Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
Authors: Guobin Shen
Dongcheng Zhao
Yiting Dong
Yang Li
Feifei Zhao
Yi Zeng
Venue: NeurIPS 2025 poster ()
Keywords: Plasticity-Driven Learning Framework
Spiking Neural Networks
Bio-Inspired
Primary area: neuroscience_and_cognitive_science

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.

Learning the Plasticity: Plasticity-Driven Learning Framework in Spiking Neural Networks
Guobin Shen 1.2.3+ Dongcheng Zhao 12.4 . Yiting Dong",
Yang Li Feifei Zhao Yi 1..34.2.3 1.2.3,4
Beijing Institute of AI Safety and Governance (Beijing-AIS) "Beijing Key Laboratory of Safe Al and Superalignment
'BrainCog Lab, CASIA *Long-term Al
(shenguobin2021, zhaodongcheng2016 dongyiting2022 liyang2019, zhaofeifei2014, yi.zeng @ia ac. cn
Abstract
The evolution the human brain has led the development of complex synap- tic plasticity, enabling dynamic adaptation to a constantly evolving world. This progress inspires our exploration into new paradigm for Spiking Neural Networks (SNNs): Plasticity-Driven Learning Framework (PDLF). This paradigm diverges from traditional neural network models that primarily focus on direct training 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 Plasticity but redefines them, aligning closely with the dynamic and adaptive nature of biological learning. This reorientation enhances key cognitive abilities in artifi- cial intelligence systems, such working memory and multitasking capabilities, and demonstrates superior adaptability in complex, real-world scenarios. Moreover, our framework sheds light 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-cent approach in SNNs marks a significant ad advancement in the fusion of neuroscience and artificial intelligence. paves the way for developing AI systems that only learn but also adapt in an ever-changing world, much like the human brain.
1 Introduction
Synaptic plasticity, characterized by the ability of synapses to strengthen or weaken over time, underpins learning and memory in the human brain. This adaptive mechanism allows for dynamic responses to an ever-evolving environment, manifesting across various levels from molecular to neural networks [ 2
6 Z Its significance is widely recognized; however, the direct application of synaptic plasticity as an optimization algorithm in artificial neural networks presents notable challenges.
One of the key challenges arises from the diversity learning rules observed biology, such as Long- Term Depression (LTD), Long-Term Potentiation (LTP), and Maieee-Tiinnn--eee Plasticity
'Equal contribution.
"Corresponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(STDP). These mechanisms, although instrumental in understanding and implementing functions like learning and memory, are often challenging to directly apply in neural network optimization due to
their complexity and subtlety 8 9 10: 11. Traditional modeling methods, inspired by biological
evidence, struggle to capture the inherent dynamism of neural systems and require meticulous hand-design or integration with deep learning optimization techniques.
Spiking Neural Networks (SNNs) adeptly emulate the discrete spike sequence information transmis- sion found in biological nervous systems, intricately modeling the dynamics of biological neurons. The intrinsic wvent-driven and real-time nature of information processing in SNNs grants them an enhanced capabiliity for managing tasks with temporal dynamics, outperforming traditional Artificial Neural Networks (ANNs) these aspects [12]. While backpropagation has been established as a foundational technique in neural network optimization [T3] [14, its precise analog in biological sys- tems remains controversial, raising questions about the feasibility of replicating complex biological tasks using such al algorithms [1] Another way to optimize SNNs is to draw on plasticity mechanisms in biology. This approach replicates the challenges of complex tasks based on local synaptic plasticity rules observed in biological systems. However, enhancing the learning ability of SNN through various learning rules often relies on the coordination of manual presets and lacks the flexibility to adapt to
changes in different environments [16 D7 19. 20 21 21,. limiting the development of this field.
We argue that the main challenge in this field stems from the rigid application of observed biological plasticity mechanisms in neural network design. There is a lack of ubstraction and deeper understand- ing of these mechanisms, leading to models that do not fully exploit the adaptability and learning potential of synaptic changes. To overcome these limitations, we propose an innovative approach that shifts the focus from traditional synaptic weight adjustments to learning the principles of plasticity. We advocate for an abstract and parametric modeling of plasticity, aiming to learn and adapt the rules of plasticity within the network. This paradigm shift aligns more closely with the dynamic nature of biological learning and opens avenues for developing more robust and adaptable neural network models.
Our contributions can be summarized as follows:
We propose abstract and parametric modeling of biological plasticity, providing a higher level of generalization and summary of local plasticity, leading more flexible forms and better generalization capabilities.
We introduce the PasticityyDrrvver Learning Framework (PDLF), emphasizing the under- standing and application of plasticity rules over traditional synaptic weight adjustments, allowing neural networks to evolve and adapt in dynamic environments.
This approach potentially enhances the generalization and multitasking abilities SNNs in dynamic and real-world scenarios, offering a platform for continuous learning and adaptation, mirroring the extraordinary capabilities of biological nervous systems.
2
Related Work
Research biologically plausible neural networks has advanced along several interconnected paths, with our work building upon and extending these foundations in novel directions.
Training Methods for SNNs. Recent advances in SNN training follow two primary paradigms: surrogate gradient techniques (22] 23 24 25 26 27 that approximate backpropagation despite
non-differentiable spiking functions, and ANN-to-SNN conversion methods [28] 29 that leverage established deep learning techniques. Though effective, these approaches rely on saudient-based optimization whose biological plausibility remains controversial and confine SNNs within traditional iologically-insprree
alternatives, including STDP- based learning [8) swwrddmoodultted plasticity [30], and three-factor rules BI), better align with biological principles but implement fixed plasticity rules that fail to match the adaptive dynamics of biological systems while typically underperforming compared gradient- based methods.
DNN frameworks, limiting their adaptability.
Adaptable Plasticity Mechanisms. Traditional implementations of synaptic plasticity in artificial systems rely on hand-designed rules with fixed parameters (22 limiting their flexibility across diverse tasks. Recent work has begun exploring meta-learning approaches for plasticity, including dif-
ferentiable plasticity frameworks 33 34 35 and neuromodulated plasticity (36). These approaches

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allow for some adaptation of plasticity rules but typically maintain a separation between weight optimization and plasticity rule optimization.
Our PDLF advances this line of research by fundamentally reorienting the learning process to focus on mastering the principles of plasticity themselves, enabling dynamic adaptation across diverse conditions. Unlike previous approaches that primarily use plasticity as a mechanism for weight updates, our framework treats plasticity as the central object of learning, allowing for the emergence of task-appropriate adaptation mechanisms.
Memory and Adaptation in Neural Networks Working memory in biological systems involves complex interplay between neural activity and synaptic dynamics. Traditional models emphasize persistent neural activity (37] while recent theories highlight the role of short-term synaptic plasticity
in maintaining information without continuous firing 338 39
In artificial systems, evolutionary strategies have been employed to optimize neural network pa- rameters 40 41 and, more recently, plasticity rules 42 36 However, these approaches typically optimize fixed rules rather than developing mechanisms that can continuously adapt throughout an agent's s lifetime.
Our work advances these efforts by developing parametric framework for plasticity that enables continuous adaptation through the synergistic interaction of Synaptic Cooperation Plasticity (SCP) and Trespnapti-DDeeendett Dependent Plasticity (PDP). This approach enhances generalization and adaptation capabilities, particularly in scenarios involving unexpected perturbations or tasks not encountered during training.
Method
3.1 Neuron and Synaptic Models
We employed leaky integrate-and-fire (LIF) neurons in our network models due their biological plausibility and computational efficiency. The state of each LIF neuron was represented by its membrane potential, which integrated the incoming signals and generated a spike when the potential crossed predefined threshold, as shown in Eq.
Or Ø1
= (w un) + ((t)
(I)
In Eq. [ Tm is the membrane time constant, v membrane potential, ut the resting potential, and [tt) is the total synaptic current at time Once the membrane potential e exceeds certain threshold Uyh, the neuron generates a spike, and the potential is reset. discrete form of the LIF neuron's 's behavior can be described as:
At
u(t) v(t At) +
w;s:(t) v(t At) vo)
Tm
s(t) = g(u(t) v(h) (t) u1t)(1 s(t)) (())
(2)
In Eq. 2 At is the time step, Ureeet is the reset potential, u(t) and v(t) represent pre- and post-spike membrane potentials, and g(-) is the Heaviside function modeling spiking behavior. After the loss of the reward signal, updating the network weights stops. This strategy of directly optimizing the weights is set as control group in our experiments. Neuronal parameters are given in Tab. unless otherwise specified.
Traces are the tracks produced at the pre- and post-synaptic sites by the spikes of pre- post-synaptic neurons. Generally, these traces represent the recent activation level of pre- and post-synaptic neurons 33 Traces can be computed by integrating spikes using linear operator in the model and low-pass filter the circuit or by using non-linear operators/cireuits the experiments, the synaptic traces were modeled as follows:
3

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x(1) Σ X ((T)
In Eq. 3 r(t) is the synaptic trace at time t, À the decay factor reflecting how quickly spike's influence fades with time, and s(r) represents the spike at time In the context of our experiment, these synaptic traces maintain a short-term history of neuron activation, thereby adding an element of temporal dynamics to our network model. As shown in Eq. 4 these synaptic traces are used to maintain short-term history of neuronal activation and, in conjunction with PDLF, to modulate synaptic weights.
3.2 Plasticity-Driven Learning Framework
Aw,, = (A, +C,++
B + DrJ
Synaptic Cooperation Plasticlty (SCP)
Presynapii-Dependdeee Plasticity (POP)
Ocn
Action
Gerneration
= tyre goor (ms)
Observation Reward
Gerneration 0
Figure Diagram of PDLE. Top: By combining Synaptic Cooperation Plasticity (SCP) and Sesyyaapti--ependeee Plasticity (PDP), neurons achieve diverse and heterogeneous plasticity. Bottom: Agents with PDLF lear plasticity rather than directly adjusting weights. Different forms of synaptic plasticity be formed between neurons, enabling better multi-task learning. Plasticity helps the agents dynamically adjust weights and learn previously unseen scenarios during training, without explicit reward signals.
The realm of Spiking Neural Networks (SNNs) has witnessed the identification of multiple local plasticity mechanisms, such as Teee---miiin-epeee
Plasticity (STDP) (3] and Bienenstock- Cooper-Munro (BCM) rules [43]. However, abstracting and refining these biological observations into concrete plasticity mechanisms suitable for diverse complex tasks remains formidable challenge. To circumvent excessive manual design and simple combination of different plasticity mechanisms, we introduce more abstract level of plasticity: a parametric plasticity framework.
Adhering to the fundamental rule of local plasticity that synaptic strength influenced by pre- and post-synaptic neural activity design our framework based on parametric expansion of the Spiking BCM rule 44 The PDLF comprises two key components: Synaptic Cooperation Plasticity Plasticity (PDP). SCP dynamically adjusts synaptic strength by
(SCP) and Deesynaptic-Dependdee considering the activity of both pre- and post-synaptic neurons. In contrast, PDP adjusts based on pre-synaptic activity alone and introduces bias synaptic changes for stability. The synaptic weight update in our framework is formulated as follows:
Awiy
Aijzzla - Ci.))
+
Bij + Di.
(4)
Synaptic Cooperation Plasticity (SCP)
Presynaptic- Plasticity (PDP)
In Eq. 4 Awiy represents the change in synaptic weight between neurons i and j. and zi represent the spike traces 32 of pre- and post-synaptic neurons respectively. The parameters Aij. B.g. Cag, and Di,j are learnable, enabling the network to form distinct and adaptable plasticity rules.

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SCP, represented by the term A - Ci adjusts synaptic strength based on the temporal correlation of neural activities, with C2 acting as threshold for post-synaptic activity. PDP, denoted by " + D..j. modifies synaptic weights based on pre-synaptic activity, with Di.j providing stable bias for each neuron. The learning rate 1/ scales the overall synaptic weight change.
To optimize these parameters, we employ an Evolutionary Strategy (ES) 41) inspired by the natural selection processes shaping biological organisms. In this context, the parameters of the plasticity- centric learning rule can be viewed as intrinsic priors, optimized throughout the evolutionary process to ensure survival and adaptation. The ES involves a population of agents, each with a unique set of plasticity parameters, with their fitness evaluated based on adaptability and performance in various tasks. Through this process, the parameters are optimized, enabling agents to maintain flexible and dynamic adaptation throughout their lifespan.
4 Results
4.1 PDLF Enhances Working Memory Capacity
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Figure 2: PDLF impact on Working Memory, A. Copying task schematic: SNNs receive motion stimuli (200ms each). followed by variable delay period, then reproduce stimuli upon receiving test signal. B. Performance comparison: SNNs with plasticity show faster convergence, longer memory duration, and greater capacity. 'Len' =stimulus length, ddal'= steps. C. Distinct synaptic weights formed for different stimuli with PDLF (input weights left of dashed line, output weights right). D. Neuron states comparison: directly trained SNNs require sustained activity during delay; PDLF-based SNNs encode stimuli in synaptic weights, preserving memory even after membrane potential reset. E. Firing rates; SNNs with PDLF maintain lower firing rates across stages.
In this section, aim demonstrate the effect of PDLF Working Memory (WM). WM is the abil- ity to maintain and process information temporarily and is the cornerstone of higher intelligence 45
We explore the significant impact of PDLF enhancing the WM capabilities of SNNs. We utilize a task known as the copying task (46), as illustrated in Fig. ZA. In this task, SNNs are initially presented with sequence of stimuli, each lasting for 200 ms. This is then followed by delay period of variable lengths, and finally, a test stimuli of equivalent duration to the sample stimuli is presented.
The challenge for the SNNs lies in accurately reproducing the initial sequence of stimuli in the correct order upon receiving the test stimuli.
To thoroughly demonstrate the advantages of employing a PDL.F-based approach in working memory tasks, we carry out a comparison with the strategy of directly optimizing weights utilizing the ES based on widely used three-layer SNNs. As shown in Fig. 2B, SNNs equipped with PDLF exhibit
5

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faster convergence rates, an ability to retain memory over longer durations, and an enhanced memory capacity.
To further investigate the influence of PDLF, we visualize synaptic weights following various stimuli inputs when the stimuli length is set to 8. As shown in Fig. Fig.2C. SNNs endow with PDLF can form distinct connection weights for different stimuli, demonstrating their superior adaptive capacity.
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Figure 3: PDLF in multi-task RL and plasticity analysis. A. Multi-task RL setup: single network learns tasks with distinct/opposing objectives, with task goals as input observations. B. Training curves comparing PDLF versus directly trained weights across direction tasks (ant_di r. swi mmer__iv velocity tasks (halfcheet:
vee, hopper_ __eel,, 1), and target location tasks (fet ch, ur PDLF enables superior multi-task performance through dynamic weight adaptation. C. Ablation study showing both SCP and PDP components are essential; removing PDP causes divergence due to loss of equilibrium. D. Evolution of synaptic plasticity parameters during training. E. Impact of plasticity on weights at different inter-spike intervals across training generations. F. Functional characteristics of plasticity across generations shown E.
We compare the neuronal states at various stages between SNNs directly trained with weights and those incorporating PDLF, as illustrated in Fig. DD. SNNs directly trained with weights rely on neuronal activity during the delay period to maintain memory. Resetting the membrane potential to 0 after the stimulus input leads to memory loss for input stimuli. This indicates that in SNNs directly optimized with weights, memory is primarily stored in neuronal activity. In contrast, SNNs incorporating PDLF encode the input stimulus into synaptic weights, demonstrating remarkable memory capabilities. Their ability to adjust synaptic weights facilitates the enhancement of working memory and allows neurons to remain in a resting state when not receiving task-related stimuli. This contributes to network efficiency and enhances network capacity, which has been validated in other biological and computational neuroscience studies [3 39].
Finally, we visualize the average spike traces of SNNs under different training strategies, as shown in Fig. E Notably, SNNs incorporating PDLF can sustain lower firing rates, thereby enhancing their efficiency in managing computational resources.

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In summary, these results highlight the significant role of PDLF in bolstering the working memory capacity of SNNs, demonstrating its potential for facilitating complex cognitive tasks in artificial intelligence systems.
4.2 PDLF Enhances Multi-task Learning
Reinforcement Learning (RL) involves an agent learning to interact with its environment to achieve a specific goal, with the quality of its actions dictated by reward signal. Conventional RL approaches often entail training the agent on a single task with fixed reward function. However, complex, real-world environments, agents need to handle multiple tasks and adjust to changing reward functions. This multi-task learning scenario presents significant challenges, mainly when the tasks involved are diverse and potentially conflicting.
In the field of RL, some of the most challenging problems lie within the domain of continuous control, usually modeled using sophisticated physics engines. These tasks require agents to manipulate simulated physical entities with high precision and coordination, similar to how humans control their limbs to carry out complex tasks. We use the Brax [47] simulator to design six continuous control environments. In these settings, agents need to navigate at various speeds, directions, and destination points. As shown in Fig. 3A, different task objectives are treated as observations to guide the agent in accomplishing different tasks. These challenging tasks serve as baselines fields such as meta-learning [48) 49 50). During training, they are only exposed to a limited number of task instances, such as eight specific directions or eight fixed speeds. They use a single network to learn these unrelated or conflicting tasks.
Our experiments compare two types of SNNs one with synaptic weights that have been directly optimized and another with optimized PDLF Both types of SNNs maintain the same scale and structure ensure fair comparison. Through this, we aim to highlight the advantages of optimizing PDLF over direct weight optimization in multi-task environment. This comparison also evaluates the effectiveness and potential of our proposed model, especially the face of the inherent challenges posed by complex continuous control tasks.
Table 1: Comparison of performance across various reinforcement learning tasks with different configurations of synaptic plasticity. Each task is evaluated using different training methods: PlasticitysCP+PPD PCP+ PlasticityscP. Plasticity pp, and direct weight training. The values presented are the mean and standard deviation over trials. The row 'Chance Level' shows the performance metrics when randomly chooses actions. Upon the removal of Seesynapti--Dependeee Plasticity (PDP), the SNNs become unstable, leading to divergence.
Training
ant__ii
swimner_di _di halfcheetah _eel
hopper_vel
fetch
ur5e
OptsCP_PPP
6904 +: 801
10531 +:2 827 7831 1 1479
-649 + 95
869 = 38
61 #
86 86:5
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3284 1 570
-870 312
792 1 50
26 1. 26
58 1: 13
Optsee
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1069 + 98
31 + 8
-1596 + 324
729 116
15 1.6
7+5
Chance Level
995 1 0.01
0.12 1. 0.02
-4946 1 1.3
6.51 1 0.3
4.74 1.741.0.0
0 0.0
We explore the performance of PDLF in three-layer, fully-connected SNN model with 128 hidden spiking neurons. Both the synaptic weights and the plasticity parameters are initialized to 0. During testing, their plasticity rules are fixed for agents with plasticity, and synaptic weights are reset to 0. The trained weights are applicd during testing for agents trained directly on weights. We utilize reinforcement learning tasks to thoroughly test PDLF, requiring the agents to learn to cope with different tasks simultaneously and generalize the acquired knowledge to unseen, more complex tasks.
In our experiments, agents with PDLF show superior performance in multiple tasks compared to agents with directly optimized synaptic weights. As seen in Fig. 33 and Tab agents with PDLF exhibit more effective learning curves. The agents with PDLF quickly adapted to the changes in tasks, making them more capable of tackling the multi-task challenges inherent in the designed environments. In contrast, SNNs with directly trained weights fail to adapt to different tasks and can only acquire trivial solutions, such as maintaining approximate immobility in tasks involving multiple target directions.
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To further validate the effectiveness of our approach, we compare PDLF against Meta-Hebb [5]. a ofateoof-the-ar meta-learning approach for ANNs that also learns plasticity rules, As shown in PDLF demonstrates superior performance across multiple tasks, highlighting the advantages of learning plasticity rules within the SNN framework.
Table 2
Table Comparison with meta-learning baselines on multi-task RL environments. Values represent
mean + standard deviation over 5 trials.
Architecture
Method
ant_dir
swimmer _dir
hal fchheththh__e
hopper_ver
ANN
Meta-Hebb
5646 1 543
7605 891
-735 1 124
794 1 67
SNN
PDLF
6904 + 801
10531 = 827
-549 + 95
869 + 38
Ablation studies, depicted Fig. 3C and Tab. [ provide further insights into the contributions of different plasticity mechanisms. Removing any form of plasticity results in decreased agent performance, with the removal of PDP causing a divergence due to the loss of the equilibrium mechanism. This result highlights the importance of all plasticity mechanisms in maintaining the stability and adaptability of the agents.
The changing curve of a synapse's 's PDLF during training (Fig. BD) along with the impact of plasticity on weights for different generations of agents (Fig. (3)), demonstrate the effective learning of optimal parameters for the PDLF rule, facilitated by the evolutionary strategy. Interestingly, the specific functions of plasticity across different generations of agents differed (Fig. 3 indicating the evolution and fine-tuning of plasticity mechanisms to improve agent performance across generations.
4.3 PDLF Enhances Generalization Ability
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Figure 4: Robustness against neural injuries. A. Recovery from temporary damage: When weights
reset 0 at step 500 for 50 steps, PDLF agents recover functionality, showing superior robustness. B. Weight visualization before/after temporary damage: Input layer weights (above dashed line) and readout layer weights (below) show recovery based on plasticity and input stimuli despite complete weight loss. C. Response to permanent damage: With varying proportions of neurons blocked (weights fixed 0), PDLF agents maintain better performance, demonstrating enhanced adaptability to irreversible neural impairment.
PDLF serves as an important mechanism for enhancing an agent's 's generalization abilitics, enabling the agent to exhibit stronger performance when dealing with unfamiliar tasks or when facing neuronal damage.
We further investigate the performance of PDLF when the agents faced injuries. We design two different types of injuries: temporary injuries and permanent injuries. Temporary injuries refer to a scenario where all synaptic weights of the agents are reset to 0 and kept for 50 steps. Permanent injuries refer to a situation where some synapses are set to initially and do not update according to plasticity. In the tests for injuries, agents with plasticity display a remarkable ability to recover from temporary neuronal damage simulated by resetting all synaptic weights to for 50 steps (Fig. (Fig.FAA). Fig. 4B illustrates the changes in network weights at various stages before and after the temporary
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damage. Remarkably, despite losing all synaptic weights due to the inflicted temporary damage, agents manage to recover these weights using their inherent plasticity and incoming input stimuli. Moreover, even under permanent neuronal damage, with proportion of neurons blocked and their weights unable to update, the plasticity-enn enabled agents continue to exhibit better performance and robustness (Fig. 4C). These results suggest that PDLF can contribute to the resilience of artificial agents, much as it does in biological systems.
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Figure 5: A. Performance of different agents in trained tasks and tasks not seen during training. Agents with plasticity can generalize well to unseen tasks, while agents trained directly weights have difficulty generalizing to unseen test tasks due to their weights being fixed during testing. B. Low-dimensional embeddings of neuronal states during reinforcement learning tasks, differentiated by training strategy. Each point corresponds to the state of the hidden layer neurons at a specific time step. The color coding signifies distinct tasks. Agents that possess plasticity demonstrate an enhanced capability to distinguish between different tasks. Moreover, the neuronal states associated with identical tasks exhibit the intriguing property of forming manifold within the high-dimensional space.
More strikingly, agents with PDLF show a robust ability to generalize to tasks unseen during training, as demonstrated in Fig. 5/. For tasks with various movement directions and speeds, the agents only encounter a small subset of cases during training, represented by the red and orange points in Fig. 5 Therefore, in this experiment, the agents are required to move in directions and speeds unseen in training, emphasizing the agents' more profound understanding and generalization capacity for the tasks. In contrast, agents trained directly on weights struggle with generalization due to their fixed weights during testing. This observation underscores the flexible adaptability offered by PDLF, enhancing the agent's ability navigate unseen scenarios.
During the training phase, the agents are instructed to move in straight lines in eight specific directions. However, illustrated in Fig.BA. agents with PDLF demonstrate degree of generalization capability. They can learn to move in straight lines toward directions not encountered during training, while agents without PDLF struggle to generalize what they have learned.
To further test the generalization capacity introduced by PDLF, we hope that agents could learn to turn or even form more complex paths simply by changing the target signal and without any additional feedback information related to posture. The results are shown in Fig. 6 Compared to agents that directly train their weights, those with PDLF demonstrate impressive generalization abilities toward this complex task. They can quickly adjust synaptic weights through PDLF and dynamically modify their state in previously unseen scenarios during different pieces of training. This allows them to progress toward varying target directions.
In Fig. 5B, we visualize the neuronal states of agents trained using different strategies during RL tasks in diwdesenniional space. Each point in this representation corresponds to the state of the hidden layer neurons at particular time step, with the varying colors indicating different tasks. The remarkable aspect of these visualizations is how agents with inherent plasticity demonstrate a pronounced ability to discern between distinct tasks. Even more intriguing is the observation that the neuronal states corresponding to the same task tend to cluster together, forming clear manifold in the high- digh-dimessional space. This feature of PDLF contributes to the agent's ''s robust ability to

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5003
X.91
-00
100 250
Figure 6: Testing of the agent's generalization capabilities. During training, the agent only learned to move in a straight line. The agent's movement trajectories are shown when the target direction altered during the testing process. The green line represents the expected trajectory of the agent moving at constant speed. The orange line is the actual movement trajectory of the agent with plasticity. Agents with plasticity can better understand different tasks, adjust synaptic weights according to different target directions, and thus show greater flexibility and superior generalization performance.
generalize across various tasks while maintaining distinctive task-specific patterns in neuronal states. This further illustrates the powerful capabilities of agents with PDLF and the profound impact of such plasticity the agent's 's learning and adaptation abilities.
5
Discussion
Current AI algorithms excel at specific tasks but struggle when conditions deviate from training scenarios, limiting their adaptability complex environments. In contrast, biological systems demonstrate exceptional adaptability through synaptic plasticity, the cornerstone of generalized intelligence [2].
While SNNs offer brain-inspired mechanisms for building flexible intelligent systems 53 54 55
56
current training methods-whethed backpropagation or fixed plasticity rules like STDP- limit generalization in multi-task environments. Our PDLF addresses this through the synergy of SCP and PDP, enabling dynamic plasticity rule adjustment rather than static weight modification. This higher-order learning process fosters adaptive synaptic modifications based neuronal activity history, narrowing the gap between artificial and biological systems.
Our results demonstrate that PDLF enhances memory capacity by encoding information in synaptic weights rather than sustained neural activity, improving energy efficiency. enables superior multi- task learning, generalization to unseen tasks, and adaptation to novel scenarios not encountered during training. Critically, PDLF provides robustness under both temporary and permanent neural damage, mirroring biological recovery mechanisms through neural reorganization-2 vital feature for deployment in dynamic, unpredictable real-world environments.
In conclusion, our study highlights PDLF as a critical feature that enhances the resilience and adaptability artificial agents. These findings provide valuable insights for designing future artificial systems, opening up new possibilities for creating adaptive, robust, and intelligent agents capable of navigating complex and dynamic environments. Further work can explore more sophisticated forms of PDLF and study their impacts on various facets of artificial agent performance.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 62406325).
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A Experimental Settings
Table Parameters of spiking neurons.
Value
Parameter
Description
WM task
RL task
A
20 ms
200 ms
Simulation time step
Tm
ms
400 ms
Membrane time constant
X Uth
54 ms
544 ms
Decay factor
0.1 V
0.1 V
Membrane threshold
Ureaet
O mV
0 mV
Reset potential
Ug
0 mV
mV
Resting potential
A.1 Working Memory Task
To validate the impact of PDLF on working memory, we designed a working memory task. The agent would first receive stimulus sequence, and after delay of m steps, the agent is asked reproduce the received stimulus. In each experiment, a random sequence of length would be generated, where rt (1,, 4), t < n. At each time step, the input three-dimensional vector dz which be divided into three stages:
Stimulus reception: If 1 < S n. ai = (rt, 1, 0). The first element is the type of input stimulus, and the second element the indicator for the input stimulus.
Delay: If + S n + m, de 0,) This phase represents a delay period where no new stimulus is presented.
Stimulus reproduction: If n + + 1 < < 2n + m. a. = (0, 0, 1). The last element indicates whether a pulse needs be reproduced.
At each step, the model has a scalar output st, which is a prediction for the stimulus. The Mean Square Error over the last steps is taken as the reward of the model:
n
Tr Sm+n+77
(5)
Eq. 5 is used as reward function in training. To intuitively compare agents with different strategies, as shown in Fig. [2B we utilize the average accuracy per step as the performance measure during testing, as shown Eq.
Acc
Σ (rs == 8m+n47)
(6)
n
During the stimulus reception stage, each stimulus follows the distribution (11, 2), which means that the average accuracy at the chance level is 0.5.
A.2 Multi-task Reinforcement Learning
We evaluated our method on five continuous control environments based on the Brax simulator (ant dir, swimmer dir, ha welffffheeehhuue vee, hopphr_pppee _ee,,, ur5e, fetch).
ant ant_dir: dir: We train an ant agent to run in target direction in this environment. The training task set includes directions, uniformly sampled from [6,3 degrees. shown in Fig. 3D, the generalization test task set includes 72 directions, uniformly sampled from (0, 360 degrees. The agent's reward comprises speed along the target direction and control cost.
21

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swimmer_ain In this environment, we train swimmer agent to move in fixed direction. The settings for training and testing tasks are similar anr_
environment, we train half-cheetah agent to move forward at a specific speed. The training tasks include speeds, uniformly sampled from [1, 10] m/s. The generalization test tasks include 72 different speeds, uni- formly sampled from the same range as the training tasks.
hal wwlffceeeahhpte
In the weelffhhetah_aye
hopper_ ve: In the hopper_ vel environment, at a specific speed. The experimental setup is the same as hal fallfhheetaheel, but the sampling interval for the speed is [0, m/s.
train a hopper agent advance
ur5e: The UR5e is common 6-DOF (degrees of freedom) robotic arm frequently used in industrial automation and robotics research, The agent receives reward when the distance between the robotic arm's s end and the target position is less than 0.02 m. The target position is then randomly reset. The agent's goal is to reach the target position as many times as possible within the stipulated time.
fetch: We train a dog agent to run to target location in this environment. The experimen- tal setup is similar 5re..
The agent's final reward the average reward across all tasks, which encourages the agent to learn multiple tasks simultaneously.
B Training Strategies
Algorithm Parameter-Explorigg Policy Gradients (PEPG)
1: Initialize the number of generations M, and the population size N
2: Initialize policy parameters
3: Initialize adaptive noise scaling parameters
4: Initialize learning rates agg Og
5: Initialize Adam parameters ma, U9, ma Co $1, $2,
6: Initialize noise standard deviation do
7: for to M do
8:
for n = 1 N do
9: Sample noise ~ ~N
10:
Compute offspring 8' = 8 +
11:
Evaluate fitness s(0))
12:
end for
13:
Compute fitness baseline meam (S(0')
Compute gradients V = 인 MN fi Ji-6; E
14:
15:
Compute adaptive noise scaling gradient Vo 1 E ((f - b)2 02)
16:
Update Adam parameters for 8: ma B mg
(1 81 V.) Uo 3220 + (1-
17:
Update policy parameters = 0 + 0gg tuote
ma
Update Adam parameters for σ: m, 31 my
(1 3,) Vo - + +1-33) -22
19:
Update adaptive noise scaling a exp(Oer (Ctr /o,+c
end for
We employ Parameter-Fxploring Policy Gradients (PEPG) 41 to optimize SNNs. For SNNs with plasticity, the plasticity parameters in Eq. are used for optimization. Evolution across generations is facilitated by modifying synaptic plasticity rules rather than directly adjusting the weights. SNNs with directly trained weights are considered control group, where synaptic weights are the optimization parameters. The implementation of PEPG used in the experiments is provided by Algorithm T Unless expressly stated otherwise, the parameter settings and their explanations are shown in Table The way to compute fitness f() varies depending the task. For the working memory task, fitness is provided by Eq.5, while for multi-task reinforcement learning, fitness is the average episodic reward across different subtasks.
All experiments were conducted on a server equipped with NVIDIA A100 GPUs, each with 40 GB of memory. The implementations were carried out using the JAX framework [57]. For tasks like
22

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Table 4: Parameters in PEPG.
Parameter
Value
Description
B
Initial policy parameters
0.1
Initial adaptive noise scaling parameters
Oa
0.15
Learning rate for policy parameters
Okr
0.1
Learning for adaptive noise scaling
ma:
0,0
Initial Adam parameters for policy parameters
ma Vo
0.0
Initial Adam parameters for adaptive noise scaling Hlppeparaameters of Adam optimizer Adam parameters
B1, 32
0.9,
e
10-8
M
1500
Number of generations
N
128
Number of offspring per generation
working memory, training was performed on single GPU, and it often took only few minutes to complete. For multi-task robotic control, training was performed in parallel across four GPUs. The training duration depended on the dimensionality of the task inputs and outputs, as well as the convergence speed, and ranged approximately from to 40 hours.
C Limitations
While our Plasticity-Drive Driven Learning Framework (PDLF) demonstrates significant advances in the adaptability and functionality of Spiking Neural Networks (SNNs), there several limitations and challenges that need to be addressed for further development and practical application:
Generalization across diverse environments: While PDLF enhances generalization within the scope of tasks similar those encountered during training, its effectiveness across highly diverse or drastically different environments remains less explored. The adaptability of PDLF in scenarios that deviate significantly from trained contexts needs further investigation to understand its limits generalization.
Complexity of Implementation: The implementation PDLF involves intricate parametric modeling of plasticity rules which may increase the complexity of neural network archi- tectures. This complexity could pose challenges in scaling the models for larger and more complex tasks due increased computational demands and parameter tuning requirements.
Optimization challenge: Using evolutionary strategies to optimize plasticity parameters, although inspired by biological processes, may computationally expensive. However, in actual deployment, evolutionary strategy optimization is not required, so it has no significant impact on reasoning.
Addressing these limitations will require continued research efforts, not only to enhance the compu- tational efficiency and applicability of PDLF but also to deepen our understanding of the interplay between artificial and biological learning systems. Further investigations into alternative optimization techniques, robust initialization methods, and comprehensive testing across varied environments will be key to advancing the PDLF approach in the field of artificial intelligence.
23


Reviews:
[{"review_id": "tlL0Yk5vMf", "reviewer": "Reviewer_WGam", "summary": "This 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.", "questions": "1. 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.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "I have decided to raise the score to borderline accept because the authors carefully addressed my major concerns in their rebuttal. However, objectively speaking, this work addresses a rather specialized subfield, which may limit its appeal to a broader audience. Additionally,  from the perspective of the spiking neural network field, and as pointed out by reviewer K8Y9, who has a different background from mine, the evaluation results in the original version of the paper were not comprehensive enough. Given these limitations, I cannot give a higher rating.", "rebuttal": "Thank you for your thoughtful review and constructive feedback. We appreciate your recognition that our work presents an interesting idea with good experimental results. Your concerns regarding comparisons with related work and evaluation on practical tasks are well-taken and have helped us strengthen our contribution. We address each of your points systematically below:\n\n### 1. Comparison with Related Plasticity-Based Methods\n\nWe acknowledge that direct comparisons with existing plasticity-based SNN methods on continuous control tasks are limited. This is because our work addresses a particularly challenging and novel paradigm: multi-task continuous control with generalization to unseen tasks, which represents one of the first attempts in the SNN literature. The task complexity and the requirement for zero-shot generalization make this setting fundamentally different from previous work, which is why we primarily focus on thorough ablation studies to validate our design choices.\n\nNevertheless, we provide a meaningful comparison with the most relevant approach - Meta-Hebb [9], implemented under identical experimental settings:\n\n|Architecture|Method|ant_dir|swimmer_dir|halfcheetah_vel|hopper_vel|\n|---|---|---|---|---|---|\n|ANN|Meta-Hebb[9]|5646±543|7605±891|-735±124|794±67|\n|SNN|PDLF|6904±801|10531±827|-549±95|869±38|\n\nThe superior performance stems from PDLF's novel dual-mechanism design. Unlike Meta-Hebb's single plasticity rule, our framework combines SCP for correlation-based learning with PDP for stability and bias control. This architectural separation allows independent optimization of learning dynamics and stability, as evidenced by our ablation study showing that removing PDP causes training divergence across all tasks (Table 1). Additionally, PDLF operates without requiring gradient information or global reward signals during deployment, making it more biologically plausible and practically deployable.\n\n### 2. Performance on Classification Tasks\n\nWe evaluated PDLF on the MNIST classification benchmark to demonstrate its generality across different task domains:\n\n| Method | STDP[2] | TDSNN[3] | R-STDP[4] | BP-STDP[5] | STDBP[6] | S4NN[7] | SSTDP[8] | Meta-Hebb[9] | PDLF (ours) |\n|--------|---------|----------|-----------|------------|----------|---------|----------|--------------|-------------|\n| MNIST  | 95.0    | 92.0     | 97.2      | 96.6       | 97.9     | 97.4    | 98.1     | 96.7         | 97.7        |\n\nResults for methods [2-8] are from their original papers. Meta-Hebb[9] and PDLF results were obtained under identical experimental settings. Under conditions of purely evolutionary optimization without reliance on gradient-based training, PDLF achieved 97.7% classification accuracy, outperforming Meta-Hebb [9] (96.7%) and performing comparably to various STDP variants. This result validates PDLF's generalization capability from complex control tasks to basic visual tasks, supporting its cross-task adaptability.\n\nWe recognize the value of demonstrating PDLF's performance on more complex visual benchmarks like CIFAR-10. However, PDLF was designed to address critical capabilities such as online adaptation and task memory, which are often difficult to fully demonstrate and evaluate in traditional static image classification tasks. Rather than pursuing extreme classification accuracy, PDLF emphasizes flexibility, robustness, and biological interpretability to meet the demands of continuous adaptation in multi-task control and dynamic environments.\n\nOur evaluation strategy prioritizes dynamic adaptation tasks that showcase PDLF's core contribution: enabling networks to continuously adapt during deployment without retraining. Unlike static classification benchmarks where performance is measured on fixed test sets, our experiments demonstrate capabilities impossible with traditional approaches—recovering from neural damage, generalizing to unseen tasks, and handling multiple conflicting objectives simultaneously. In robotics and autonomous systems, the ability to continuously adapt without retraining represents a fundamental advantage over specialized single-task methods, addressing scenarios where traditional fixed-weight approaches fundamentally fail. We plan to apply PDLF to more complex visual tasks in future work to further examine its scalability and structural compatibility across diverse domains.\n\n### 3. Computational Complexity and Scalability\n\nRegarding computational overhead, it's crucial to distinguish between training and inference phases, as well as consider the unique capabilities gained.\n\n**Training Efficiency**: While evolutionary training requires more computational time initially, this represents a one-time development cost that enables deployment across multiple tasks. We evaluated training efficiency by measuring episodic reward achieved after different wall-clock training times on a single A100 GPU:\n\n|Task|Method|$500$s|$1000$s|$2000$s|$5000$s|$10^4$s|$2\\times10^4$s|$5\\times10^4$s|$10^5$s|\n|---|---|---|---|---|---|---|---|---|---|\n|fetch|$Opt_{Weight}$|6.75|10.69|13.04|14.75|15.37|16.28|17.12|17.7|\n|fetch|$Opt_{Plasticity}$|4.54|5.29|5.93|9.15|25.03|46.04|56.71|63.02|\n|ur5e|$Opt_{Weight}$|9.37|12.38|12.41|12.47|12.54|12.52|13.48|14.42|\n|ur5e|$Opt_{Plasticity}$|0.27|0.38|0.79|12.83|45.58|67.45|86.31|99.27| \n\nWhile PDLF converges more slowly initially, it achieves significantly higher final performance and provides unique capabilities that justify the training investment.\n\n**Deployment Efficiency**: During deployment, plasticity updates occur in parallel with environmental interactions, making the online adaptation overhead minimal and often masked by environment interaction latency. For energy-constrained scenarios, the system can flexibly disable plasticity updates after convergence, trading adaptability for efficiency.\n\n**Multi-task Amortization**: Once trained, a single PDLF network can handle multiple tasks without retraining, amortizing the initial training cost across applications.\n\n### 4. Importance of Working Memory\n\nWorking memory is widely recognized as fundamental to intelligence and cognitive flexibility [10,11]. In AI systems, enhanced working memory directly enables superior sequential decision-making, longer context maintenance, and improved multi-task learning [12,13]. \n\nOur experiments demonstrate that PDLF stores information in synaptic weights rather than sustained neural activity, which is both more energy-efficient and biologically plausible [14,15]. This mechanism allows the network to maintain task-relevant information across trials without requiring continuous neural activation, leading to better performance in multi-task scenarios where context switching is frequent.\n\nThe practical importance is evident in our RL experiments, where enhanced working memory directly contributes to:\n- Superior performance across diverse locomotion tasks\n- Robust recovery from neural damage\n- Zero-shot generalization to unseen task variants\n\nThe versatility of our approach across domains—from multi-task continuous control and neural damage resilience to task generalization and classification—demonstrates that PDLF's advantages extend beyond control scenarios to general learning problems requiring adaptive plasticity mechanisms.\n\nWe will incorporate these additional clarifications and references into our revision. Thank you again for your detailed feedback, which has helped us strengthen our work. We hope you will consider revising your evaluation in light of these clarifications and additional results.\n\n---\n\n### References\n[1] Liu, F., Zhao, W., Chen, Y., Wang, Z., Yang, T., & Jiang, L. (2021). SSTDP: Supervised spike timing dependent plasticity for efficient spiking neural network training. Frontiers in Neuroscience, 15, 756876.\n\n[2] Diehl, Peter U., and Matthew Cook. \"Unsupervised learning of digit recognition using spike-timing-dependent plasticity.\" Frontiers in computational neuroscience 9 (2015): 99.\n\n[3] Lee, Jun Haeng, et al. \"Training deep spiking neural networks using backpropagation.\" Frontiers in neuroscience 10 (2016): 508.\n\n[4] Mozafari, M., et al. \"First-spike-based visual categorization using reward-modulated STDP.\" IEEE Trans. Neural Networks and Learning Systems 29.12 (2018): 6178-6190.\n\n[5] Tavanaei, A., and Maida, A. \"BP-STDP: approximating backpropagation using spike timing dependent plasticity.\" Neurocomputing 330 (2019): 39-47.\n[6] Zhang, M., et al. \"Spike-timing-dependent back propagation in deep spiking neural networks.\" arXiv preprint arXiv:2003.11837 (2020).\n\n[7] Kheradpisheh, S. R., and Masquelier, T. \"Temporal backpropagation for spiking neural networks with one spike per neuron.\" International Journal of Neural Systems 30.06 (2020): 2050027.\n\n[8] Liu, Fangxin, et al. \"SSTDP: Supervised spike timing dependent plasticity for efficient spiking neural network training.\" Frontiers in Neuroscience 15 (2021): 756876.\n\n[9] 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[10] Baddeley, Alan. \"Working memory.\" Science 255.5044 (1992): 556-559.\n\n[11] Diamond, Adele. \"Executive functions.\" Annual Review of Psychology 64 (2013): 135-168.\n\n[12] Graves, Alex, et al. \"Hybrid computing using a neural network with dynamic external memory.\" Nature 538.7626 (2016): 471-476.\n\n[13] Santoro, Adam, et al. \"Meta-learning with memory-augmented neural networks.\" International Conference on Machine Learning (2016).\n\n[14] Mongillo, Gianluigi, et al. \"Synaptic theory of working memory.\" Science 319.5869 (2008): 1543-1546.\n\n[15] Masse, Nicolas Y., et al. \"Circuit mechanisms for the maintenance and manipulation of information in working memory.\" Nature Neuroscience 22.7 (2019): 1159-1167."}, {"review_id": "Rj0ASSOW0X", "reviewer": "Reviewer_K8Y9", "summary": "This 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.", "questions": "The 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.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths: \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.", "was_revised": true, "final_justification": "The authors did a great job at addressing my remaining concerns and I'm revising my score to 5 (accept).", "rebuttal": "Thank you for your comprehensive review and positive assessment of our experimental results. We appreciate your recognition of PDLF's dynamic adaptability and the value of our ablation studies. We address each of your concerns systematically below:\n\n### 1. Comparison with Najarro & Risi (2020)\n\nThank you for bringing up this relevant work. Our research was primarily focused on advancing plasticity mechanisms within the SNN paradigm, exploring how spike-based temporal dynamics can enhance adaptive learning. However, your suggestion to include comparisons with ANN-based plasticity approaches is excellent and provides valuable insights into the broader applicability of our framework.\n\nWe have now included Najarro & Risi (2020)[1] in our discussion and conducted comparative experiments. While both approaches use evolutionary strategies to optimize plasticity rules, there are several key methodological differences:\n\n- **Temporal Dynamics**: Our framework operates on spike traces that capture temporal dependencies, whereas Meta-Hebb uses instantaneous activation values. This temporal aspect is crucial for the dynamic adaptation we observe.\n- **Network Architecture**: Our focus on spiking neural networks introduces unique advantages through their event-driven nature and biological plausibility.\n- **Experimental Scope**: We extensively explore permanent neural damage, multi-task adaptation, and generalization scenarios that demonstrate the robustness of our approach.\n  \nWe conducted experiments using equivalent network architectures with comparable parameter counts, evolutionary parameters, and evaluation protocols. Results averaged over 5 independent runs:\n\n| Architecture | Method        | ant_dir      | swimmer_dir   | halfcheetah_vel | hopper_vel   |\n|--------------|---------------|--------------|---------------|-----------------|--------------|\n| ANN          | Meta-Hebb [1] | 5646 ± 543   | 7605 ± 891    | -735 ± 124      | 794 ± 67     |\n| ANN          | PDLF          | 6438 ± 467   | 9534 ± 712    | -601 ± 98       | 841 ± 52     |\n| SNN          | PDLF          | 6904 ± 801   | 10531 ± 827   | -549 ± 95       | 869 ± 38     |\n\nThe superior performance demonstrates the effectiveness of our dual-mechanism approach and temporal trace-based plasticity formulation.\n\n### 2. Advantages of Spiking Implementation\n\nYou raise an excellent point about substantiating the benefits of the spiking implementation. We provide evidence from two complementary perspectives:\n\n**Empirical Performance Advantages:**\nAs demonstrated in our comparison table, the SNN implementation of PDLF consistently outperforms the ANN version across all tasks. While the improvements may appear modest (~7-10%), they are consistent across diverse tasks and represent meaningful performance gains. This suggests that the temporal precision and dynamics inherent in spike-based processing provide genuine benefits for our plasticity mechanisms, particularly in capturing temporal correlations that are crucial for adaptive behavior.\n\n**Theoretical and Practical Advantages:**\nBeyond empirical performance, SNNs offer several important advantages: (1) Biological plausibility - providing a more direct path toward understanding biological learning principles and brain-inspired AI, (2) Neuromorphic compatibility - enabling deployment on energy-efficient neuromorphic hardware platforms, and (3) Event-driven computation - offering potential computational advantages in sparse or temporal data scenarios. While some of these benefits may not be fully realized in our current dense-input RL tasks, they represent important considerations for the broader applicability and future development of plasticity-driven learning approaches.\n\n\n### 3. Online Adaptation vs. Generalization from Evolved Parameters\n\nThis is an excellent and nuanced question that gets to the heart of our contribution. To address the distinction between online adaptation and parameter interpolation, we conducted controlled experiments testing performance on unseen task variations. We conducted experiments specifically testing performance on unseen task variations (training on 8 tasks, testing on unseen task variations, as shown in Figure 5a). \n\nBaseline Comparisons:\n- **Weight Interpolation**: For each test task, we identified the two nearest trained tasks and linearly interpolated their final network weights\n- **MAML [2]**: Meta-learning approach that performs gradient-based adaptation on new tasks during testing\n- **PEARL [3]**: Context-based meta-learning that infers task identity for adaptation\n\n\n| Method        | ant_dir | swimmer_dir | halfCheetah_vel | hopper_vel |\n|---------------|---------|-------------|-----------------|------------|\n| Interpolation | 1553    | 534         | -2304           | 436        |\n| MAML [2]      | 4763    | 7509        | -831            | 683        |\n| PEARL [3]     | 4706    | 8814        | -663            | 816        |\n| PDLF (ours)   | 6819    | 10526       | -574            | 834        |\n\nOur results demonstrate that PDLF achieves genuine online adaptation rather than simple parameter generalization. The significant performance advantage over weight interpolation rules out the possibility that gains stem from mere parameter interpolation between trained configurations. More importantly, PDLF outperforms sophisticated meta-learning approaches that explicitly perform gradient updates or context inference during testing, while requiring no additional optimization or task identification whatsoever. This demonstrates genuine online adaptation capabilities that emerge from the dynamic interaction between evolved plasticity mechanisms and current environmental demands, rather than simple generalization from stored parameters.\n\nIn addition, there are several pieces of evidence supporting genuine online adaptation rather than mere interpolation in the manuscript:\n\n- **Task-Specific Neural Reorganization**: Figure 5B shows that neural states form task-specific clusters, indicating active reorganization of internal representations rather than simple interpolation.\n- **Recovery from Complete Weight Loss**: The temporary damage experiments (Figure 3A-B) demonstrate that networks can rebuild functional connections solely from current sensory input and plasticity rules, without any reward signals.\n\n\nThank you again for your valuable feedback, which has significantly enhanced our evaluation. We believe the additional comparisons, computational analysis, and clarifications we've provided substantially address your concerns and demonstrate PDLF's meaningful contributions. We will incorporate these improvements into our revision and respectfully hope you might consider whether these responses warrant a more favorable evaluation. We remain open to further discussion.\n\n---\n\n### References\n[1] 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[2] Finn, Chelsea, Pieter Abbeel, and Sergey Levine. \"Model-agnostic meta-learning for fast adaptation of deep networks.\" International Conference on Machine Learning. PMLR, 2017.\n\n[3] Rakelly, Kate, et al. \"Efficient off-policy meta-reinforcement learning via probabilistic context variables.\" International Conference on Machine Learning. PMLR, 2019."}, {"review_id": "bLFHK9oSW9", "reviewer": "Reviewer_7YQN", "summary": "**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", "questions": "I 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", "limitations": "The 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.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength: 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.", "was_revised": true, "final_justification": "I maintain my score", "rebuttal": "Thank you for your positive assessment and recognition that our paper is well-written with interesting ideas and good experimental results. We appreciate your thoughtful feedback and would like to address your concerns systematically.\n\n### 1. Comparison with Traditional Network Plasticity Methods\n\n\nThank you for highlighting these important related works. The works you mentioned [1-4] represent important algorithmic plasticity approaches that differ fundamentally from our biologically-inspired synaptic plasticity:\n\n- **Dynamic topology [1,2]** achieves plasticity through structural modifications (growing/expanding networks), requiring global coordination and architectural adjustments during training\n- **Adaptive reset [3]** addresses optimization challenges (dormant neurons) through explicit algorithmic interventions \n- **Activation function replacement [4]** analyzes plasticity loss in continual learning and proposes engineering solutions like periodic resets\n\nTo provide direct comparison, we conducted experiments using identical network architectures across all methods, with each approach following its original experimental settings as described in the respective papers. We were unable to include [4] in our comparison as the code and sufficient implementation details for reproduction were not available. We tested on the MuJoCo Ant environment with results averaged over 5 independent runs:\n\n\n| Method           | Growing Networks [1] | Neuroplastic Expansion [2] | Dormant Neuron [3] | PDLF (Ours) |\n|------------------|----------------------|----------------------------|--------------------|-------------|\n| Ant (avg return) | 1736±124             | 5617±203                   | 5423±187           | 5546±156    |\n\nThese results demonstrate that PDLF achieves competitive performance with state-of-the-art plasticity methods, which is notable given that SNNs are inherently more challenging to optimize than traditional ANNs used in the baseline methods.\n\nWhile these algorithmic approaches excel at solving specific optimization challenges in deep RL training, PDLF explores a fundamentally different question: how to achieve autonomous lifelong adaptation through evolved plasticity rules. For instance, while [3] provides algorithmic solutions for dormant neurons, PDLF's activity-dependent plasticity inherently avoids such issues through biological self-regulation mechanisms. The key advantages of our approach include lifelong adaptation without requiring periodic interventions, autonomous recovery from neural damage, and biological plausibility that provides insights into brain function, whereas algorithmic methods are primarily engineering solutions.\n\n### 2. Enhanced Benchmark Evaluation\n\nWe appreciate your suggestion to use more complex benchmarks like DeepMind Control/Meta World. However, our experimental design already presents significant challenges that go beyond traditional benchmarks and many existing meta-learning setups.\n\nOur agents learn only 8 different directions or speeds during training, yet must generalize to 72 completely unseen variations without any reward signals or additional training data. This is fundamentally different from typical meta-learning benchmarks, which often involve only 2-4 task variants and provide task-specific examples during adaptation. What makes our evaluation particularly challenging is the requirement for zero-shot generalization across continuous task spaces while maintaining robustness to neural damage.\n\nWhile the base environments (Ant, Swimmer) may appear simple, the generalization challenge is actually more apparent precisely because of this simplicity. Success cannot be attributed to learning complex environmental dynamics, but rather to the agent's ability to adapt its own learning mechanisms to handle unseen scenarios. Many meta-learning works like MAML or Reptile evaluate on tasks with relatively limited variation, while our agents must navigate a continuous 360-degree space of movement directions and demonstrate autonomous recovery from neural damage.\n\nWe chose these foundational experiments to clearly demonstrate the principles of plasticity-driven learning before extending to more complex environments in future work, where the same plasticity mechanisms can be applied to richer task distributions and more sophisticated control challenges.\n\nThank you for your valuable feedback. We will incorporate all mentioned references and experimental results in our revision. We hope these clarifications address your concerns and look forward to further discussion. We would appreciate if you could reconsider your evaluation in light of these responses."}, {"review_id": "W7EGYTuQsV", "reviewer": "Reviewer_dN6G", "summary": "The 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.", "questions": "N/A", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The supplementary results have addressed most of the concerns I have, and I decide to raise my rating.\nPlease include the necessary revision to the updated paper.", "rebuttal": "Thank you for your thoughtful and detailed review. We appreciate your recognition of our pioneering contribution in extending Hebbian plasticity to SNNs for multi-task RL and working memory tasks. Below we will address each of your concerns systematically.\n\n### 1. Significance of Temporary Synaptic Injury Experiments\n\nWe must clarify a crucial distinction: the temporary injury experiments occur during the **inference/deployment phase**, not during training. During these experiments:\n- The network receives no external reward signals\n- All synaptic weights are catastrophically reset to zero mid-task  \n- The network must recover functionality using only current sensory input and previously evolved plasticity rules\n  \nThis is fundamentally different from starting with zero weights during training. The significance lies in demonstrating that our evolved plasticity enables real-time recovery from catastrophic weight loss without any retraining—a capability that fixed-weight networks completely lack. \n\nBeyond temporary injury, we conducted a comprehensive suite of challenging evaluation paradigms including permanent neural damage (Fig. 4C), zero-shot generalization to unseen task variations (Fig. 5), and multi-task adaptation without reward signals (Fig. 6). In all these scenarios, our PDLF-enabled agents must adapt autonomously using only their evolved plasticity mechanisms, without any external training or reward feedback.\n\nThese evaluation paradigms are significantly more challenging than standard benchmarks, as they require true lifelong adaptation capabilities rather than conventional supervised learning. Despite this increased difficulty, our method demonstrates superior performance and remarkable robustness across all tested conditions, highlighting the fundamental advantages of learning adaptive plasticity rules over fixed network parameters.\n\n\n### 2. Distinction from Prior Plasticity Rules\n\nOur PDLF represents a fundamentally different approach from [1] in several critical aspects:\n\n- **Biologically-grounded dual mechanisms**: SCP captures spike-timing dependent correlations (mimicking STDP), while PDP provides homeostatic regulation (mimicking intrinsic plasticity) - both have distinct neurobiological foundations that are absent in [1]'s generic parametric formulation.\n- **Temporal precision**: Unlike [1]'s instantaneous activations, our spike traces capture precise temporal dependencies crucial for dynamic adaptation. Our experiments (see Response to Reviewer `K8Y9`) demonstrate that this temporal aspect significantly enhances performance.\n- **Emergent stability**: Our dual-mechanism design naturally prevents the instabilities that plague single-rule approaches, as demonstrated by our ablation studies where removing PDP causes divergence. This systematic analysis in complex control tasks goes beyond previous work.\n- **Enhanced biological plausibility**: The SNN framework opens exciting possibilities for incorporating advanced biological mechanisms impossible in ANNs, such as neuronal heterogeneity (different neuron types with specialized plasticity rules), structural plasticity (dynamic synapse formation/elimination), and neuromodulation (context-dependent plasticity regulation). This foundation enables the development of specialized learning circuits for different cognitive functions, promising more sophisticated adaptive behaviors than homogeneous plasticity approaches.\n\n### 3. Comparisons with Evolved Hebbian Plasticity in ANNs\n\nWe conducted additional experiments comparing PDLF with established meta-learning and plasticity-based approaches on our multi-task scenarios. The results demonstrate PDLF's superior performance:\n\n|Method|ant_dir|swimmer_dir|halfCheetah_vel|hopper_vel|\n|---|---|---|---|---|\n|SNAIL[2]|4654|7946|-764|641|\n|MAML[6]|4763|7509|-831|683|\n|PEARL[7]|4706|8814|-663|816|\n|Meta-Hebb[3,4]|4032|7405|-791|768|\n|[5]|3914|8064|-819|724|\n|PDLF(ours)|6904|10531|-549|869|\n\nA critical advantage of our approach is that, unlike meta-learning methods that require task-specific gradient-based adaptation or fine-tuning during deployment, our PDLF agents generalize to new tasks using only their evolved plasticity rules without any additional optimization or parameter updates. This represents true zero-shot adaptation through autonomous synaptic adjustments, making our approach more practically deployable and biologically plausible than conventional meta-learning paradigms.\n\n### 4. Computational Analysis\n\nWe evaluated training efficiency by measuring average episodic reward achieved after different wall-clock training times on a single A100 GPU:\n\n|Task|Method|$500$s|$1000$s|$2000$s|$5000$s|$10^4$s|$2\\times10^4$s|$5\\times10^4$s|$10^5$s|\n|---|---|---|---|---|---|---|---|---|---|\n|fetch|$Opt_{Weight}$|6.75|10.69|13.04|14.75|15.37|16.28|17.12|17.7|\n|fetch|$Opt_{Plasticity}$|4.54|5.29|5.93|9.15|25.03|46.04|56.71|63.02|\n|ur5e|$Opt_{Weight}$|9.37|12.38|12.41|12.47|12.54|12.52|13.48|14.42|\n|ur5e|$Opt_{Plasticity}$|0.27|0.38|0.79|12.83|45.58|67.45|86.31|99.27| \n\nWhile PDLF converges more slowly initially, the significant performance gains justify the training investment. For inference, our approach offers three key advantages:\n1. Spike-based computation is inherently more efficient than continuous activations\n2. **Once trained, the same plasticity rules can be deployed across diverse tasks without retraining** - unlike task-specific models that require separate training for each domain\n3. For energy-constrained applications, weight updates can be disabled after stabilization (trading adaptability for efficiency)\n\n\n### 5. Deeper Analysis of Permanent Injury Mechanisms\n\nTo quantify compensation mechanisms, we analyzed weight changes in undamaged synapses during permanent injury:\n\n|Task|DamageLevel| $\\|\\Delta w\\| / \\|\\overline{w}\\|$ |\n|---|---|---|\n|ant_dir|10%|0.072|\n||25%|0.124|\n||50%|0.186|\n|fetch|10%|0.067|\n||25%|0.118|\n||50%|0.163|\n|fetch|10%|0.064|\n||25%|0.124|\n||50%|0.159|\n\nThese results confirm that remaining synapses actively compensate for damaged connections, with compensation magnitude scaling with injury severity—paralleling biological neural reorganization.\n\nOur visualizations (Fig. 4B and 5B) also show different weight activation patterns before/after temporary damage and task-specific clustering. This also suggests the existence of compensatory mechanisms.\n\n\n### 6. Parameter Choices\n\nOur neuronal parameters follow standard practices in SNN literature with biological justification:\n- **Δt**: Aligned with environment interaction frequency (20ms for cognitive tasks, 200ms for control)\n- **τm = 2×Δt**: Standard ratio ensuring proper membrane integration over 2-3 timesteps\n- **λ**: Designed for exponential decay to 1/e over biologically relevant timescales\n- **Threshold/reset voltages**: Standard SNN values ensuring stable dynamics\n\nWe chose not to optimize τm and λ to maintain biological plausibility, and our ablation studies confirm minimal performance impact (as can be seen in the table below). The threshold/reset voltages serve similar roles to the bias term D in Eq. 1. ablation analysis on ant_dir confirms our results are stable across parameter variations:\n\n| attr.| | | | | |\n|---|---|---|---|---|---|\n|τm|100ms|200ms|400ms|600ms|800ms|\n||6584|6867|6904|6874|6823|\n|λ|136ms|272ms|544ms|1088ms|2176ms|\n||6745|6813|6904|6932|6894|\n|$v_{th}$|0.01V|0.05V|0.1V|0.2V|0.4V|\n||6813|6902|6904|6714|6438|\n\nThank you for your detailed feedback, which has significantly improved our work's clarity and completeness. We will incorporate all mentioned references and additional analyses in our revision. We believe these comprehensive responses address your concerns and help clarify the novelty and significance of our contributions. We would be grateful for any further discussion and hope you might consider these clarifications when finalizing your evaluation.\n\n---\n\n### References\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\n[6] Finn, Chelsea, Pieter Abbeel, and Sergey Levine. \"Model-agnostic meta-learning for fast adaptation of deep networks.\" International conference on machine learning. PMLR, 2017.\n\n[7] Rakelly, Kate, et al. \"Efficient off-policy meta-reinforcement learning via probabilistic context variables.\" International conference on machine learning. PMLR, 2019."}]

Author rebuttal:

Meta-review: All reviewers recommended acceptance - barely. The proposed method seems novel and to produce results. There was some concern about the simplicity of the experiments.

More importantly, there was concern about the lack of citations from existing literature. I would add a strong recommendation to cite the relevant work of the Vogels lab, which also "evolves" plasticity rules for spiking networks - e.g. Confavreux et al., Neurips 2023.

Provided that the recommended additions and clarifications are included in the final paper, I would incline towards acceptance.
Decision: Accept (poster)