Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

First, we find that only 2% of supervised updates (epochs and images) are needed to achieve \(\sim\)80% of a fully trained model's match to adult ventral stream.

The review comment to be evaluated is:

### Strength:

First of all, the question addressed in the paper is essential for both the machine learning and neuroscience community and the author aim at addressing in a well-written piece of work that also clearly contextualizes and motivates the issue at hand. This work uses a state-of-the-art neural architecture, instead of a reduced version of models as is often the case in this type of work. The authors also propose a good analysis and clearly lay out their results, although their interpretation is one of the weaknesses of the paper.

### Weakness:

Although it is true that this work has many strengths it is hard to overcome the following weaknesses, which question the motivation of the approach undertaken. First, there exists a set of more principled approaches that have been developed over the last 4-5 years for training ANNs in a biologically plausible way.  Second, the results obtained are very subjective to the authors as the result of the improvement of the score is minor.

Although it is true that we don’t expect the authors to solve how the visual system develops and that this work corresponds to a proof of principle, it is still hard to navigate the plausibility of the assumptions made among all possibilities. On the other hand, there is a large set of work that has been proposed to build biologically plausible deep neural networks starting from clear objective function minimization and enforcing locality of learning rules. Compared to this set of works this one seems more adhoc than principled. This represents an important weakness when addressing the more neuroscience-friendly community.

Indeed the approach proposed is more of a combination of existing approaches rather than a novel idea: the use of training of a subset of layers, different weights initialization based on the distribution of trained weights. It is not clear how this provides new insight to the statistical learning community.

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT).

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience. Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse.

We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training.

However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights.

High brain predictivity can be achieved with a relatively small number of supervised synaptic updates All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

_WC+CT_ therefore most positively interact with a small budget of supervised updates (which is the focus of this work).

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review of the evidence and claim "only 2% of supervised updates (epochs and images) are needed to achieve ~80% of a fully trained model's match to adult ventral stream", I find this to be a moderately overstated claim.

Figure 1A and Figure 8A show that with 2% of updates, the brain predictivity score reaches approximately 76%, not 80%. While this is close, it's not quite the claimed level. The claim lacks important context about the variability in performance across different visual areas and doesn't acknowledge that early visual areas (V1, V2) are better predicted than higher areas with fewer updates, as shown in Figure 1B.

The review comments also highlight that these improvements are "minor" and the approach is "more adhoc than principled." This suggests the claim could benefit from more measured language and explicit acknowledgment of limitations.

<score>3</score>

Justification:
1. The evidence shows ~76% performance at 2% updates, not 80% as claimed
2. The claim oversimplifies varying performance across different visual areas
3. Reviewer notes the improvements are "minor"
4. The claim would be more accurate if it:
   - Used precise performance figures
   - Acknowledged variability across visual areas
   - Included caveats about the ad hoc nature of the approach


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

First, we find that only 2% of supervised updates (epochs and images) are needed to achieve \(\sim\)80% of a fully trained model's match to adult ventral stream.

The review comment to be evaluated is:

## Strengths
The paper addresses an important problem in theoretical neuroscience: while trained deep networks appear to be good models of the visual stream, the amount of training they need to get there is incompatible with biology.

Two of the proposed approaches to reduce the amount of training are somewhat straightforward: training for fewer epochs and freezing the amount of trained layers. However, I think the provided results are important as they show good fit to brain data can be achieved without closely following the training procedures of deep learning.

The last approach, weight clustering, is perhaps more interesting to the theoretical neuro community, as it works in the same way as genomically encoding the network (the “at birth” state).

Importantly, the proposed ways to reduce the amount of training are tested not only using ImageNet performance, but also ventral stream data, which is arguably more important for neuroscience.

Overall, the paper presents a novel set of results for an important problem, and supports its claims with a significant number of experiments.

## Weaknesses
As mentioned before, I think the weight compression part is the most valuable one, and therefore it should receive more attention in the paper.
1. Clustering should be better explained in the main text. Especially how the number of clusters was chosen.
2. In the beginning of Sec. 4, you discuss how many bits it would take to encode different networks. I think the same should be done for your experiments. E.g. what is the size of CORnet-S vs. the size of a compressed network?
3. I think similar weight compression techniques have been used before, see http://proceedings.mlr.press/v80/wu18h.html for example
4. In general, it’d be interesting to see how at-birth performance changes with the amount of “genetically encoded” information, i.e. the number of clusters passed through compression. E.g., would doubling the amount of clusters you use drastically improve performance? I’m not expecting this experiment to appear in the paper, but perhaps it’s worth discussing.

The majority of results is limited to one architecture, CORnet-S, which is not a very standard deep learning model. However, I think it is OK as repeating the experiments on something like ResNet50 would be very resource-consuming, and CORnet-S appears to be a better model of the visual stream anyway.

## Recommendation
As the paper addresses an important problem with a novel approach and extensive experiments, I recommend to accept the paper.

## Minor points
In Fig.1, were learning rates tuned individually for each length of training, or were short runs just snapshots of the longer ones? If it’s the latter, I’d imagine you can get better results by cranking up the learning rate and later decreasing it before you hit a plateau (e.g., by spacing out the same three LR decreases within a shorter run). I guess doing it for each point in Fig.1A is too much training, but repeating all points in Fig.1B-C should be possible?

> For instance, the fully trained model’s 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans

I would say that a more correct conclusion is that supervised learning is a bad concept for development, but I also think your results are applicable beyond supervised learning. Basically, you show that reducing the amount of updates in backprop retains good Brain-Score performance. But that probably tells more about SGD dynamics in deep networks rather than a specific loss function, and might easily translate to, say, more realistic self-supervised learning. (I guess you briefly mention this point in the discussion.)

> Abstract

> First, we find that only 2% of supervised updates (epochs and images) are needed to achieve ∼80% of the match to adult ventral stream.

80% of the best performing network -- currently it reads as 80% of absolute performance, which would presumably be 1.0 Brain-Score?

The caption of Fig.3C-D should better explain what exactly is trained in each setting

In Sec.6, you should specify that you used MobileNet V1 and ResNet-50.

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT).

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience. Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse.

We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training.

However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights.

High brain predictivity can be achieved with a relatively small number of supervised synaptic updates All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

_WC+CT_ therefore most positively interact with a small budget of supervised updates (which is the focus of this work).

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review of the claim "only 2% of supervised updates (epochs and images) are needed to achieve ~80% of a fully trained model's match to adult ventral stream" against the evidence:

The evidence from Figure 1A and associated text directly supports this claim. The graph clearly shows that at around 2% of updates, the brain predictivity score reaches approximately 76-80% of the fully trained model's performance. This is further substantiated by detailed experimental results and figures throughout the paper. The claim is specific, quantitative, and accurately reflects the empirical findings without exaggeration.

The review comments do not dispute this particular claim, and in fact acknowledge the importance of demonstrating good brain-fit with reduced training.

<score>1</score>

Justification:
1. Direct visual evidence in Figure 1A shows the 2% updates → ~80% performance relationship
2. The claim uses appropriately cautious language ("~80%")
3. Multiple experiments and analyses throughout the paper validate this finding
4. The claim is precise and quantitative, matching the actual experimental results
5. The limitations and scope are appropriately acknowledged
6. Review comments support the validity of these findings


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

First, we find that only 2% of supervised updates (epochs and images) are needed to achieve \(\sim\)80% of a fully trained model's match to adult ventral stream.

The review comment to be evaluated is:

The authors present thorough analyses of standard feedforward networks that quantitatively demonstrate that such networks can model the visual stream well while requiring far fewer coordinated parameter changes than occur with traditional training methods.  In particular, they show that very little supervision is required for strong matches to neural responses, that plausible distributions of initial random weights can match neural responses very well, and that weights can be updated very sparsely while still achieving quite high neural response matches.

Such findings are highly relevant to allowing for such networks to be more plausibly accepted by the neuroscience community as valid models of cortical networks.  The striking finding that well-chosen random weights (Weight Compression) can predict neural responses with more than 50% accuracy is  especially interesting, as it suggests that indeed there may be less training required in mammalian brains compared to randomly initialized neuronal networks. This possibility opens up further avenues of study relating to the ability of real brains to learn with far fewer examples compared to ANNs.

Figure 4b further demonstrates this possibility, showing that, under the ethologically relevant condition of little supervision, WC + Critical Training, where only a small subset of weights in so-called critical layers are trained, greatly outperforms traditional training.  We note that, in the ideal case, a biologically plausible method might be found that performs well both under little supervision, as WC+CT does here, and under a large amount of supervision, as traditional backprop does, the demonstration of principle for the former case already represents appreciable progress.

However, little in the way of tying model predictivity to accuracy on another task was explored. While tying brain predictivity to network performance is of course a very active area in its own right, we feel that addressing this directly within the context of the present work would facilitate readers' abilities to contextualize the present study with respect to ANN performance.

Overall, we find the authors' presented work to be of great interest to those interested in applying ML models to theoretical questions in neuroscience and to have a thorough set of analyses that both illustrates progress in this area while suggesting future avenues of work.

Minor:
- opening quotes are backwards throughout paper

The evidence to be evaluated is:We evaluated the brain predictivity scores of CORnet-S variants that were trained with a combination of fewer epochs and images.

Similarly, when training until convergence with fractions of the 1.28M total images, 50,000 images are sufficient to obtain high neural scores (80% of full training in V1, V2, V4, IT).

For instance, the fully trained model's 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans (Yarbus, 1967; Gibaldi & Sabatini, 2020).

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience. Training thin down-sampling layers reduces the number of updated synapses while maintaining high brain predictivity

As a proof-of-principle that training a reduced set of layers can retain high performance, we propose a novel thin training technique, which we term _Critical Training (CT: Figure 3A). CT_ updates only the weights in critical layers, instead of updating every single model synapse.

We explore successive variants of applying _CT_ up to a block in the architecture and then training the following blocks, e.g. freezing V1, V2, V4 with critical training of the respective down-sampling layers and additional IT training.

However, while the average score with _DT_ (gray) already drops below 65% with over a quarter of trained parameters remaining and _BN_ drops to 62% with very few parameters, _CT_ (blue) maintains over 75% with only 1.4 out of 52.8 million parameters trained.

By reducing the number of trained parameters, _Critical Training_ also yields engineering benefits with more than 40% of the ImageNet score maintained at \(<3\%\) of parameters trained - a significant improvement over 22% accuracy with _BatchNormNorm_(Frankle et al., 2021) while adding only a small number of additional weights.

High brain predictivity can be achieved with a relatively small number of supervised synaptic updates All three training reduction methods independently minimize the number of supervised synaptic updates required to reach a reasonably high brain predictivity score. Reducing the number of supervised updates minimizes the number of epochs and images (Section 3); _Weight Compression (WC)_ improves the at-birth synaptic connectivity for high initial scores with no training at all (Section 4); and _Critical Training (CT)_ reduces the number of synapses that are updated during training (Section 5). Testing synergies between these strategies, we combined all three methods to build novel models that only require a small number of supervised synaptic updates to reasonably capture the mechanisms of adult ventral visual stream processing and object recognition behavior.

_WC+CT_ yield strong benefits (green numbers) in a regime with few supervised updates, improving by up to 27 percent points when training for only 1 epoch on 1,000 images.

_WC+CT_ therefore most positively interact with a small budget of supervised updates (which is the focus of this work).

We developed a range of models with neural and behavioral scores approaching those of the current leading model of the adult ventral visual stream as quantified in Brain-Score, while requiring only a fraction of supervised synaptic updates. These models were built by complementarily 1) reducing the number of supervised updates, i.e. training epochs and labeled images; 2) improving the "at birth" distribution of synaptic connectivity; and 3) training only critical synapses at the end of each model area.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

Recent unsupervised approaches are starting to rival the classification performance of supervised models (Caron et al., 2018; Henaff et al., 2019; Zhuang et al., 2020) and combining them with the advances presented here could further reduce the number of synaptic updates.

The changes to model initialization and training presented here serve as a proof-of-principle that models can be changed to more closely align with primate development by reducing training steps with labeled images and improving initialization. It is also possible to achieve high brain predictivity when training only a fraction of weights, but all these models are still far from the actual biological mechanisms.

Weight Compression (WC)'s cluster-based initialization generates an "at-birth" network that already captures some useful aspects of the visual inputs in a compressed manner and allows the system to learn faster.

Our best model _WC+CT_ benefits from a combination of improved initialization through weight compression, and critical training. Figure 8A shows models with standard initialization and training all weights, but with fewer supervised updates (cf. Figure 1), models that only train down-sampling layers (_CT_), and models that combine critical training with weight compression (_WC+CT_).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 4: Benefits of combining training reductions for high brain predictivity with few supervised synaptic updates and limitations of techniques in high-update regimes. A (log x-axis) By reducing updates with a combination of fewer supervised updates (Figure 1), improved initialization WC (Figure 2), and training only down-sampling layers CT (Figure 3), the resulting models (dark blue dots; fewer supervised updates alone in light blue) maintain high brain predictivity scores while requiring only a fraction of supervised synaptic updates compared to standard CORnet-S (black dot, top right). B Comparison between WC-initialized models trained with CT versus standardly initialized models training all weights, when varying training epochs and labeled images. Colors represent their percent point difference in brain predictivity scores. WC+CT improve performance in regimes with few epochs and images, but fall behind in regimes with many labeled images and epochs.

Input: Figure 8: Detailed analysis of WC+CT. A When reducing the number of supervised synaptic updates, adding critical training (dark grey) and adding weight compression initialization (dark blue) both improve scores in brain predictivity at the same number of supervised synaptic updates, in comparison to a model with standard initialization and training all weights (bright blue). B Brain predictivities for the WC+CT model when trained with a range of epochs and labeled images. C Same as B, but for a standardly initialized (KN) model training all weights.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Let me assess the claim "only 2% of supervised updates (epochs and images) are needed to achieve ~80% of a fully trained model's match to adult ventral stream."

The evidence largely supports this claim:

1. Figure 1A clearly shows that at around 2% of updates (~10^13 updates), the brain predictivity score reaches approximately 80% of the fully trained model's performance.

2. The reviewer acknowledges the strong findings regarding minimal supervision requirements, noting the work demonstrates "very little supervision is required for strong matches to neural responses."

3. The text evidence confirms achieving "80% of full training in V1, V2, V4, IT" with reduced training.

However, two minor points prevent this from being a perfectly stated claim:
- The "only" suggests this is a hard minimum, when different model variants show varying performance
- The "~80%" is slightly imprecise - some figures show slightly different percentages depending on specific conditions

Overall, this claim is very well-supported by the evidence with only minor imprecisions, warranting a score of 2 (slightly overstated but generally accurate).

<score>2</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training.

The evidence to be evaluated is:Early ventral stream regions V1 and V2 are predicted especially well with no loss in score but we note that these two benchmarks are less well predicted by the trained model to begin with. V4 scores also approximate those of a trained model relatively well (75%). The major drop occurs in the IT and especially behavioral scores where CORnet-SWC only reaches 39% and 6% of the trained model's score respectively.

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience.

For example, neurons in supra- and infra-granular layers adapt more rapidly than those in layer 4, where inputs from lower areas arrive, as observed in rat somatosensory cortex (Diamond et al., 1994) and primate V1 (Schoups et al., 2001).

_WC_ initialization improves most over _KN_ in early visual regions V1 and V2, while additional training with _CT_ is most beneficial in mid- to high-level visual cortex V4 and IT, as well as the behavioral benchmark.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model. The V1 score is identical over all training states, since we do not train the V1 area.

V1 scores do not change because weights in the V1 model area are all frozen.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After careful review, the claim that "training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally" is generally well-stated and supported by multiple pieces of evidence. The graphs show that V1 and V2 (early areas) maintain consistently high scores with minimal improvement during training, while IT and behavioral scores (higher areas) show substantial improvements over training epochs. This is corroborated by quantitative evidence showing V1/V2 achieving >80% scores early on, while IT and behavioral benchmarks require more training to improve. The data clearly demonstrates differential benefits of training across the visual hierarchy.

<score>1</score>

Justification:
1. Multiple figures (1B/C, 9A/B) consistently show V1/V2 scores remaining high and stable
2. Evidence states "V1 and V2 are predicted especially well with no loss in score"
3. Graphs demonstrate substantial improvements in IT/behavioral scores with training
4. Text confirms "IT and Behavior benchmarks continuously improve over later epochs"
5. The claim's wording appropriately reflects the magnitude and direction of these differences without exaggeration
6. Supporting developmental evidence is provided showing earlier maturation of early visual processing vs complex visual behaviors


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training.

The review comment to be evaluated is:

Strengths:
- The paper is clear and well written
- The analysis is thorough and supports the authors' arguments
- The method of initializing weights via clusters of trained kernels is interesting
- The "critical training" method of training only selected layers is also interesting

Weaknesses:
- "Recently, such models have been criticized due to how their learning departs from brain development (Marcus, 2004; Grossberg, 2020; Zador, 2019)." This seems to be the motivation for the work, but those authors don't raise the issues that are addressed in this paper. The Marcus book predates deep learning. Grossberg focuses on catastrophic forgetting. Zador focuses on the sparsity of labelled data in life. I am not sure whether the number of weight updates needed to train deep networks presents a substantial independent concern about their biological plausibility, particularly given the possibility of constant unsupervised learning. If the issue has been raised in the literature, more specific references would help here. If not, I think the authors should argue more specifically that it is a problem, before going on to solve it.
- The cluster-based initialization method seems to be presented as a way of bringing some learning results through a genetic bottleneck, but the biological plausibility isn't obvious. Further discussion of how this relates to and departs from biology would be helpful.
- It's interesting that critical training worked so well, but in the context of the paper's goals, the biological relevance of this method should also be laid out more clearly.

Minor:
- I don't think it was shown how classification performance is affected by the cluster-based initialization method, but it would be nice to see that.
- "37 bits per synapse (Zador, 2019)" This refers to the binary connectivity pattern (an issue of architecture) rather than the weights (which are addressed in this paper).

The evidence to be evaluated is:Early ventral stream regions V1 and V2 are predicted especially well with no loss in score but we note that these two benchmarks are less well predicted by the trained model to begin with. V4 scores also approximate those of a trained model relatively well (75%). The major drop occurs in the IT and especially behavioral scores where CORnet-SWC only reaches 39% and 6% of the trained model's score respectively.

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience.

For example, neurons in supra- and infra-granular layers adapt more rapidly than those in layer 4, where inputs from lower areas arrive, as observed in rat somatosensory cortex (Diamond et al., 1994) and primate V1 (Schoups et al., 2001).

_WC_ initialization improves most over _KN_ in early visual regions V1 and V2, while additional training with _CT_ is most beneficial in mid- to high-level visual cortex V4 and IT, as well as the behavioral benchmark.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model. The V1 score is identical over all training states, since we do not train the V1 area.

V1 scores do not change because weights in the V1 model area are all frozen.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
I'll evaluate the claim: "Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training."

1. Summary:
The evidence strongly supports this claim. Figures 9A and 9B show that early visual areas (V1, V2) achieve high scores (>80%) with minimal training, while higher areas (IT, Behavior) require more extensive training to improve. V1 scores remain constant due to frozen weights, and V2 shows minimal changes. The text explicitly states that "lower visual areas (V1, V2) are approximated earlier in training" and higher regions like IT and behavioral benchmarks show continuous improvement over training. This pattern is consistently demonstrated across multiple analyses and aligns with developmental studies in primates.

<score>1</score>

The claim is well-stated (score of 1) because it:
- Accurately reflects the differential impact of training on higher vs. lower visual areas
- Is supported by multiple pieces of evidence (figures and text)
- Aligns with developmental findings
- Makes no exaggerated claims beyond what the data shows
- Is precise in describing the marginal improvements in early areas and greater benefits in higher areas


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training.

The review comment to be evaluated is:

### Strength:

First of all, the question addressed in the paper is essential for both the machine learning and neuroscience community and the author aim at addressing in a well-written piece of work that also clearly contextualizes and motivates the issue at hand. This work uses a state-of-the-art neural architecture, instead of a reduced version of models as is often the case in this type of work. The authors also propose a good analysis and clearly lay out their results, although their interpretation is one of the weaknesses of the paper.

### Weakness:

Although it is true that this work has many strengths it is hard to overcome the following weaknesses, which question the motivation of the approach undertaken. First, there exists a set of more principled approaches that have been developed over the last 4-5 years for training ANNs in a biologically plausible way.  Second, the results obtained are very subjective to the authors as the result of the improvement of the score is minor.

Although it is true that we don’t expect the authors to solve how the visual system develops and that this work corresponds to a proof of principle, it is still hard to navigate the plausibility of the assumptions made among all possibilities. On the other hand, there is a large set of work that has been proposed to build biologically plausible deep neural networks starting from clear objective function minimization and enforcing locality of learning rules. Compared to this set of works this one seems more adhoc than principled. This represents an important weakness when addressing the more neuroscience-friendly community.

Indeed the approach proposed is more of a combination of existing approaches rather than a novel idea: the use of training of a subset of layers, different weights initialization based on the distribution of trained weights. It is not clear how this provides new insight to the statistical learning community.

The evidence to be evaluated is:Early ventral stream regions V1 and V2 are predicted especially well with no loss in score but we note that these two benchmarks are less well predicted by the trained model to begin with. V4 scores also approximate those of a trained model relatively well (75%). The major drop occurs in the IT and especially behavioral scores where CORnet-SWC only reaches 39% and 6% of the trained model's score respectively.

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience.

For example, neurons in supra- and infra-granular layers adapt more rapidly than those in layer 4, where inputs from lower areas arrive, as observed in rat somatosensory cortex (Diamond et al., 1994) and primate V1 (Schoups et al., 2001).

_WC_ initialization improves most over _KN_ in early visual regions V1 and V2, while additional training with _CT_ is most beneficial in mid- to high-level visual cortex V4 and IT, as well as the behavioral benchmark.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model. The V1 score is identical over all training states, since we do not train the V1 area.

V1 scores do not change because weights in the V1 model area are all frozen.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
Let me evaluate the claim: "Training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training."

Summary:
The evidence actually supports this claim quite well. Looking at Figures 9A and 9B, early visual areas (V1, V2) show high scores (>80%) with minimal training, while higher areas (IT and Behavior) require more extensive training to improve and still fall short of fully trained models. The text evidence confirms that V1 and V2 are predicted well even with minimal training, while IT and behavioral scores show major drops (reaching only 39% and 6% respectively). The reviewer's comments about "minor" improvements don't directly contradict this pattern of results.

<score>1</score>

Justification:
1. The evidence directly shows differential effects of training across visual hierarchy:
- Early areas (V1/V2) achieve >80% scores with minimal training
- Higher areas (IT/Behavior) require more training and show larger improvements
- Figures 9A/B clearly visualize this pattern
- Text explicitly states "early visual areas converge earlier during training"
- Developmental studies cited support this pattern

2. The claim avoids overstatement by:
- Using measured language ("marginally" for early areas)
- Not making absolute claims about improvement magnitudes
- Accurately reflecting the relative benefits across regions
- Being consistent with both quantitative data and qualitative findings

The claim represents a well-calibrated interpretation of the available evidence.


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training.

The review comment to be evaluated is:

## Strengths
The paper addresses an important problem in theoretical neuroscience: while trained deep networks appear to be good models of the visual stream, the amount of training they need to get there is incompatible with biology.

Two of the proposed approaches to reduce the amount of training are somewhat straightforward: training for fewer epochs and freezing the amount of trained layers. However, I think the provided results are important as they show good fit to brain data can be achieved without closely following the training procedures of deep learning.

The last approach, weight clustering, is perhaps more interesting to the theoretical neuro community, as it works in the same way as genomically encoding the network (the “at birth” state).

Importantly, the proposed ways to reduce the amount of training are tested not only using ImageNet performance, but also ventral stream data, which is arguably more important for neuroscience.

Overall, the paper presents a novel set of results for an important problem, and supports its claims with a significant number of experiments.

## Weaknesses
As mentioned before, I think the weight compression part is the most valuable one, and therefore it should receive more attention in the paper.
1. Clustering should be better explained in the main text. Especially how the number of clusters was chosen.
2. In the beginning of Sec. 4, you discuss how many bits it would take to encode different networks. I think the same should be done for your experiments. E.g. what is the size of CORnet-S vs. the size of a compressed network?
3. I think similar weight compression techniques have been used before, see http://proceedings.mlr.press/v80/wu18h.html for example
4. In general, it’d be interesting to see how at-birth performance changes with the amount of “genetically encoded” information, i.e. the number of clusters passed through compression. E.g., would doubling the amount of clusters you use drastically improve performance? I’m not expecting this experiment to appear in the paper, but perhaps it’s worth discussing.

The majority of results is limited to one architecture, CORnet-S, which is not a very standard deep learning model. However, I think it is OK as repeating the experiments on something like ResNet50 would be very resource-consuming, and CORnet-S appears to be a better model of the visual stream anyway.

## Recommendation
As the paper addresses an important problem with a novel approach and extensive experiments, I recommend to accept the paper.

## Minor points
In Fig.1, were learning rates tuned individually for each length of training, or were short runs just snapshots of the longer ones? If it’s the latter, I’d imagine you can get better results by cranking up the learning rate and later decreasing it before you hit a plateau (e.g., by spacing out the same three LR decreases within a shorter run). I guess doing it for each point in Fig.1A is too much training, but repeating all points in Fig.1B-C should be possible?

> For instance, the fully trained model’s 55 million supervised updates translate to 5.5 updates every second, whereas the model with 1 million updates and 76% relative brain predictivity translates to one labeled image update every 10 seconds which appears more plausible given the upper limit of 2-3 saccades per second in humans

I would say that a more correct conclusion is that supervised learning is a bad concept for development, but I also think your results are applicable beyond supervised learning. Basically, you show that reducing the amount of updates in backprop retains good Brain-Score performance. But that probably tells more about SGD dynamics in deep networks rather than a specific loss function, and might easily translate to, say, more realistic self-supervised learning. (I guess you briefly mention this point in the discussion.)

> Abstract

> First, we find that only 2% of supervised updates (epochs and images) are needed to achieve ∼80% of the match to adult ventral stream.

80% of the best performing network -- currently it reads as 80% of absolute performance, which would presumably be 1.0 Brain-Score?

The caption of Fig.3C-D should better explain what exactly is trained in each setting

In Sec.6, you should specify that you used MobileNet V1 and ResNet-50.

The evidence to be evaluated is:Early ventral stream regions V1 and V2 are predicted especially well with no loss in score but we note that these two benchmarks are less well predicted by the trained model to begin with. V4 scores also approximate those of a trained model relatively well (75%). The major drop occurs in the IT and especially behavioral scores where CORnet-SWC only reaches 39% and 6% of the trained model's score respectively.

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience.

For example, neurons in supra- and infra-granular layers adapt more rapidly than those in layer 4, where inputs from lower areas arrive, as observed in rat somatosensory cortex (Diamond et al., 1994) and primate V1 (Schoups et al., 2001).

_WC_ initialization improves most over _KN_ in early visual regions V1 and V2, while additional training with _CT_ is most beneficial in mid- to high-level visual cortex V4 and IT, as well as the behavioral benchmark.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model. The V1 score is identical over all training states, since we do not train the V1 area.

V1 scores do not change because weights in the V1 model area are all frozen.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
After reviewing the evidence, the claim "training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training" is well-supported by multiple pieces of evidence:

1. Figures 1B and 1C show earlier visual areas (V1, V2) achieving high scores with minimal training while higher areas (IT, behavior) require more training to improve.

2. The text explicitly states that "Lower visual areas (V1, V2) are approximated earlier in training" and that "High scores in brain predictivity can be achieved with few supervised updates."

3. The evidence shows V1 and V2 scores remain consistently high throughout training, while IT and behavioral scores show more dramatic improvements with additional training.

4. This pattern is consistently demonstrated across different training conditions (varying epochs and image numbers).

<score>1</score>


Input:
Your role is to assess whether a claim is well-stated or overstated based on the available evidence.

The Input Information will include:
1. Original Claim: The claim under evaluation.
2. Evidence: Research findings, including figures, tables, or other relevant data supporting the claim.

Optional. review comment: Reviewer feedback relevant to the claim's validity.

The claim should be assesed with the following:
Score: Evaluate the claim on a scale of 1 to 5, where:
- 1: Well-stated (accurately reflects the evidence without exaggeration)
- 5: Overstated (exaggerates findings' significance, magnitude, or certainty)

Justification: Provide a detailed explanation that includes:
- Instances of exaggerated wording, insufficient experiments, lack of experimental details, gaps in knowledge, weak grounding in evidence, or lack of acknowledgment of limitations.
- Direct citations from relevant evidence that substantiate your reasoning.
- If review comment is included only take into account the relevant comments towards the claim.

The claim to be assessed is:

Specifically, training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training.

The review comment to be evaluated is:

The authors present thorough analyses of standard feedforward networks that quantitatively demonstrate that such networks can model the visual stream well while requiring far fewer coordinated parameter changes than occur with traditional training methods.  In particular, they show that very little supervision is required for strong matches to neural responses, that plausible distributions of initial random weights can match neural responses very well, and that weights can be updated very sparsely while still achieving quite high neural response matches.

Such findings are highly relevant to allowing for such networks to be more plausibly accepted by the neuroscience community as valid models of cortical networks.  The striking finding that well-chosen random weights (Weight Compression) can predict neural responses with more than 50% accuracy is  especially interesting, as it suggests that indeed there may be less training required in mammalian brains compared to randomly initialized neuronal networks. This possibility opens up further avenues of study relating to the ability of real brains to learn with far fewer examples compared to ANNs.

Figure 4b further demonstrates this possibility, showing that, under the ethologically relevant condition of little supervision, WC + Critical Training, where only a small subset of weights in so-called critical layers are trained, greatly outperforms traditional training.  We note that, in the ideal case, a biologically plausible method might be found that performs well both under little supervision, as WC+CT does here, and under a large amount of supervision, as traditional backprop does, the demonstration of principle for the former case already represents appreciable progress.

However, little in the way of tying model predictivity to accuracy on another task was explored. While tying brain predictivity to network performance is of course a very active area in its own right, we feel that addressing this directly within the context of the present work would facilitate readers' abilities to contextualize the present study with respect to ANN performance.

Overall, we find the authors' presented work to be of great interest to those interested in applying ML models to theoretical questions in neuroscience and to have a thorough set of analyses that both illustrates progress in this area while suggesting future avenues of work.

Minor:
- opening quotes are backwards throughout paper

The evidence to be evaluated is:Early ventral stream regions V1 and V2 are predicted especially well with no loss in score but we note that these two benchmarks are less well predicted by the trained model to begin with. V4 scores also approximate those of a trained model relatively well (75%). The major drop occurs in the IT and especially behavioral scores where CORnet-SWC only reaches 39% and 6% of the trained model's score respectively.

These findings further suggests that matching representations in higher visual regions might be especially dependent on visual experience whereas early visual regions might already be reasonably well specified without experience.

For example, neurons in supra- and infra-granular layers adapt more rapidly than those in layer 4, where inputs from lower areas arrive, as observed in rat somatosensory cortex (Diamond et al., 1994) and primate V1 (Schoups et al., 2001).

_WC_ initialization improves most over _KN_ in early visual regions V1 and V2, while additional training with _CT_ is most beneficial in mid- to high-level visual cortex V4 and IT, as well as the behavioral benchmark.

The fact that early visual areas converge earlier during training (Figure 1) and are better predicted than higher areas by _WC_ initialization is consistent with developmental studies of the primate ventral stream. In humans, behaviors that rely on low-level spatial and temporal processing of visual inputs reach adult-like performance considerably earlier than complex visual behaviors that rely on higher cortical regions, such as face perception (Ellenberg et al., 1999; Grill-Spector et al., 2008).

The specific benchmark scores when either training with all labeled images for a varying number of epochs (Figure 9A) or when training with fewer labeled images until convergence (Figure 9B) show the benchmarks of early visual achieve the best results, relative to a fully trained model. The V1 score is identical over all training states, since we do not train the V1 area.

V1 scores do not change because weights in the V1 model area are all frozen.

Input: Figure 1: High scores in brain predictivity can be achieved with few supervised updates (log xaxes). A Average brain predictivity of models trained with a range of supervised updates (different combinations of epochs × images). Each dot is a different hypothesis of how the ventral visual stream might have developed and shows the adult brain-likeness score that is achieved by that model. Fairly brain-like representations are already realized with few supervised updates, relative to a fully trained model (black dot; see also Figure 6). Standardly trained CORnet-S is set to be 100% score in brain predictivity on the benchmarks. A “pixels” baseline achieves 7% of this brain predictivity score. B Individual brain predictivity scores over epochs. Models start to approximate the primate ventral stream with few training epochs. Lower visual areas (V1, V2) are approximated earlier in training. C Like B, but number of training images instead of epochs. Few images are sufficient to approximate especially early visual areas.

Input: Figure 9: Individual brain benchmark scores for WC+CT model A Individual brain predictivitiy scores of WC+CT models trained with a range of epochs on all images. These models score especially high on V1, V2 and V4 already after one epoch in comparison to a model with standard initialization training all weights. IT and Behavior benchmarks continuously improve over later epochs as well but fall short of a fully trained model. B Like A, but with models trained until convergence on different numbers of labeled images, up to the full dataset of 1.28M images (rightmost points). As in A we see > 80% V1, V2 and V4 scores with only 100,000 images. For comparable IT and behavioral scores, more images are required.

Input:
You should:
1. Carefully review the claim and the text and image evidence. Summarize how the evidence influenced your evaluation of the claim's validity and strength and briefly explain whether the claim is wellstated or overstated on a scale of 1 to 5 (up to 100 words).
2. Provide the final score (choose from 1 to 5), in the format: <score>{score}</score>.

Output:
1. Summary of Evaluation:
The claim "training benefits predictions of higher visual cortex the most whereas predictions of earlier areas improve only marginally over the course of training" is well-supported by multiple pieces of evidence. Figures 1B, 9A, and 9B clearly show that lower visual areas (V1, V2) achieve high scores early and improve minimally with training, while higher areas (IT) and behavior scores show substantial improvements with more training. The text evidence explicitly states that "early visual areas converge earlier during training" and higher regions (IT, behavior) "continuously improve over later epochs." The review comment also indirectly supports this pattern. The claim accurately reflects the evidence without exaggeration.

<score>1</score>

Detailed Justification:
- The figures demonstrate minimal changes in V1/V2 scores across training epochs/images while showing progressive improvements in higher areas
- The text directly states "early visual areas converge earlier during training"
- Multiple data points confirm early areas achieve high scores with minimal training (">80% V1, V2 and V4 scores with only 100,000 images")
- The claim avoids overgeneralization by specifically referring to "predictions" rather than making broader claims about development
- The wording "marginally" accurately describes the small changes seen in early visual areas
- No exaggeration of effect sizes or unsupported claims are present