Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this “weight transport problem” (Grossberg, 1987), two biologically-plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry (SS) algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs. We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018) and establish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures.
 In the submitted manuscript, the authors compare the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures, with the aim of testing biologically-plausible learning algorithms alternative to the more artificial backpropagation. The obtained results are promising and quite different to those in (Bartunov , 2018) and lead to the conclusion that biologically plausible learning algorithms in general and sign- symmetry in particular are effective alternatives for ANN training. Although all the included ideas are not fully novel, the manuscript shows a relevant originality, paving the way for what can be a major breakthrough in deep learning theory and practice in the next few years. The paper is well written and organised, with the tackled problem well framed into the context. The suite of experiments is broad and diverse and overall convincing, even if the performances are not striking. Very interesting the biological interpretation and the proposal for the construction in the brain. A couple of remarks: I would be interested in understanding the robustness of the sign-symmetry algorithm w.r.t. for instance dropout and (mini)batch size, and to see the behaviour of the algorithm on datasets with small sample size; second, there is probably too much stress on comparing w/ (Bartunov , 2018), while the manuscript is robust enough not to need such motivation. Minor: refs are not homogeneous, first names citations are not consistent.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this “weight transport problem” (Grossberg, 1987), two biologically-plausible algorithms, proposed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP’s weight symmetry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry (SS) algorithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs. We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO). Surprisingly, networks trained with sign-symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018) and establish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 In the submitted manuscript, the authors compare the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures, with the aim of testing biologically-plausible learning algorithms alternative to the more artificial backpropagation. The obtained results are promising and quite different to those in (Bartunov , 2018) and lead to the conclusion that biologically plausible learning algorithms in general and sign- symmetry in particular are effective alternatives for ANN training. Although all the included ideas are not fully novel, the manuscript shows a relevant originality, paving the way for what can be a major breakthrough in deep learning theory and practice in the next few years. The paper is well written and organised, with the tackled problem well framed into the context. The suite of experiments is broad and diverse and overall convincing, even if the performances are not striking. Very interesting the biological interpretation and the proposal for the construction in the brain. A couple of remarks: I would be interested in understanding the robustness of the sign-symmetry algorithm w.r.t. for instance dropout and (mini)batch size, and to see the behaviour of the algorithm on datasets with small sample size; second, there is probably too much stress on comparing w/ (Bartunov , 2018), while the manuscript is robust enough not to need such motivation. Minor: refs are not homogeneous, first names citations are not consistent.
assistant
 In the submitted manuscript, the authors compare the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures, with the aim of testing biologically-plausible learning algorithms alternative to the more artificial backpropagation. The obtained results are promising and quite different to those in (Bartunov , 2018) and lead to the conclusion that biologically plausible learning algorithms in general and sign- symmetry in particular are effective alternatives for ANN training. Although all the included ideas are not fully novel, the manuscript shows a relevant originality, paving the way for what can be a major breakthrough in deep learning theory and practice in the next few years. The paper is well written and organised, with the tackled problem well framed into the context. The suite of experiments is broad and diverse and overall convincing, even if the performances are not striking. Very interesting the biological interpretation and the proposal for the construction in the brain. A couple of remarks: I would be interested in understanding the robustness of the sign-symmetry algorithm w.r.t. for instance dropout and (mini)batch size, and to see the behaviour of the algorithm on datasets with small sample size; second, there is probably too much stress on comparing w/ (Bartunov , 2018), while the manuscript is robust enough not to need such motivation. Minor: refs are not homogeneous, first names citations are not consistent.