Efficient Learning in Neural Networks without Gradient Backpropagation
Brain-inspired;Learning algorithm;Learning efficiency
Yes
No ethics review needed.
How did you control the learning rate when estimating learning efficiency? Additionally, algorithmic complexity may be less relevant in inherently parallel systems, such as the brain, neuromorphic chips, and, to a lesser extent, GPUs. How does this affect your conclusion?

Wouldn’t it be more effective to consider low-rank perturbations of the postsynaptic units rather than projecting the presynaptic units into a low-rank space? What is the motivation or benefit of projecting $x_{l-1} $ to a low-rank space instead of adding perturbations in a low-rank space?
The numerical experiments are thoroughly and clearly explained.
Moreover, the results convincingly demonstrate the benefits of the proposed algorithm over conventional NP for deep neural networks with more than five hidden layers.
In this manuscript, the authors propose a novel biologically plausible learning algorithm called LOCO. This algorithm is based on the node perturbation (NP) method but incorporates an update projected onto a low-rank activity subspace. The authors explore the theoretical properties of LOCO and demonstrate numerically that it outperforms vanilla NP and several previously proposed algorithms when applied to multi-layered spiking neural networks on the MNIST and NETtalk datasets.
There are several mathematical inaccuracies in the presented results that affect the soundness of the conclusions:

- The abstract states that "LOCO provides an unbiased estimate of the BP gradient," which appears to be incorrect. In the limit of infinite perturbations, the LOCO update becomes $\Delta W^{LOCO}_l = \Delta W^{NP}_l P_l^T = \Delta W^{BP}_l P_l^T$. Thus, unless the projection matrix $P_l$ is full-rank, LOCO introduces a bias relative to the BP gradient.

- In Line 894, $\ell$ is referred to as the Hessian matrix. However, according to Malladi et al. (2024), $\ell$ represents the smoothness parameter (the Lipschitz constant) of the loss function. 

- Eq. 17: trace is missing from the Frobenius inner product.

- Eq. 18: While a matrix P satisfying this equation exists, P in this context is no longer a projection matrix. 

- A.3: Inequalities are missing from the equations throughout the proof, making the arguments difficult to follow.

- L965-966: The origin of $tr [\sum(\theta_t)]$ is unclear, and the equation appears to be incorrect. 

- L1221: Although $g$ follows a Gaussian distribution if the perturbation is Gaussian, the covariance is not necessarily the identity matrix.

- A 15: There seems to be confusion between subspaces in the activity space and the weight space. Even if the activity of pre-units resides in an r-dimensional space, the gradient g may not be constrained to an r-dimensional space.

- L1239-1241: Is it assumed that $\sigma^2 = || \mu_g ||^2$? If so, why should that be the case?

While the learning algorithm is motivated by biological plausibility, the method for obtaining the projection matrix $P_l$ lacks biological plausibility. 

It is well known that orthogonalization prevent catastrophic forgetting (French, CogSci 1991; Farajtabar et al., AISTATS 2020), hence the presented result on continual learning is not surprising.
Yes
No ethics review needed.
Couple of typos: 
- 102 and 156, typo weights 
- 160 to obtained, 
- 319 we varies
- 399 typo performance
- Interesting algorithm that has connections to neuroscientific data and the orthogonal activity subspaces found in e.g. motor control. 
- Testing the algorithm’s performance across both SNNs and ANNs is a strength. 
- Algorithm certainly improves over node perturbation.
This paper introduces a new learning algorithm, low-rank cluster orthogonal (LOCO) weight modification. LOCO improves on node perturbation by projecting the layer inputs into task-dependent orthogonal low dimensional space, effectively reducing the parameter space searched by node perturbation. LOCO’s performance is tested on MNIST and Netalk datasets, including a catastrophic forgetting task, for both spiking neural networks and artificial neural networks and found to be better than vanilla node perturbation.
- Abstract, statement about traditional brain-inspired algorithms. Convergence guarantees to what? Also this is an incorrect statement, node perturbation itself has guarantees and e.g. see https://arxiv.org/pdf/2110.10815 for FA. 
- The proposed approach’s efficiency  is not tested (e.g. flops) and does not seem to be efficient as the projection matrix must be calculated at every update. 
- Similar to efficiency aspects, the proposed approach trades off one aspect of biological plausibility for another (the cluster orthogonal projection algorithm on the inputs).
- The method seems to rely on a simple task structure. Namely that the inputs can be projected to low dimensions (without losing too much task relevant information) and orthogonalised using kmeans clustering (relying on data prototypes). Neither of these seems valid assumptions for complicated tasks, and the submission does not test on appropriately hard tasks such as those in bartunov 2018 (Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures for ANNs.)
- Figures are small and difficult to read at standard zoom. In particular Fig 1. 
- Comparisons to BP are not always made
Yes
No ethics review needed.
•	Can you demonstrate how the projection matrix P could be computed using known biological signals?

•	Does your learning rule perform effectively only when the task can be learned via low-dimensional orthogonal manifolds? How does the rank of the task influence performance, and does the task rank need to be known in advance?

•	Could you discuss how your method relate to Duncker and Driscoll et al. (NeurIPS 2020)?
•	The pursuit of biologically plausible alternatives to backpropagation is a significant and intriguing problem for computational neuroscientists, though it holds less interest for the ML community, where backpropagation is already effective and versatile.

•	The authors support their findings by combining empirical results on well-known benchmarks with theoretical analysis, which strengthens the validity of their approach (although the theoretical assumptions would benefit from clearer presentation within formal statements).
This paper addresses the well-known challenges of implementing backpropagation in neural systems and proposes an alternative learning rule, LOCO, to train neural networks. LOCO extends node perturbation (NP) by introducing two additional constraints—low-rank and orthogonality in the weight modification space—which significantly improve learning efficiency. Through empirical results on tasks like XOR, MNIST, and phonetic transcription, alongside theoretical analysis, the authors demonstrate that LOCO outperforms traditional NP and others.
•	If biological plausibility is the main motivator for this work, the learning rule should be computable using known biological processes. However, the projection matrix P lacks clear justification in terms of how it could be implemented biologically.

•	A significant concern of this submission is the absence of a discussion on the limitations.

•	A closely related work by Duncker and Driscoll et al. (NeurIPS 2020), which also uses a projection matrix P to promote orthogonal subspace learning (minimizing interference in continual learning), was not cited.

•	The presentation is mostly clear; however, the title is overly general and does not clearly distinguish this work from the extensive literature on learning without backpropagation.

•	The theoretical analyses are not framed within formal Theorem statements, making it unclear what specific conditions or assumptions (e.g., convexity) apply.

•	Minor typos: mixing up of \citep vs \citet in the second-to-last paragraph of the introduction, as well as "wights" on line 102 and "perofmrance" on line 399.
Yes
No ethics review needed.
see weaknesses
The theoretical aspects of this paper are clearly articulated, which is one of its strengths.
This paper presents an improved training algorithm based on node perturbation. The main idea is to project the gradients to be altered into a low-dimensional space that is orthogonal to the direction of the original gradient changes, thereby accelerating the convergence speed of node perturbation.
However, I have two main concerns:

1. **Biological Plausibility of Node Perturbation Compared to Backpropagation**: In the work of Lillicrap et al., it is suggested that backpropagation can actually be implemented using a local Hebbian-like rule. The conclusion regarding its biological implausibility arises from three main points:
   - a. Backpropagation demands synaptic symmetry in the forward and backward paths.
   - b. Error signals are signed and potentially extreme-valued.
   - c. Feedback in the brain alters neural activity.
   
   It would be beneficial to clarify how node perturbation or the proposed LOCO method addresses these three issues to demonstrate its greater biological plausibility.

2. **Comparison with Other Learning Algorithms**: My second concern stems from the lack of comparisons with other learning algorithms, which challenges the novelty of this work. The related work section is insufficient; there exists a substantial class of non-backpropagation algorithms, such as:
   - Neural Sampling in Hierarchical Exponential-family Energy-based Models
   - Predictive Coding in the Visual Cortex: A Functional Interpretation of Some Extra-Classical Receptive-Field Effects
   
   Additionally, the concept of orthogonal projection has already been explored in related works, such as:
   - Hebbian Learning Based Orthogonal Projection for Continual Learning of Spiking Neural Networks.
   
   Furthermore, node perturbation has been extensively studied in the context of artificial neural networks, including models like the forward-forward model, 
   - Scaling forward gradient with local losses
   - Gradients without backpropagation. 
I hope the authors can include comparisons with these learning algorithms, as both experimental and theoretical evaluations are lacking in this paper. This may obscure the novelty of the proposed work.
An efficient non-BP algorithm with mathematical convergence guarantees , achieving state-of-the-art performance in deep spiking neural networks while overcoming catastrophic forgetting.
@inproceedings{
anonymous2024efficient,
title={Efficient Learning in Neural Networks without Gradient Backpropagation},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=R0YGjmqiwB},
note={under review}
}
The brain possesses highly efficient learning algorithms that have not been fully understood. The gradient backpropagation (BP) serves as a powerful tool for training artificial neural networks, but it diverges from the known anatomical and physiological constraints of the brain. Conversely, biologically plausible learning algorithms have efficiency limitations in training deep neural networks. To bridge this gap, we introduce a perturbation-based approach called low-rank cluster orthogonal (LOCO) weight modification. Unlike traditional brain-inspired algorithms, LOCO offers mathematical convergence guarantees. Theoretical analysis shows that LOCO provides an unbiased estimate of the BP gradient, with low variance. As a result, LOCO is highly efficient. It can train the deepest spiking neural networks to date without gradient backpropagation, achieving state-of-the-art performance on several benchmark datasets and exhibiting the ability to overcome catastrophic forgetting. These findings suggest that biologically feasible learning methods can be substantially more efficient than previously believed. Furthermore, avoiding gradient backpropagation allows LOCO to achieve O(1) time complexity for weight updates. This opens a promising avenue for developing distributed computing systems that are more efficient than BP-based counterparts.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Brain-inspired Learning algorithm Learning efficiency
I certify that there is no acknowledgement section in this submission for double blind review.
/pdf/0f652b4a71d4e46407d69750466a5222ca6b53b7.pdf
learning theory
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
/attachment/ceb678959f6e98c0a8c3858c6dbf2118c0e5e7ab.pdf
Efficient Learning in Neural Networks without Gradient Backpropagation
ICLR 2025 Conference Submission
ICLR.cc/2025/Conference/Submission