Title: Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: Plasticity-Driven Learning Framework

Spiking Neural Networks

Bio-Inspired
Abstract: The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into a new paradigm for Spiking Neural Networks (SNNs): a Plasticity-Driven Learning Framework (PDLF). This paradigm diverges from traditional neural network models that primarily focus on direct training of synaptic weights, leading to static connections that limit adaptability in dynamic environments. Instead, our approach delves into the heart of synaptic behavior, prioritizing the learning of plasticity rules themselves. This shift in focus from weight adjustment to mastering the intricacies of synaptic change offers a more flexible and dynamic pathway for neural networks to evolve and adapt. Our PDLF does not merely adapt existing concepts of functional and Presynaptic-Dependent Plasticity but redefines them, aligning closely with the dynamic and adaptive nature of biological learning. This reorientation enhances key cognitive abilities in artificial intelligence systems, such as working memory and multitasking capabilities, and demonstrates superior adaptability in complex, real-world scenarios. Moreover, our framework sheds light on the intricate relationships between various forms of plasticity and cognitive functions, thereby contributing to a deeper understanding of the brain's learning mechanisms. Integrating this groundbreaking plasticity-centric approach in SNNs marks a significant advancement in the fusion of neuroscience and artificial intelligence. It paves the way for developing AI systems that not only learn but also adapt in an ever-changing world, much like the human brain.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper applies Hebbian plasticity to spiking neural networks (SNNs) for multi-task reinforcement learning and working memory tasks, highlighting its resilience to synaptic damage and ability to generalize to new tasks as a key strength. However, it lacks references and baselines for comparison, such as evolving Hebbian plasticity in ANNs and meta-learning approaches. Some in-depth analyses to resilience to injuries would also be preferable.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- This work represents a (probably) pioneering contribution by extending Hebbian plasticity to spiking neural networks (SNNs) and is (probably) among the first to apply evolving plastic SNNs to multi-task reinforcement learning (RL) and working memory tasks.  \n-  The experiments on permanent synaptic damage provide compelling evidence for the resilience of the evolved plasticity rule, offering insights into the model’s robustness.\n\n**Weaknesses**\n- The significance of temporary synaptic injury experiments is questionable, as synaptic weights are always reset to zero during evolution (a design choice to mimic biological plasticity). Many existing works initialize weights from zero or randomly [3], and it may not provide additional insights. \n- While the SNN implementation is novel, the plasticity rule closely resembles that proposed in [1] for artificial neural networks (ANNs). \n- The paper lacks critical references and baselines for comparison. Including benchmarks with evolving Hebbian plasticity + ANNs [3,4,5] and meta-learning approaches [2] would strengthen the analysis. Even if SNNs are harder to train, such comparisons could contextualize performance and inspire future hybrid models.  \n- The computational demands of evolving plastic SNNs should be quantified and compared to plastic ANNs. Discussing how Hebbian plasticity’s inherent cost impacts scalability would address a key practical barrier.  \n- This paper would also be strengthened by expanding the discussion on permanent injury experiments, which could draw meaningful parallels to compensatory mechanisms observed in biological neural systems. E.g., show how the other parts of synaptic weights behave when part of the synaptic weights are damaged compared with the normal case.\n- The rationale for the specific parameter choices in Table 2 (e.g., neuronal time constants) should be clarified. Are these standard defaults in the SNN literature or artificially designed? Were they possible to be optimized alongside plasticity rules? Why or why not\n\n\n[1] Soltoggio, Andrea, et al. \"Evolutionary advantages of neuromodulated plasticity in dynamic, reward-based scenarios.\" Proceedings of the 11th international conference on artificial life (Alife XI). MIT Press, 2008.\n\n[2] Mishra, Nikhil, et al. \"A Simple Neural Attentive Meta-Learner.\" International Conference on Learning Representations. 2018.\n\n[3] Najarro, Elias, and Sebastian Risi. \"Meta-learning through hebbian plasticity in random networks.\" Advances in Neural Information Processing Systems 33 (2020): 20719-20731.\n\n[4] Joachim Winther Pedersen and Sebastian Risi. Evolving and merging hebbian learning rules: increasing generalization by decreasing the number of rules. In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 892–900, 2021.\n\n[5] Wang, Fan, et al. \"Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning.\" Transactions on Machine Learning Research.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n**Motivation**\n\nThe human brain's evolution has enabled complex synaptic plasticity, allowing dynamic adaptation to changing environments.\nThis biological insight inspires a new paradigm for Spiking Neural Networks (SNNs).\n\n**Plasticity-Driven Learning Framework (PDLF):**\n\nShifts focus from traditional SNNs that directly train static synaptic weights to learning plasticity rules themselves.\nMimics biological synaptic behavior, prioritizing dynamic adaptability over fixed connections.\n\n**Methodological Innovation**\n\nRedefines concepts of functional and presynaptic-dependent plasticity to align with biological learning dynamics.\nEnables AI systems to master the \"intricacies of synaptic change,\" enhancing flexibility in evolving environments.\n\n**Contributions**\n\nCognitive Enhancements: Improves artificial intelligence (AI) capabilities such as working memory and multitasking.\nDemonstrates superior adaptability in complex, real-world scenarios.\n\nTheoretical Insights: Reveals relationships between plasticity mechanisms and cognitive functions.\nAdvances understanding of brain-inspired learning processes.\n\nPDLF represents a significant step forward in bridging biological principles (e.g., synaptic plasticity) with computational models, enabling more biologically plausible and adaptive AI\n\nSTRENGTHS AND WEAKNESSES:\nStrength: The paper is well-written, and the idea is interesting to me. The experimental results are good with clear and diverse visualization.\n\n Weaknesses can be seen below.\n\nQUESTIONS:\nI am not very familiar with the field of pulse neural networks. However, I am quite familiar with the field of plasticity. I think from the perspective of SNN, the motivation and interest of the entire article are clear. But when evaluating this research from the perspective of general plasticity studies, it raises a question: \n\nThere are many studies on the plasticity of traditional networks, such as dynamic topology [1,2], adaptive reset [3], and replace activation function [4], which have all demonstrated good performance, especially in reinforcement learning tasks.\nHowever, this article does not conduct a discussion or experimental comparison with these works. I hope the author can discuss these works and further point out the value and advantages of this work.\n\n[1]: Growing with Experience: Growing Neural Networks in Deep Reinforcement Learning\n\n[2]: Neuroplastic Expansion in Deep Reinforcement Learning\n\n[3]: The Dormant Neuron Phenomenon in Deep Reinforcement Learning\n\n[4]: Loss of Plasticity in Continual Deep Reinforcement Learning\n\nLIMITATIONS:\nThe experimental tasks in the reinforcement learning section are too simplistic, resulting in a relatively low level of confidence in the method. I suggest following previous related papers and selecting more benchmarks such as DeepMind Control/Meta World to further demonstrate the effectiveness of the method.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel approach in which learning is driven not by the direct optimization of synaptic weights but by the evolution of plasticity rules themselves. The authors propose a framework, they call PDLF (Plasticity-Driven Learning Framework), based on a parametric formulation of local plasticity, combining Synaptic Cooperation Plasticity (SCP) and Presynaptic-Dependent Plasticity (PDP). The key claim is that this framework enables spiking neural networks (SNNs) to dynamically adapt to new environments, exhibit improved working memory, and generalize better to tasks beyond those seen during training.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\nOne of the strengths of the paper is its experimental breadth. The authors evaluate their model across working memory tasks, multi-task reinforcement learning environments, and tests of robustness to both temporary and permanent neural impairments. These results are compelling and suggest that evolving plasticity rules can lead to more adaptive and robust behaviors in SNNs. The ablation studies are demonstrate that both components of the plasticity rule (i.e. SCP and PDP) are necessary for stable and effective learning. The visualizations of neuron states and weight adaptations are convincing in illustrating the dynamic adaptability of the model.\n\nWeakness:\nThe paper should compare its contributions to closely related prior work, such as the paper by Najarro and Risi (2020), \"Meta-learning through Hebbian plasticity in random networks\", which was not cited. That work similarly explores meta-learning via evolved plasticity rules (also using evolution strategy), and some experiments in the present paper, such as resetting synaptic weights to zero during a task or testing performance under morphological damage, mirror experiments from Najarro & Risi almost exactly. The main difference is that Najarro&Risi did not use spiking neural network. I was surprised to find this paper was not cited. \n\nI would also suggest to add a comparison to a non-spiking baseline, to highlight the advantage of the spiking setup. The authors do not investigate whether the benefits they observe are inherent to the spiking nature of the network or if similar results could be achieved using a plasticity-driven framework in standard ANNs. Given the computational overhead of simulating SNNs and the broader interest in biologically inspired learning, it would be valuable to understand what, if any, unique advantages SNNs offer in this setting. Without this comparison, the motivation for using SNNs remains a bit unsubstantiated.\n\nQUESTIONS:\nThe authors suggest that their agents can learn during their lifetime via plasticity, but it is not entirely clear how much of the observed behavior is due to actual online adaptation as opposed to generalization from evolved plasticity parameters. For instance, in the generalization experiments where agents face new task directions or speeds, the agents perform well without receiving any new rewards. While this might suggest an ability to adapt, it could also reflect interpolation based on prior experience. It would be great if the authors could comment on this further.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a plasticity-driven learning framework for SNNs that shifts focus from tuning fixed synaptic weights to learning the rules of synaptic change. It defines two plasticity rules with trainable parameters that are optimized by an evolutionary method so that the network can adjust its own plasticity. The trained networks store memories in their connections rather than in ongoing activity, show stronger working memory, learn several control tasks with one network, recover after their weights are reset, and adapt to tasks they have not seen before.\n\nSTRENGTHS AND WEAKNESSES:\nThe framework provides a new learning approach that takes synaptic change rules rather than fixed weights as the main goal of training, which provides the network with greater flexibility and long-term learning capabilities. PDLF reduces ongoing neural activity by storing information in connections, allowing a single network to master several consecutive control tasks without separate training. It can restore its functionality when all weights are cleared or some connections fail due to its built-in plasticity, showing strong generalization capabilities. Overall, the idea of ​​this article is quite interesting. However, several concerns remain:\n1. This article almost only compares itself, and lacks comparison with similar/related works. It is difficult to know whether PDLF is truly effective compared to previous plasticity-based learning methods. In addition, this paper lacks experimental results on general tasks（e.g., image/speech classification), and it is impossible to know whether PDLF is effective in practical scenarios.\n2.  The PDLF relies on complicated plasticity parameters that can take a long training/inference time and use lots of computational power. Therefore, it is unknown whether PDLF is scalable. However, STDP, a classical plasticity-based learning method, has been used on the relatively complex Cifar-10 and Caltech 101 datasets [1].\n3. Although the paper spends a lot of space explaining the gain of PDLF on working memory, it seems unclear why working memory is important. If the importance of working memory can be linked to the model performance and explained accordingly, it will enhance the quality of this article.\n\n[1] Liu et al. Sstdp: Supervised spike timing dependent plasticity for efficient spiking neural network training. Frontiers in Neuroscience, 2021.\n\nQUESTIONS:\n1. Can the authors provide a performance comparison of PDLF on practical tasks with plasticity-based learning methods?\n2. Can the authors provide a comparison of PDLF with other plasticity-based learning methods in terms of training/inference time/computational complexity, and the required computational resources?\n3. Can the author provide an empirical analysis to illustrate the importance of working memory?\n\nI am willing to discuss with the author and adjust my score accordingly.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}