nQuality: The paper presents a well-motivated and carefully implemented framework for continual learning in EEG decoding, grounded in biological principles.\n\nClarity: The paper is clearly written and well-organized. Key concepts such as critical memory reactivation, consolidation, and renormalization are clearly introduced and motivated through biological analogies.\n\nWeaknesses: The paper does not provide details on the computational overhead or resource requirements of SPICED, particularly in terms of memory expansion and replay mechanisms. Understanding the scalability of the model is crucial for real-time BCI applications.\n\nThe paper does not analyze where SPICED may fail or behave suboptimally—e.g., in the presence of low-quality EEG signals, highly non-overlapping user domains, or very long task sequences.\n\nQUESTIONS:\n1) To what extent are the bio-inspired mechanisms in SPICED necessary for performance, versus engineering design choices that could be replaced with other techniques (e.g., replay buffers, attention-based routing)?\n\n2) How does SPICED handle extremely noisy EEG signals, domain shifts across populations (e.g., clinical vs. healthy), or longer task sequences?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}