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:

Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved "at birth" (i.e. no training at all).

The evidence to be evaluated is:If few supervised updates can get model representations fairly close to a fully trained model (Figure 1), how close are the initial representations without any training? In relation to biology and following the introduced framework of treating all consecutive training as developmental learning, these "at-birth" synaptic connections would result from information encoded in the genome as a product of evolution. Due to the genome's capacity bottleneck, it is infeasible to precisely encode every synapse. Primary visual cortex alone contains \(\sim\)1.4E8 neurons per hemisphere (Leuba & Krafts, 1994), \(\sim\)1E3 synapses per neuron, each requiring \(\sim\)37 bits per synapse (Zador, 2019). Thus, without any clever rules, specifying the connections in only one hemisphere of V1 could require up to \(\sim\)5.2E12 bits - orders of magnitude more than the entire genome's 1GB = 8E9 bits (Zador, 2019). Sampling synaptic weights from reasonably compressed distributions on the other hand places little memory requirements on genetic encoding while potentially yielding useful initial weights.

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer.

This model is _not trained at all_ and we only evaluate the goodness of its initial wiring on the suite of Brain-Score benchmarks. Strikingly, we find that even without any training, CORnet-S\({}_{\text{WC}}\) achieves \(54\pm 1.5\%\)of the brain predictivity score relative to a fully-trained model (Figure 2), representing a 12 percent point improvement (\(n=10\) seeds; permutation test \(p<1E-5\)) over the Kaiming Normal initialized model with a score of \(43\pm 1.7\%\).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility). 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.

While improved "at-birth" connectivity can reach 54% of a fully-trained model's score, additional experience dependent updates appear necessary to reach higher predictivities.

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).

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 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. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

We have explored a variety of weight compression methods applied to different layers and evaluate their performance "at birth" without training and when trained with critical training. 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.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner. Evolution also acquired aspects of the visual inputs (with a different strategy) and encoded them in the genome in a lower information regime. For example, we know that there are multiple neuro-developmental mechanisms, such as spontaneous retinal waves and axon guidance cues, that significantly shape the architecture and function of the visual system requiring no (or very little) visual experience ((Huberman et al., 2008)). These mechanisms depend on a relatively small number of proteins encoded in a genome and give rise to a highly complex pattern of at-birth synaptic connectivity representing a very large compression of information.

A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates.

Notably, ImageNet performance of these networks seems to not be predictive of their brain predictivity, since even untrained networks with at-chance ImageNet performance correspond reasonably well to e.g. V1 (Figures 1 and 2).

* **KN** A model initialized by standard _Kaiming Normal_ initialization without training.

* WC A model initialized by our _Weight Compression_ initialization, described in Section 4, without training.

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 2: “At-birth” synaptic connectivity yields reasonable scores in brain predictivity. A Specifying the initial weight distribution: Kaiming Normal (KN, He et al., 2015) samples from a generic Gaussian. Weight Compression (WC) compresses trained weights into low-parameter clustered distributions that weights can be initialized from. B Visualization of WC compressed parameters: Gabor filters for first layer and cluster centers for following layers with kernel size > 3. The cluster centers capture an intuitive variety of kernel types. C “At-birth” representations with WC achieve 54% score in brain predictivity of a fully trained model, with no training at all. Scores after training remain virtually unchanged (shaded bars).

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 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: 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 reviewing the claim and evidence, I find that it is well-stated and accurately reflects the data presented. The evidence clearly shows that the Weight Compression (WC) initialization method achieves 54±1.5% of brain predictivity score without any training compared to a fully trained model, which is a statistically significant 12 percentage point improvement over standard Kaiming Normal initialization (43±1.7%). This is directly supported by multiple figures and statistical tests (permutation test p<1E-5). The authors appropriately qualify their findings and acknowledge limitations.

<score>1</score>

Justification:
1. Direct empirical evidence: Figure 2C and numerical results explicitly demonstrate the 54% score
2. Statistical rigor: Includes uncertainty measures (±1.5%) and statistical testing
3. Appropriate context: Authors explain the biological relevance and limitations
4. Multiple validations: Results are shown across different figures and analyses
5. Conservative language: Claim uses precise, measured wording ("can already be achieved") rather than overstatement
6. Proper scope: The specific numerical claim aligns exactly with the presented evidence without extrapolation

The claim represents a clear example of well-stated scientific reporting that directly matches the empirical 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:

Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved "at birth" (i.e. no training at all).

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:If few supervised updates can get model representations fairly close to a fully trained model (Figure 1), how close are the initial representations without any training? In relation to biology and following the introduced framework of treating all consecutive training as developmental learning, these "at-birth" synaptic connections would result from information encoded in the genome as a product of evolution. Due to the genome's capacity bottleneck, it is infeasible to precisely encode every synapse. Primary visual cortex alone contains \(\sim\)1.4E8 neurons per hemisphere (Leuba & Krafts, 1994), \(\sim\)1E3 synapses per neuron, each requiring \(\sim\)37 bits per synapse (Zador, 2019). Thus, without any clever rules, specifying the connections in only one hemisphere of V1 could require up to \(\sim\)5.2E12 bits - orders of magnitude more than the entire genome's 1GB = 8E9 bits (Zador, 2019). Sampling synaptic weights from reasonably compressed distributions on the other hand places little memory requirements on genetic encoding while potentially yielding useful initial weights.

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer.

This model is _not trained at all_ and we only evaluate the goodness of its initial wiring on the suite of Brain-Score benchmarks. Strikingly, we find that even without any training, CORnet-S\({}_{\text{WC}}\) achieves \(54\pm 1.5\%\)of the brain predictivity score relative to a fully-trained model (Figure 2), representing a 12 percent point improvement (\(n=10\) seeds; permutation test \(p<1E-5\)) over the Kaiming Normal initialized model with a score of \(43\pm 1.7\%\).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility). 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.

While improved "at-birth" connectivity can reach 54% of a fully-trained model's score, additional experience dependent updates appear necessary to reach higher predictivities.

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).

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 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. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

We have explored a variety of weight compression methods applied to different layers and evaluate their performance "at birth" without training and when trained with critical training. 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.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner. Evolution also acquired aspects of the visual inputs (with a different strategy) and encoded them in the genome in a lower information regime. For example, we know that there are multiple neuro-developmental mechanisms, such as spontaneous retinal waves and axon guidance cues, that significantly shape the architecture and function of the visual system requiring no (or very little) visual experience ((Huberman et al., 2008)). These mechanisms depend on a relatively small number of proteins encoded in a genome and give rise to a highly complex pattern of at-birth synaptic connectivity representing a very large compression of information.

A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates.

Notably, ImageNet performance of these networks seems to not be predictive of their brain predictivity, since even untrained networks with at-chance ImageNet performance correspond reasonably well to e.g. V1 (Figures 1 and 2).

* **KN** A model initialized by standard _Kaiming Normal_ initialization without training.

* WC A model initialized by our _Weight Compression_ initialization, described in Section 4, without training.

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 2: “At-birth” synaptic connectivity yields reasonable scores in brain predictivity. A Specifying the initial weight distribution: Kaiming Normal (KN, He et al., 2015) samples from a generic Gaussian. Weight Compression (WC) compresses trained weights into low-parameter clustered distributions that weights can be initialized from. B Visualization of WC compressed parameters: Gabor filters for first layer and cluster centers for following layers with kernel size > 3. The cluster centers capture an intuitive variety of kernel types. C “At-birth” representations with WC achieve 54% score in brain predictivity of a fully trained model, with no training at all. Scores after training remain virtually unchanged (shaded bars).

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 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: 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 reviewing the evidence, the claim that "54% of the brain match can be achieved at birth" appears to be well-stated. The evidence strongly supports this through:

1. Multiple figures showing consistent 54±1.5% brain predictivity scores for WC initialization without training
2. Statistical significance (permutation test p<1E-5) compared to standard initialization
3. Detailed data across multiple benchmarks and experiments
4. Clear acknowledgment of limitations and the model's simplified nature compared to actual biological mechanisms

The reviewers did not dispute this specific claim, though they requested more clarity on biological plausibility of the initialization method. The claim carefully specifies the exact percentage and appropriately qualifies it as an artificial "at birth" state.

<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:

Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved "at birth" (i.e. no training at all).

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:If few supervised updates can get model representations fairly close to a fully trained model (Figure 1), how close are the initial representations without any training? In relation to biology and following the introduced framework of treating all consecutive training as developmental learning, these "at-birth" synaptic connections would result from information encoded in the genome as a product of evolution. Due to the genome's capacity bottleneck, it is infeasible to precisely encode every synapse. Primary visual cortex alone contains \(\sim\)1.4E8 neurons per hemisphere (Leuba & Krafts, 1994), \(\sim\)1E3 synapses per neuron, each requiring \(\sim\)37 bits per synapse (Zador, 2019). Thus, without any clever rules, specifying the connections in only one hemisphere of V1 could require up to \(\sim\)5.2E12 bits - orders of magnitude more than the entire genome's 1GB = 8E9 bits (Zador, 2019). Sampling synaptic weights from reasonably compressed distributions on the other hand places little memory requirements on genetic encoding while potentially yielding useful initial weights.

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer.

This model is _not trained at all_ and we only evaluate the goodness of its initial wiring on the suite of Brain-Score benchmarks. Strikingly, we find that even without any training, CORnet-S\({}_{\text{WC}}\) achieves \(54\pm 1.5\%\)of the brain predictivity score relative to a fully-trained model (Figure 2), representing a 12 percent point improvement (\(n=10\) seeds; permutation test \(p<1E-5\)) over the Kaiming Normal initialized model with a score of \(43\pm 1.7\%\).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility). 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.

While improved "at-birth" connectivity can reach 54% of a fully-trained model's score, additional experience dependent updates appear necessary to reach higher predictivities.

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).

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 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. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

We have explored a variety of weight compression methods applied to different layers and evaluate their performance "at birth" without training and when trained with critical training. 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.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner. Evolution also acquired aspects of the visual inputs (with a different strategy) and encoded them in the genome in a lower information regime. For example, we know that there are multiple neuro-developmental mechanisms, such as spontaneous retinal waves and axon guidance cues, that significantly shape the architecture and function of the visual system requiring no (or very little) visual experience ((Huberman et al., 2008)). These mechanisms depend on a relatively small number of proteins encoded in a genome and give rise to a highly complex pattern of at-birth synaptic connectivity representing a very large compression of information.

A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates.

Notably, ImageNet performance of these networks seems to not be predictive of their brain predictivity, since even untrained networks with at-chance ImageNet performance correspond reasonably well to e.g. V1 (Figures 1 and 2).

* **KN** A model initialized by standard _Kaiming Normal_ initialization without training.

* WC A model initialized by our _Weight Compression_ initialization, described in Section 4, without training.

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 2: “At-birth” synaptic connectivity yields reasonable scores in brain predictivity. A Specifying the initial weight distribution: Kaiming Normal (KN, He et al., 2015) samples from a generic Gaussian. Weight Compression (WC) compresses trained weights into low-parameter clustered distributions that weights can be initialized from. B Visualization of WC compressed parameters: Gabor filters for first layer and cluster centers for following layers with kernel size > 3. The cluster centers capture an intuitive variety of kernel types. C “At-birth” representations with WC achieve 54% score in brain predictivity of a fully trained model, with no training at all. Scores after training remain virtually unchanged (shaded bars).

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 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: 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 reviewing the claim and evidence, I find that the statement about achieving 54% brain match "at birth" through improved random distribution of synaptic connectivity is well-stated and accurately reflects the evidence. The paper provides:

1. Clear empirical results showing 54±1.5% brain predictivity score for the Weight Compression (WC) initialization without any training
2. Statistical significance (p<1E-5) compared to baseline Kaiming Normal initialization
3. Appropriate context about biological plausibility and genome capacity constraints
4. Transparent limitations and acknowledgment that this is a proof-of-principle
5. Multiple figures validating the claim, especially Figure 2C showing the 54% score

The review comments don't specifically challenge this particular claim's validity. While they raise concerns about the overall approach, they don't dispute these specific numerical findings.

<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:

Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved "at birth" (i.e. no training at all).

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:If few supervised updates can get model representations fairly close to a fully trained model (Figure 1), how close are the initial representations without any training? In relation to biology and following the introduced framework of treating all consecutive training as developmental learning, these "at-birth" synaptic connections would result from information encoded in the genome as a product of evolution. Due to the genome's capacity bottleneck, it is infeasible to precisely encode every synapse. Primary visual cortex alone contains \(\sim\)1.4E8 neurons per hemisphere (Leuba & Krafts, 1994), \(\sim\)1E3 synapses per neuron, each requiring \(\sim\)37 bits per synapse (Zador, 2019). Thus, without any clever rules, specifying the connections in only one hemisphere of V1 could require up to \(\sim\)5.2E12 bits - orders of magnitude more than the entire genome's 1GB = 8E9 bits (Zador, 2019). Sampling synaptic weights from reasonably compressed distributions on the other hand places little memory requirements on genetic encoding while potentially yielding useful initial weights.

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer.

This model is _not trained at all_ and we only evaluate the goodness of its initial wiring on the suite of Brain-Score benchmarks. Strikingly, we find that even without any training, CORnet-S\({}_{\text{WC}}\) achieves \(54\pm 1.5\%\)of the brain predictivity score relative to a fully-trained model (Figure 2), representing a 12 percent point improvement (\(n=10\) seeds; permutation test \(p<1E-5\)) over the Kaiming Normal initialized model with a score of \(43\pm 1.7\%\).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility). 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.

While improved "at-birth" connectivity can reach 54% of a fully-trained model's score, additional experience dependent updates appear necessary to reach higher predictivities.

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).

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 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. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

We have explored a variety of weight compression methods applied to different layers and evaluate their performance "at birth" without training and when trained with critical training. 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.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner. Evolution also acquired aspects of the visual inputs (with a different strategy) and encoded them in the genome in a lower information regime. For example, we know that there are multiple neuro-developmental mechanisms, such as spontaneous retinal waves and axon guidance cues, that significantly shape the architecture and function of the visual system requiring no (or very little) visual experience ((Huberman et al., 2008)). These mechanisms depend on a relatively small number of proteins encoded in a genome and give rise to a highly complex pattern of at-birth synaptic connectivity representing a very large compression of information.

A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates.

Notably, ImageNet performance of these networks seems to not be predictive of their brain predictivity, since even untrained networks with at-chance ImageNet performance correspond reasonably well to e.g. V1 (Figures 1 and 2).

* **KN** A model initialized by standard _Kaiming Normal_ initialization without training.

* WC A model initialized by our _Weight Compression_ initialization, described in Section 4, without training.

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 2: “At-birth” synaptic connectivity yields reasonable scores in brain predictivity. A Specifying the initial weight distribution: Kaiming Normal (KN, He et al., 2015) samples from a generic Gaussian. Weight Compression (WC) compresses trained weights into low-parameter clustered distributions that weights can be initialized from. B Visualization of WC compressed parameters: Gabor filters for first layer and cluster centers for following layers with kernel size > 3. The cluster centers capture an intuitive variety of kernel types. C “At-birth” representations with WC achieve 54% score in brain predictivity of a fully trained model, with no training at all. Scores after training remain virtually unchanged (shaded bars).

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 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: 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:
I'll assess whether the claim "by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved 'at birth' (i.e. no training at all)" is well-stated or overstated.

Summary:
The evidence strongly supports this claim through:
1. Direct experimental results showing 54±1.5% brain predictivity score for the WC-initialized model without training (Figure 2C)
2. Statistical significance (p<1E-5) over baseline Kaiming Normal initialization (43±1.7%)
3. Clear methodology explanation of weight compression initialization
4. Appropriate acknowledgment of limitations, noting this is likely not how evolution achieved initialization
5. Contextualizing results within biological plausibility discussions

The claim is precise, supported by data, and acknowledges inherent limitations. It accurately reflects the evidence without exaggeration.

<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:

Second, by improving the random distribution of synaptic connectivity, we find that 54% of the brain match can already be achieved "at birth" (i.e. no training at all).

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:If few supervised updates can get model representations fairly close to a fully trained model (Figure 1), how close are the initial representations without any training? In relation to biology and following the introduced framework of treating all consecutive training as developmental learning, these "at-birth" synaptic connections would result from information encoded in the genome as a product of evolution. Due to the genome's capacity bottleneck, it is infeasible to precisely encode every synapse. Primary visual cortex alone contains \(\sim\)1.4E8 neurons per hemisphere (Leuba & Krafts, 1994), \(\sim\)1E3 synapses per neuron, each requiring \(\sim\)37 bits per synapse (Zador, 2019). Thus, without any clever rules, specifying the connections in only one hemisphere of V1 could require up to \(\sim\)5.2E12 bits - orders of magnitude more than the entire genome's 1GB = 8E9 bits (Zador, 2019). Sampling synaptic weights from reasonably compressed distributions on the other hand places little memory requirements on genetic encoding while potentially yielding useful initial weights.

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer.

This model is _not trained at all_ and we only evaluate the goodness of its initial wiring on the suite of Brain-Score benchmarks. Strikingly, we find that even without any training, CORnet-S\({}_{\text{WC}}\) achieves \(54\pm 1.5\%\)of the brain predictivity score relative to a fully-trained model (Figure 2), representing a 12 percent point improvement (\(n=10\) seeds; permutation test \(p<1E-5\)) over the Kaiming Normal initialized model with a score of \(43\pm 1.7\%\).

_Weight Compression_ explores the hypothesis that evolution may have discovered an initialization strategy with improved at-birth representations (relative to current initializations). _WC_ is most likely not how evolution found the at-birth synaptic connections, but shows that with nearly identical capacity, an alternative initialization distribution leads to networks that are more brain-like in their adult state - revealing a new space of possibilities (hypotheses) that should be considered (see Appendix B.4 for more details on biological plausibility). 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.

While improved "at-birth" connectivity can reach 54% of a fully-trained model's score, additional experience dependent updates appear necessary to reach higher predictivities.

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).

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 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. We expect future work in this direction to further close the gap with improved evolutionarily encoded wiring mechanisms and developmental learning rules.

We have explored a variety of weight compression methods applied to different layers and evaluate their performance "at birth" without training and when trained with critical training. 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.

However, independent of _how_ these clusters are determined, WC shows that it is possible to encode certain priors about a system's wiring diagram in a very compressed manner. Evolution also acquired aspects of the visual inputs (with a different strategy) and encoded them in the genome in a lower information regime. For example, we know that there are multiple neuro-developmental mechanisms, such as spontaneous retinal waves and axon guidance cues, that significantly shape the architecture and function of the visual system requiring no (or very little) visual experience ((Huberman et al., 2008)). These mechanisms depend on a relatively small number of proteins encoded in a genome and give rise to a highly complex pattern of at-birth synaptic connectivity representing a very large compression of information.

A model initialized with weight compression achieves (only _WC_) 54% brain predictivity score with 0 supervised synaptic updates.

Notably, ImageNet performance of these networks seems to not be predictive of their brain predictivity, since even untrained networks with at-chance ImageNet performance correspond reasonably well to e.g. V1 (Figures 1 and 2).

* **KN** A model initialized by standard _Kaiming Normal_ initialization without training.

* WC A model initialized by our _Weight Compression_ initialization, described in Section 4, without training.

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 2: “At-birth” synaptic connectivity yields reasonable scores in brain predictivity. A Specifying the initial weight distribution: Kaiming Normal (KN, He et al., 2015) samples from a generic Gaussian. Weight Compression (WC) compresses trained weights into low-parameter clustered distributions that weights can be initialized from. B Visualization of WC compressed parameters: Gabor filters for first layer and cluster centers for following layers with kernel size > 3. The cluster centers capture an intuitive variety of kernel types. C “At-birth” representations with WC achieve 54% score in brain predictivity of a fully trained model, with no training at all. Scores after training remain virtually unchanged (shaded bars).

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 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: 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, the claim that "54% of the brain match can already be achieved 'at birth' (i.e. no training at all)" appears to be well-stated and accurately reflects the evidence. Multiple pieces of evidence support this:

1. Figure 2C explicitly shows WC achieving 54±1.5% brain predictivity score without training
2. The statistical significance is demonstrated (n=10 seeds; permutation test p<1E-5)
3. The comparison to baseline Kaiming Normal initialization (43±1.7%) is clearly stated
4. The review comment confirms this as a "striking finding"
5. The limitations are acknowledged in the paper's discussion

The evidence directly supports the numerical claim made, and the authors appropriately qualify it within the context of initialization strategies and evolutionary possibilities.

<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:

Third, we find that, by training only \(\sim\)5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream.

The evidence to be evaluated is: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).

Concretely relating supervised updates to primate ventral stream development, Seibert (2018) establishes that no more than \(\sim\)4 months - or 10 million seconds - of waking visual experience is needed to reach adult-level primate IT cortex (as assessed by its capability to support adult level object recognition). From this estimate, we can compute how many supervised updates per second different models in Figure 1A would require (assuming those updates are evenly distributed over the 10 million seconds). 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).

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer. To determine the right parameterization, we compress a trained model's weights into clusters which we then sample from ("_Weight Compression, WC_").

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. The final _CT_ ventral stream model is almost completely frozen and only the synapses generating each cortical area's output are trained.

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.

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).

_WC+CT_ maintain 91% of the score in ResNet despite an almost 80% reduction in parameters.

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.

Local learning rules (Lowe et al., 2019; Xiong et al., 2020) might alleviate these concerns and with critical training (Figure 3), it could be sufficient to learn in only a subset of layers. 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.

This training version allows for more training but still reduces the amount of trained parameters by 43% while maintaining 78% of the original score.

Input: Figure 3: Training only critical layers reduces the number of updated synapses while maintaining high brain predictivity. A We could naively reduce the parameters of a fully-trained model by freezing layers from the bottom up, training only the top layers (“Downstream Training DT”; gray box). We instead propose Critical Training (CT) which only trains thin “critical” layers (e.g. downsampling layers, blue box). B CORnet-S circuitry. CT only trains critical layers, leaving the rest frozen. C Naively reducing parameters from standard training (black dot, top right) quickly deteriorates brain predictivity scores (DT, gray line) whereas Critical Training reduces parameters while retaining high scores (blue line, CT). D Like C, but measuring ImageNet score. CT retains nearly half the score with a fraction of parameters.

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 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 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 reviewing the claim and evidence carefully, I find that the claim "by training only ~5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream" is well-supported by multiple pieces of evidence. Figure 3C shows Critical Training (CT) maintaining over 75% brain predictivity while training only 1.4 out of 52.8 million parameters (~2.6%). The text explicitly states "CT maintains over 75% with only 1.4 out of 52.8 million parameters trained." Additional evidence shows that combining CT with other techniques (WC+CT) can maintain up to 91% performance with an 80% reduction in parameters. The claim is conservatively stated and accurately reflects the empirical findings.

<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:

Third, we find that, by training only \(\sim\)5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream.

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: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).

Concretely relating supervised updates to primate ventral stream development, Seibert (2018) establishes that no more than \(\sim\)4 months - or 10 million seconds - of waking visual experience is needed to reach adult-level primate IT cortex (as assessed by its capability to support adult level object recognition). From this estimate, we can compute how many supervised updates per second different models in Figure 1A would require (assuming those updates are evenly distributed over the 10 million seconds). 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).

To test the hypothesis that the genome might already encode more powerful initial representations with synaptic wiring sampled from distributions specified by only few bits, we explored multi-dimensional distributions as a more expressive alternative. These distributions only require a small number of parameters, but unlike current generic initializers, we explicitly specify them for each layer. To determine the right parameterization, we compress a trained model's weights into clusters which we then sample from ("_Weight Compression, WC_").

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. The final _CT_ ventral stream model is almost completely frozen and only the synapses generating each cortical area's output are trained.

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.

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).

_WC+CT_ maintain 91% of the score in ResNet despite an almost 80% reduction in parameters.

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.

Local learning rules (Lowe et al., 2019; Xiong et al., 2020) might alleviate these concerns and with critical training (Figure 3), it could be sufficient to learn in only a subset of layers. 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.

This training version allows for more training but still reduces the amount of trained parameters by 43% while maintaining 78% of the original score.

Input: Figure 3: Training only critical layers reduces the number of updated synapses while maintaining high brain predictivity. A We could naively reduce the parameters of a fully-trained model by freezing layers from the bottom up, training only the top layers (“Downstream Training DT”; gray box). We instead propose Critical Training (CT) which only trains thin “critical” layers (e.g. downsampling layers, blue box). B CORnet-S circuitry. CT only trains critical layers, leaving the rest frozen. C Naively reducing parameters from standard training (black dot, top right) quickly deteriorates brain predictivity scores (DT, gray line) whereas Critical Training reduces parameters while retaining high scores (blue line, CT). D Like C, but measuring ImageNet score. CT retains nearly half the score with a fraction of parameters.

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 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 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 evaluate the claim "by training only ~5% of model synapses, we can still achieve nearly 80% of the match to the ventral stream."

Based on the evidence:
- Figure 3C shows Critical Training (CT) maintains over 75% brain predictivity score while training only 1.4 out of 52.8 million parameters (~2.7% of parameters)
- The evidence directly states "CT maintains over 75% with only 1.4 out of 52.8 million parameters trained"
- Data shown in figures confirms that with minimal parameter training, the model maintains high brain predictivity scores

The claim is well-stated and closely matches the quantitative evidence. If anything, it's slightly conservative since CT achieves 75% score with even fewer parameters (~2.7%) than claimed (5%). The review comments don't dispute this specific claim.

<score>1</score>