Title: Meta-learning local learning rules for structured credit assignment with sparse feedback
Venue: NeurIPS 2025
Accepted: no
Keywords: Biologically Plausible Deep Networks

Plasticity and Adaptation

Recurrent Networks

Reinforcement Learning (Cognitive/Neuroscience)
Abstract: Biological neural networks can learn complex behaviors from sparse, delayed feedback using local synaptic plasticity, yet the mechanisms enabling structured credit assignment remain elusive. In contrast, artificial recurrent networks solving similar tasks typically rely on biologically implausible global learning rules or hand-crafted local updates. The space of local plasticity rules capable of supporting learning from delayed reinforcement remains largely unexplored. Here, we present a meta-learning framework that discovers local learning rules for structured credit assignment in recurrent networks trained with sparse feedback. Our approach interleaves local neo-Hebbian-like updates during task execution with an outer loop that optimizes plasticity parameters via **backpropagation through learning**. The resulting three-factor learning rules enable long-timescale credit assignment using only local information and delayed rewards, offering new insights into biologically grounded mechanisms for learning in recurrent circuits.

Abstract

Biological neural networks learn complex behaviors from sparse, delayed feedback using local synaptic plasticity, yet the mechanisms enabling structured credit assignment remain elusive. In contrast, artificial recurrent networks solving similar tasks typically rely on biologically implausible global learning rules or handcrafted local updates. The space of local plasticity rules capable of supporting learning from delayed reinforcement remains largely unexplored. Here, we present a meta-learning framework that discovers local learning rules for structured credit assignment in recurrent networks trained with sparse feedback. Our approach interleaves local neo-Hebbian-like updates during task execution with an outer loop that optimizes plasticity parameters via backpropagation through learning (BPTL) . The resulting three-factor learning rules enable long-timescale credit assignment using only local information and delayed rewards, offering new insights into biologically grounded mechanisms for learning in recurrent circuits.

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1 Introduction

Learning in biological organisms involves changes in synaptic connections (synaptic plasticity) between neurons [1, 2]. Synaptic changes are believed to underlie memory formation and are essential for adaptive behaviour [3]. Experimental evidence suggests that synaptic changes depend on the coactivation of pre- and postsynaptic activity [4, 5], and possibly other local variables available at the synaptic site [6, 7]. These unsupervised synaptic modifications have explained activity-dependent circuit refinement during development such as the emergence of functional properties like receptive field formation based on naturalistic input statistics [8].

Yet, most organisms routinely solve complex tasks that require feedback through explicit supervisory or reinforcement signals. These signals are believed to gate or modulate plasticity, acting in the form of a third factor that scales and also probably imposes the direction of the synaptic modifications [9]. How error- or reward-related information is propagated through the recurrent interactions is not yet clear. While prior work has largely focused on hand-crafted synaptic updates for unsupervised self-organization, or biologically plausible approximations of backpropagation [10], the space of plasticity rules capable of supporting structured credit assignment from delayed feedback remains vastly underexplored.

Backpropagation through time (BPTT), the standard approach for training recurrent neural networks 31 (RNNs), is biologically implausible since it requires symmetric forward and backward connections 32 and non-local information [11, 12]. Although recent work has reformulated BPTT into more biolog33 ically plausible variants using random feedback [13], truncated approximations [14], or by learning 34

feedback pathways [15], these methods require continuous error signals to refine recurrent connec35 tions. 36

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Here, we adopt a bottom-up approach: instead of imposing hand-designed synaptic rules, we discover biologically plausible plasticity rules that support learning through delayed reinforcement signals via meta-optimisation. Building on recent work [16], we parameterise plasticity rules as functions of local signals (presynaptic activity, postsynaptic activity, and synapse size) and metalearn their parameters within a second reinforcement learning loop. With that, our present work tackles the following questions:

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Which local learning rules can implement structured credit assignment under biological constraints?

Do different forms of plasticity give rise to different computational regimes and representations as observed with gradient based training (e.g., 'lazy' vs. 'rich' learning)?

Recent theory distinguishes between lazy and rich regimes of learning in RNNs: in the lazy regime, representations remain fixed while output weights adapt; in the rich regime, the network reorganises its internal dynamics to encode task structure. While these regimes are well-characterised for gradient-trained networks, it remains unclear whether biologically plausible learning rules can support either or both, and what synaptic mechanisms underlie each regime. Here we demonstrate that different forms of plasticity naturally lead to qualitatively different learning trajectories and internal representations, akin to their gradient-based learning rules.

2 Method

Network dynamics. Weconsider recurrent neural networks (RNNs) of firing rate neurons coupled through a synaptic matrix W ∈ R N × N [17], with additional input and output matrices W in ∈ R N in × N and W out ∈ R N × N out that route task-relevant input into the recurrent circuit and read out network activation to generate task-specific outputs (actions). The equations governing the network dynamics are

where x t ∈ R N is the vector of pre-activations (or input currents) to each neuron in the network, ϕ ( · ) : R N → R N denotes the single-neuron transfer functions, r t ∈ R N + is the vector of instantaneous firing rates, u t stands for the activity of the N in input neurons. In the terms above, the · t superscript indicates time dependence. Network outputs z t are obtained from linear read-out neurons as

Sparse feedback and parametrized learning rules. We consider networks that learn contextdependent cognitive tasks using biologically plausible local learning rules, guided by sparse reinforcement signals R provided only at the end of each training episode. To enable learning from such delayed and global signals, each synapse between a pre-synaptic unit j and a post-synaptic unit i maintains an eligibility trace e ij [18], which integrates the history of (co-)activation during the episode. We define the evolution of eligibility traces with differential equations of the form

where τ e is a decay time-scale, ¯ x i is a running average of the pre-activation of neuron i , and θ k,l ∈ R 71 are learnable coefficients. In contrast to eligibility traces based solely on pairwise correlations [19], 72 we use here a polynomial expression that captures richer interactions between pre- and post-synaptic 73 activity. Each coefficient θ k,l can be construed as a term-specific learning rate, which may be posi74 tive (Hebbian), negative (anti-Hebbian). This parameterization allows individual terms to modulate 75 synaptic eligibility based on pre-synaptic activity, post-synaptic activity, co-activity, or deviations 76 from a homeostatic set point. In our experiments, we set d = 2 , yielding 9 monomial terms that 77 capture nonlinearities and interaction effects, while remaining computationally tractable. 78

The recurrent weight matrix W gets updated at the end of each training episode according to a 79 reward-modulated learning rule 80

where τ w denotes the time scale of weight decay, e ij stands for the eligibility trace accumulated 81 during the episode, while R , ¯ R stand for the obtained and the expected reward. Here, we model 82 reward expectations for each type of trial independently as a running average of past rewards for 83 this trial type [20]. This update rule enables credit assignment through the interaction between 84 synaptic eligibility and trial-specific reward prediction error, consistent with neo-Hebbian three85 factor learning rules hypothesized to operate in biological circuits [19]. In principle the weight 86 updates happen due to (slow) weight decay or due to reward prediction errors. 87

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Meta-learning plasticity rules. While previous work has relied on hand-crafted eligibility trace dynamics and synaptic update rules to train recurrent neural networks with sparse feedback [20], we instead adopt a meta-learning approach to learn the parameters of the plasticity rules. Our framework consists of two nested training loops: (i) an inner loop in which the recurrent network is trained over several episodes using local learning rules and sparse reinforcement signals provided at the end of each episode, as described above; and (ii) an outer loop that optimizes the plasticity meta-parameters Θ = {{ θ k,l } 2 k,l =0 , τ w , τ e } via gradient descent using backpropagation through learning on a meta-loss computed over K training episodes (trials). This approach allows the learning rules themselves to be adapted to the task, rather than be fixed a priori.

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Backpropagation through learning. Our goal is to optimise the learning rule parameters θ to maximise task performance, measured as the expected cumulative reward ⟨ R ⟩ obtained after a fixed number of learning episodes. However, the reward R obtained by the agent depends on the network's output, which in turn is determined by its synaptic weights W = { W in , W, W out } . The weights are dynamically updated according to the employed synaptic update rule (Eq. 5). This plasticity rule, depends on the eligibility traces e ij , which themselves are parameterised by θ . This establishes a complex dependency chain over the network parameters: R ← W ← e ← θ . Thus directly computing the gradient ∇ θ ⟨ R ⟩ by backpropagating through the entire network dynamics over learning is computationally challenging.

To address this, we employ a REINFORCE-inspired approximation [21] to estimate the gradient ∇ θ ⟨ R ⟩ . Recall that the REINFORCE gradient formula involves computing the gradient of an expected value by observing outcomes and scaling a measure of what elicited that outcome with the associated reward. Or more formally, scaling the gradient of the log-probability of an outcome with the reward associated with that outcome

Here, since we consider deterministic weight updates, we do not have a stochastic policy π , as is common in policy gradient methods in reinforcement learning. However, we can consider the final weight configuration W (Θ) as an implicit policy with parameters Θ , that determine the learned network behaviour. We then use the reward prediction error , defined as δR = R -¯ R (where ¯ R is a running average of the reward), as a signal to adapt the parameters θ

Since the weight updates depend linearly on the eligibility trace (Eq. 5), we have

To relate this to the gradient of the reward with respect to θ , we sum over all synapses, resulting in 117 the approximation 118

The eligibility trace e ij is a function of neural activity, and its dependency on the parameters θ is 119 explicitly defined by the model (Eq. 4). For the eligibility trace parametrised in the polynomial 120

form of Eq. 4, the term de ij dθ has an explicit expression in terms of neural activations and firing rates 121 (Eq. 9). This expression is fully analytic and requires no gradient propagation through the network 122 or the learning episodes. The plasticity parameters θ are then updated using gradient ascent based 123 on this estimated gradient. 124

To enforce sparsity on the identified rules in order to minimise the number of active terms in the 125 identified rule to render it interpretable. 126

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3 Results

We defer the reader to the Extensive results section in the Supplementary Information for the results of the numerical experiments.

4 Related work

Decades of research on synaptic plasticity have focused on hand-crafted learning rules designed to replicate experimentally observed changes in post-synaptic potentials from single-neuron recordings. However, the recent explosion in large-scale functional recordings, particularly longitudinal data collected across learning, has sparked growing interest in identifying the types of plasticity rules that may underlie observed changes in neural activity and behavioural performance. Despite this interest, the task remains extremely challenging: current experimental techniques do not allow direct measurement of synaptic interactions across large neural populations, making it difficult to infer the underlying synaptic mechanisms at play. Thus an increasing number of frameworks have emerged that aim to discover plasticity rules from indirect signatures such as changes in neural activity distributions, recorded trajectories, or behavioural performance. These approaches differ in what kind of observations they use, and in the assumptions they make about the network structure, plasticity rule parameterisation, and underlying task.

Matching rate distributions. One line of work focuses on inferring synaptic plasticity rules from pre- and post-learning firing rate distributions. Lim et al.[22] jointly infer neuron transfer functions and synaptic updates from observed rate distributions, under assumptions of Poisson firing statistics and linearized plasticity. This approach was later extended using Gaussian process priors over plasticity functions[23], improving flexibility but still restricted to feedforward networks and ignoring temporal dynamics.

These approaches do not model the full trajectory of activity during learning, instead identify plasticity rules that explain cumulative changes across learning. As a result, they cannot constrain rule parameters based on how learning unfolded in time.

Inference by conditioning on neural trajectories. Asecond group of methods exploits neural activity trajectories recorded over learning. Ramesh et al. [24] use a generative adversarial framework to infer plasticity rules that generate neural trajectories similar to empirical ones. While highly expressive, this method requires extensive data and computational resources, and suffers from known instability issues in GAN training. Confavreux et al. [16] proposed a meta-learning framework to discover plasticity rules that produce desired temporal coding properties in rate-based networks. While insightful, their approach optimises for a fixed synthetic objective (e.g., encoding elapsed time), rather than learning from observed data or behaviour.

Behavior-based plasticity inference. A third set of studies use behavioural performance trajectories to constrain synaptic plasticity. Ashwood et al.[25] fit learning rule parameters in rodent decision tasks using a Bayesian model, requiring approximation of the full posterior over synaptic weights. Rajagopalan et al.[26] reformulate the plasticity inference problem as logistic regression by assuming presynaptic activity and reward as the only inputs. These frameworks remain limited in flexibility, often neglecting dependencies on postsynaptic activity or synapse strength, which are essential for biologically grounded learning.

Most of these approaches assume feed-forward structure of the underlying network [23, 27], and 167 consider plasticity evolving network dynamics in an unsupervised setting. Only the recent work of 168

[27] considers a reward term in the plasticity rule, that effectively puts the learning framework under 169 a reinforcement learning and thus closer to how biological organisms learn. 170

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5 Limitations

Despite its strengths, our work has several limitations that point to opportunities for future improve172 ment and extension. One limitation is that the proposed meta-learning procedure must be run mul173 tiple times independently to discover multiple plasticity rules that satisfy the same task constraints. 174 Recent advances using simulation-based inference [16] provide a promising alternative for sampling 175 entire distributions over plasticity rules that solve a given cognitive task, potentially offering a more 176 efficient and principled exploration of solution space. Yet, simulation based inference is easy to 177 incorporate in our setting. 178

Another limitation is that our current framework is purely exploratory and does not explicitly in179 corporate constraints from experimentally recorded neural activity. While this allows for a broad 180 and flexible search over possible learning mechanisms, it limits the biological specificity of the dis181 covered rules. Extending our framework to incorporate such constraints, for instance, by biasing 182 the meta-optimisation toward activity trajectories consistent with recorded data, could yield more 183 realistic models of synaptic updates. 184

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Justification: We now include in the supplement a section called Broader Impact. However, briefly we do not see any direct negative societal impact.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

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If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA] .

Justification: [NA] .

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

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Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: The paper is strongly influenced by the paper [20], and the paper is cited, however the code used in the experiments was developed from scratch in Python after consulting the original C++ code released with the aforementioned paper.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

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13. New assets

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Answer: [Yes]

Justification: Again this is included in 'Details on Numerical experiments'

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Answer: [NA]

Justification: [NA]

Guidelines:

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Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

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Justification: [NA]

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Peer reviews:
comments: SUMMARY:
This is an incomplete submission missing experiments verifying the main claims of the paper, and therefore cannot be properly reviewed. The authors mention "We defer the reader to the Extensive results section in the Supplementary Information for the results of the numerical experiments", however the supplementary materials does not contain such result section.

STRENGTHS AND WEAKNESSES:
This is an incomplete submission missing experiments verifying the main claims of the paper, and therefore cannot be properly reviewed. The authors mention "We defer the reader to the Extensive results section in the Supplementary Information for the results of the numerical experiments", however the supplementary materials does not contain such result section.

QUESTIONS:
This is an incomplete submission missing experiments verifying the main claims of the paper, and therefore cannot be properly reviewed. The authors mention "We defer the reader to the Extensive results section in the Supplementary Information for the results of the numerical experiments", however the supplementary materials does not contain such result section.

LIMITATIONS:
/
TITLE: Official Review
CLARITY: 1
IMPACT: 1
ORIGINALITY: 1
DATE: 2025-06-21

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a meta-learning approach (i.e. an outer training loop) to learn local rules controlling the weights between the neurons of a RNN. The paper discuss how it aims at looking at how internal representations are affected by different local learning rules.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n- The paper poses an interesting problem: learning local synaptic rules in ANN and investigate how different rules result in different internal representations in the RNN.\n    \n\n## Weakness\n\n- No results of using the proposed algorithm to solve any task seem to have been included—neither in the main text nor in the appendix. Section 3 reads ““We defer the reader to the Extensive results section” ([pdf](zotero://open-pdf/library/items/LDSDZWNH?page=4)) but there isn’t any results section in the appendix.\n    \n- The method is very similar to existing meta-learning algorithms to learn local rules, e.g.,:\n    \n    - Rosenfeld B, Rajendran B, Simeone O. Fast on-device adaptation for spiking neural networks via online-within-online meta-learning. In2021 IEEE Data Science and Learning Workshop (DSLW) 2021 Jun 5 (pp. 1-6). IEEE.\n        \n    - Najarro E, Risi S. Meta-learning through hebbian plasticity in random networks. Advances in Neural Information Processing Systems. 2020;33:20719-31.\n        \n    - Stewart KM, Neftci EO. Meta-learning spiking neural networks with surrogate gradient descent. Neuromorphic Computing and Engineering. 2022 Sep 30;2(4):044002.\n        \n\n- Paper states: “While previous work has relied on hand-crafted eligibility trace  dynamics and synaptic update rules to train recurrent neural networks with sparse feedback, we instead adopt a meta-learning approach to learn the parameters of the plasticity rules.” Examples of previous work that do the also meta-learn local rules:\n    \n    - Pedersen JW, Risi S. Evolving and merging hebbian learning rules: increasing generalization by decreasing the number of rules. In Proceedings of the Genetic and Evolutionary Computation Conference 2021 Jun 26 (pp. 892-900).\n        \n- The paper present the learning approach as contrasting to ANN which “rely on biologically implausible global learning rules”; and “using only local information”. However, the proposed method  keeps a history of the trace and co-activations between the neurons. It’s not clear how the trace and the co-activation history are stored in differential equation (4) such that information remains purely local.\n    \n- Offloading the whole results sections to the appendix seems like bending the spirit of having an appendix a bit too much as it provides an unfair space advantage to the authors wrt to other submissions. According to the guidelines, appendix is meant to support the results presented in the paper.\n    \n- Some relevant work not discussed:\n    \n    - Tyulmankov D, Yang GR, Abbott LF. Meta-learning synaptic plasticity and memory addressing for continual familiarity detection. Neuron. 2022 Feb 2;110(3):544-57.\n        \n    - See references in here for more missing citations: Schmidgall S, Ziaei R, Achterberg J, Kirsch L, Hajiseyedrazi S, Eshraghian J. Brain-inspired learning in artificial neural networks: a review. APL Machine Learning. 2024 Jun 1;2(2).\n\nQUESTIONS:\n- Results are missing, both regarding the performance of the algorithm solving tasks and how the internal representations are affected by different rules.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors want to learn parameters of three-factor rules with eligibility traces using backpropagation. They parameterize the eligibility traces with a (polynomial of postsynaptic firing rates) times (presynaptic rate minus average) and a decay tau_e . This is a valid way to write an eligibility trace and overall the approach has a valid aim. Unfortunately, the result section is absent in the main text. The main text refers to supplementary.  The supplement file that I could download starts at page 16 and extends the theory, but also does not contain any results.\n\nSTRENGTHS AND WEAKNESSES:\n- strengths\n\nThe aim is commendable.\n\n- weaknesses\n\n1. The theory sketch has many inconsistencies across equations in the main text and also in supplementary.\n2. No numerical results. The main text does not contain any results. Nor does supplementary\n\nOverall I had the impression that the authors planned to have results, but did not have time to generate them in time for submission of the main text. But the rules of NeurIPS are that all main results are summarized in the main text.\n\nQUESTIONS:\ncomments:\n1. tau_e is the time scale of the eligibility trace. It  should be around 1 second according to experimental data . In Eq. (24) the decay is replaced by a rectangular window corresponding to tau_e to infinity. This replacement is ad hoc and not mentioned in the main text.\nNote that with REINFORCE (as opposed to TD learning) it is close to impossible to learn tasks with rewards that are delayed longer than 2 tau_e (whereas it is simple to learn with TD!). \n\n2.  Eq. (6-9) in main text: I find that the detour to log-policy  in Eq. (6) is not helpful (it is not even a useful analogy).\nMoreover Eq (7) does not lead to Eq. (8).   Eq. (8) is correct, but combined with Eq. (7) it does not yield Eq. 9).  Equation (19) has a square and  is different from Eq. (9).  Probably Eq. (9) has a mistake, because Figure 1 in Supplementary and Eq. (24) give a different equation.\n\n3. Could the authors start with a Loss (R - bar{R})^2. ? Equation (7) is then found using chain rule if the term dR/dW in the chain rule is set to unity. \n\n4. The authors write: ``For the eligibility trace parametrised in the polynomial form of Eq. 4, the term d eij/d theta has an explicit expression in terms of neural activations and firing rates''.\nYes, this statement is correct.  This is indeed what one can use for optimization,  but this insight  does not give equation (9) or (19). Rather it seems that the authors use this insight as a starting point for a heuristic approximation scheme.\n\nLIMITATIONS:\nthere are many more limitations since the theory is wrong/incomplete/inconsistent.", "TITLE": "Official Review", "RECOMMENDATION": 1, "REVIEWER_CONFIDENCE": 5, "CLARITY": 1, "IMPACT": 1, "ORIGINALITY": 1, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce an approach to meta-learning synaptic update rules that can perform credit assignment via a modification to REINFORCE. The central idea is to alter the REINFORCE objective to think of the weights as implementing an effective policy. The aims introduced are to study credit assignment under this biologically realistic framework for learning rules and to determine if such rules can give rise to rich and lazy learning regimes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nIn my estimation, both aims introduced were very compelling. Using a REINFORCE-style loss to find plasticity suited to learn arbitrary reward functions is a nice concept and has potential. Additionally, the survey of the literature is quite thorough.\n\nWeaknesses:\n\nMy primary concern is that there are no results. I was quite looking forward to reading that section! The referenced Extended Results section of the supplement does not appear to exist.\n\nAlso, I believe Eq. 8 is wrong. The weight updates depend linearly on the eligibility trace, as in Eq. 5, but the weights themselves should be an exponential filtration of $e_{ij} (R - \\bar{R})$. One can then take the derivative with respect to $\\theta$, yielding\n\n$$ \\frac{dW_{ij}}{d\\theta_{kl}} = \\int_0^t \\delta R(t’) \\frac{de_{ij}(t')}{d\\theta_{kl}} dt’. $$\n\nI believe the description given of Confavreux et al. is incorrect and actually applies to Bell et al., 2024.\n\nAlso, perhaps this approach would be improved by also considering codependent plasticity as in Agnes and Vogels, 2024. Another interesting addition might include considering multiple cell types as in Confavreux et al., 2023.\n\nQUESTIONS:\n1. Is there a results section I somehow missed?\n\nI will refrain from asking further questions as without a results section I cannot properly evaluate the work.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 1, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes a meta-learning algorithm that parameterizes biologically plausible plasticity rules as functions of presynaptic and postsynaptic activity and synapse size, and updates these plasticity parameters in the meta-learning outer loop. It is unclear what specific problem the paper aims to address due to the lack of experiments.

STRENGTHS AND WEAKNESSES:
Weaknesses
- The paper is incomplete.
- There are references to experiments in the main text, yet there are no experiments, also not in the supplementary material
- I am unsure whether authors who do not include their code in the supplementary material can answer 'Yes' in the NeurIPS checklist.

QUESTIONS:
- Where is the results section in the supplementary material? There is a "details on numerical implementation section" in (Appendix E), and a reference to the results section in the main paper but there is no results section.

LIMITATIONS:
Limitations are discussed about the method in general, but as no empirical evidence is given, no limitation thereof is discussed.
TITLE: Official Review
CLARITY: 1
IMPACT: 1
ORIGINALITY: 1
DATE: 2025-07-02