Title: BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
Venue: NeurIPS 2024
Accepted: yes
Keywords: diffusion models

fMRI

computational neuroscience

generative AI
Abstract: When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or  more powerful signal extraction from neural recordings. However, in practice, new reconstruction methods could improve performance for at least three other reasons: learning more about the distribution of stimuli, becoming better at reconstructing text or images in general, or exploiting weaknesses in current image and/or text evaluation metrics. Here we disentangle how much of the reconstruction is due to these other factors vs. productively using the neural recordings. We introduce BrainBits, a method that uses a bottleneck to quantify the amount of signal extracted from neural recordings that is actually necessary to reproduce a method's reconstruction fidelity. We find that it takes surprisingly little information from the brain to produce reconstructions with high fidelity. In these cases, it is clear that the priors of the methods' generative models are so powerful that the outputs they produce extrapolate far beyond the neural signal they decode. Given that reconstructing stimuli can be improved independently by either improving signal extraction from the brain or by building more powerful generative models, improving the latter may fool us into thinking we are improving the former. We propose that methods should report a method-specific random baseline, a reconstruction ceiling, and a curve of performance as a function of bottleneck size, with the ultimate goal of using more of the neural recordings.

BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?

Abstract

When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powerful signal extraction from neural recordings. However, in practice, new reconstruction methods could improve performance for at least three other reasons: learning more about the distribution of stimuli, becoming better at reconstructing text or images in general, or exploiting weaknesses in current image and/or text evaluation metrics. Here we disentangle how much of the reconstruction is due to these other factors vs. productively using the neural recordings. We introduce BrainBits, a method that uses a bottleneck to quantify the amount of signal extracted from neural recordings that is actually necessary to reproduce a method's reconstruction fidelity. We find that it takes surprisingly little information from the brain to produce reconstructions with high fidelity. In these cases, it is clear that the priors of the methods' generative models are so powerful that the outputs they produce extrapolate far beyond the neural signal they decode. Given that reconstructing stimuli can be improved independently by either improving signal extraction from the brain or by building more powerful generative models, improving the latter may fool us into thinking we are improving the former. We propose that methods should report a method-specific random baseline, a reconstruction ceiling, and a curve of performance as a function of bottleneck size, with the ultimate goal of using more of the neural recordings.

1 Introduction

Applying powerful generative models to decoding images and text from the brain has become an active area of research with many proposed methods of mapping brain responses to model inputs. A race between publications is driving down the reconstruction error to produce higher fidelity images and text [17, 27, 28]. It could be easy to assume that as the field gets better at reconstructing stimuli, we will simultaneously be getting better at modeling vision and language processing in the brain. We argue that this is not necessarily the case.

There are several reasons why a method might have higher quality reconstructions yet actually require the same or less signal from the brain. For example, a much larger model can learn a stronger prior over the space of images and text, so even if it were given less information from the brain, it might produce better reconstructions. In particular, a generative model might become more fine-tuned toward the distribution of images and text that are used in standard datasets. This is problematic because so few open neuroscience datasets exist, and even fewer at the scale that would

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enable this research. It is easy to inadvertently overfit a model and over-optimize for the particular biases of the standard benchmarks, never mind explicitly tuning the parameters. Finally, there is the separate, confounding issue of how to best evaluate the reconstructions. Even the best intentioned modeling approaches on novel data can run afoul of the extremely limited image and text evaluation methods that we have today. Later in the manuscript, we demonstrate the importance of appropriately calibrating and understanding the shortcomings of these methods.

Given that better decoding need not explain more of the brain, we create the first metric to measure this: BrainBits. BrainBits measures how reconstruction performance varies as a function of an information bottleneck. We learn linear mappings from the neural recordings to a smaller-dimensional space, optimizing the reconstruction objective of each method.

The result of running BrainBits on state-of-the-art reconstruction methods is striking: a bottleneck that is a small percentage of the full brain data size is sufficient to guide the generative models towards images of seemingly high fidelity. For fMRI, the entire brain volume often has on the order of 100K total voxels and about 14K voxels in the visual area, which is what the methods we report here use. We find that a reduction through a bottleneck of only 30 to 50 dimensions provides the vast majority of the performance of a reconstruction method depending on the metric.

BrainBits enables us to disentangle the contributions of the generative model's prior and the signal extracted from neural recordings when evaluating models. This is critical to soundly using stimuli reconstruction as a tool for making neuroscientific progress. We would like reconstruction methods that explain more of the brain rather than merely relying on better priors. In particular, we propose three components: to produce a method-specific random baseline that uses no neural recordings, to compute a method-specific reconstruction ceiling, and to compute reconstruction performance as a function of the bottleneck size.

Ideally, models would achieve near full performance only with large bottlenecks, showing that they are relying on the neural signal for their performance. Models that have high random baselines and then exploit only a few bits of information from the brain in order to achieve their, at first glance, impressive performance, are doing so largely from their prior. And while all the examples of BrainBits we provide here use fMRI, the method can be applied to any neural recording modality. BrainBits also provides interpretability, by showing which brain areas contribute to decoding as a function of the bottleneck size and making the activity of these regions available for probing via decoder.

Our contributions are:

BrainBits, a novel method that uncovers that models use very little of the neural signal to achieve their performance.

An application of BrainBits to three recent stimulus reconstruction methods, two for vision and one for language decoding.

An investigation of which brain areas are most relied upon for reconstruction, made possible by our interpretable linear bottleneck design

An analysis of features which are available in the bottlenecks, how quickly those features saturate, and which features can contribute to performance when using more of the brain recordings.

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2 Related work

Several recent works have focused on predicting the latent features of deep, pretrained, generative models from fMRI data in order to reconstruct corresponding stimuli. Han et al. [9] introduced a technique that projects fMRI recordings to the bottleneck layer of an image-pretrained variational autoencoder [12] and reconstructs the stimulus via the decoder network. Similarly, [24] learns projections to the input space of the generator network belonging to a pretrained generative adversarial network [8]. Given the success of models such as Dall-E [22], more recent work has focused on learning mappings to the latent space of large diffusion models [2, 23, 25, 26, 30]. Furthermore, approaches such as [5] and [16] leverage recent generative models for the multimodal case, decoding both images and captions.

This family of methods has been facilitated by the growth of fMRI datasets containing pairs of stimuli and recorded neural data, the current largest of which is the publicly available Natural Scenes Dataset (NSD) [1]. The Natural Scenes Dataset contains fMRI recordings of multiple subjects cumulatively viewing tens of thousands of samples from the Microsoft CoCo dataset [14]. Given its increased size relative to previous similar datasets [11], it presents more potential for data-driven neural decoding. As a result, it is a popular choice for many recent methods [6, 29], and we select it for our analysis.

Numerous metrics for measuring reconstruction fidelity have been proposed. In the visual domain these include pixel correlation, SSIM, CLIP similarity, and DreamSim [7]. For language these include word error rate (WER), BLEU, METEOR, and BERTScore [32]. None take into account the prior knowledge that modern models have built into them.

3 Approach

Given a reconstruction method f that maps brain data X to images Y , we seek to determine how much the quality of the images ˆ Y = f ( X ) depends on the brain signal. We do this by placing restrictions on information flow, and then examining the resulting reconstructions. This restriction is operationalized by a bottleneck mapping g L that compresses the brain data to a vector of smaller dimension. Specifically, let Y = { y i } where y i is an individual original image corresponding to the brain data response x i . Then, as stated, our aim is to find the best reconstruction achievable for a given restriction L , where reconstruction quality is scored by some metric s ( · , · ) :

This allows us to produce a curve of reconstruction quality as a function of L . We restrict our attention to linear transformations, g L , to find the interpretable mappings that yield the best reconstruction quality.

Model performance lies in a range between model-specific randomly generated images and a modelspecific ceiling. To compute a model's random performance, we run bottleneck training and reconstruction, substituting the original brain data with synthetic data generated according to N (0 , 1) . The purpose of this baseline is to obtain a set of images that reflect the generative prior of the model without any input from the brain.

To compute a ceiling on the image reconstructions, BrainDiffuser and Takagi et al 2023, we run the complete original reconstruction pipeline, but substitute the ground truth image latents instead of using the latents as predicted from the brain data. We do this to obtain the reconstructions as produced by the generative models, had the target latents been predicted perfectly. No analogous ceiling procedure exists for the language reconstruction method, Tang et al 2023, which is based on scoring word predictions via an encoder model.

4 Experiments

We adapt three state-of-the-art methods BrainDiffusers [17], Takagi & Nishimoto [27], and Tang et al. [28] to compute BrainBits; the first two are vision reconstruction methods and the last is a language reconstruction approach. In each case, BrainBits is computed in the same way, but it is computed jointly with the optimization for each method. This means that BrainBits is not a simple pre- or post-processing step or function call, it must be integrated into the method, which at times can

require updates to the optimizer being used, effectively calling for a port from standard regression libraries to a deep learning framework. We optimize reconstruction for varying bottleneck sizes, and evaluate the resulting reconstructions on the standard metrics, including the ones used by the authors, as well as a new metric that has since been proposed, DreamSim, in order to show that BrainBits produces the same message regardless of the metric chosen or modality of the reconstruction. Below, we describe each method and how BrainBits was computed.

4.1 BrainDiffusers

The original BrainDiffusers uses fMRI data from the Natural Scenes Dataset [1] (see Section 2), in which the brain volumes have been masked specifically to only include the visual areas ( µ = 13930 voxels). In the BrainDiffusers approach, regressions are fitted to map the fMRI data to latent representations of the corresponding images, namely VDVAE [3] and CLIP [21] embeddings of the images. An additional regression is fitted to predict CLIP embeddings of the corresponding COCO captions. The predicted VDVAE latent is used to produce a coarse version of the image. Then, the predicted VDVAE image, the predicted CLIP-text embedding, and the predicted CLIP-vision embeddings are given as input to versatile-diffusion [31], which produces the final predicted image. Complete details are given in the original paper [17].

Our approach to bottlenecking BrainDiffusers is shown in Figure 1. For a given bottleneck size L , we learn a mapping g L from the fMRI input to a L -dimensional vector. From this vector, we learn a mapping to the image and text embedding targets. See (Appendix A.1: Training Bottlenecks) for training details.

4.2 Takagi & Nishimoto

This approach is broadly similar to BrainDiffuser in that the same dataset is used, and the same approach of mapping fMRI signal to embedding targets is used. However, there are a few key differences. First, separate mappings are learned for different parts of the brain. The early visual area is mapped to the latent representation space of a V AE, and the outputs of this mapping are passed through the VAE's decoder to produce a course reconstruction of the image stimuli.

Another mapping is fit from a concatenation of the early, ventral, midventral, midlateral, lateral, and parietal regions to BLIP [13] embeddings of the image stimuli. Predictions of these embeddings are decoded into text that is then used along with the coarse image reconstruction to guide image generation with Stable Diffusion [19]. Since two separate mappings are learned, we insert two bottlenecks trained separately and keep their size the same.

4.3 Tang et al.

We show how our benchmark framework can be extended to other modalities by also making a study of an fMRI-to-language reconstruction approach. In the original approach, an encoding model is fit to map GPT [20] embeddings to brain activity. Then, at inference time, the decoder takes the brain activity as input and uses GPT to auto-regressively propose candidate predictions for the next word. The word with the highest likelihood, as computed by the encoding model, is accepted as the next word in the sequence. We follow the original method in separating the training and decoding steps. We insert the information bottleneck by first learning a mapping from brain activity to a compressed vector that is mapped to a GPT text embedding. Instead of using the raw brain activity as input, we use the resulting bottleneck representations, and run the rest of the pipeline without modification. This method has the fewest changes and simplest adaptation to BrainBits. Tang et al. reconstruct language from perceived speech, imagined speech, perceived movie, and perceived multi-speaker speech (see [28]), and we report performance averaged across these tasks. Additional details are given in the appendix.

5 Results

We use standard metrics for image [18] and text [28] construction. When reporting CLIP score, we compute the absolute cosine similarity between images rather than an average image rank sorted by CLIP similarity; this makes results comparable to CLIPScore [10] modulo a constant scaling factor.

Effective dim. of vision bottlenecks

Effective dim. of language bottlenecks

We also consider similarity as measured by DreamSim [7]. Including additional metrics demonstrates that the BrainBits message is independent of the metric used: models use relatively little of the neural recordings to achieve the vast majority of their performance.

As described above, we insert information bottlenecks into two vision and one language reconstruction method and vary the bottleneck size while optimizing on the original objectives. Having learned these bottleneck mappings, we then investigate the resulting reconstructions. We can also study the representations learned by the bottleneck mappings themselves, and we compute the effective dimensionality of the bottleneck representations, the types of information decodable from the representations, and the weight that each bottleneck mapping places on different regions of the brain.

How much information is needed to reconstruct an image or text? Qualitative results are shown in Figure 2 while quantitative results are shown in Figure 3. For BrainDiffuser a bottleneck of size 50 achieves 75%, 95%, 100%, and 89% of the original performance as measured by DreamSim, CLIP cosine similarity, SSIM, and pixel correlation. This is a reduction of a factor of approximately 300, given that the method starts with approximately 14,000 voxels, to achieve most to all of the reconstruction performance. A similar trend holds for the Takagi & Nishimoto method.

Chance performance is high for both visual reconstruction methods because the models learn strong priors over the data. More surprising is the relatively low ceiling that both methods have. Even inserting the best possible latents, methods are very limited in their ability to maximize these metrics.

For language reconstruction, the original uses whole-brain fMRI; approximately 90,000 floating point numbers. A bottleneck of size 1000 is sufficient to recover 50% of the performance, averaged across subjects, as measured by BERT. This bottleneck achieves 26% and 20% of the original performance, as measured by BLEU and METEOR respectively, and the WER is comparable. Chance performance is similarly surprisingly good, with a WER of approximately 1.1, BLEU of approximately 0.18, METOR of approximately 0.14, and BERT score of approximately 0.79; these vary slightly by subject. As discussed in the conclusion, a limitation of BrainBits is that the bottleneck size may be exaggerated in cases like these where performance is relatively close to chance.

How effectively are the bottlenecks used?

The bottleneck dimension is an upper bound on the information being extracted from the brain. A 50-dimensional bottleneck may contain a much lower dimensional signal; see Figure 4. We use the number of principal components needed to explain at least 95% of the variance, as a measure of the effective dimensionality of the bottlenecked representations [4]. For BrainDiffuser, a bottleneck of size 50 has an effective dimension of about 16, averaged across subjects. For comparison, the average effective dim of the fMRI inputs is 2 , 257 (see supplementary Figure 16). Finally, language models have a much smaller effective dimension: a bottleneck of size 1,000 has roughly an effective dimensionality of 5 to 20 depending on the subject.

What regions of the brain matter most?

We plot the weights of the bottleneck mapping back onto the brain for BrainDiffuser; see Figure 5. The vast majority of the weight is assigned to voxels in the periphery of the early visual cortex. As the model has access to larger bottlenecks, it continues to assign more importance to these areas rather than including new areas. One would ideally like models to expand the brain areas that they use effectively as the bottleneck size increases.

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What do the bottlenecks contain?

We attempt to decode visual features from bottlenecks of different sizes for BrainDiffuser; see Figure 6. Lower-level features like contrast, brightness, and edge energy are available very quickly at low bottleneck sizes and are also largely exhausted fairly quickly. High-level features like object class may be driving performance as the bottleneck size increases.

6 Limitations

BrainBits has a number of limitations. It requires rerunning the decoding process several times. This can be expensive depending on the method. It is also not plug-and-play, since it must be optimized jointly with the reconstruction method. While a simple fixed compression scheme such as PCA can be used for a rough estimate, jointly optimizing the bottleneck and the reconstruction method can be a more difficult optimization problem requiring some manual attention. Depending on the method, BrainBits may be inserted at different points, for example, a method may have several steps that extract information from the brain. All of this prevents BrainBits from being a simple library call. Models must be adapted to compute BrainBits instead, although this adaptation is generally simple.

The resolution of BrainBits depends in part on sweeping the bottleneck size, but it also depends on precisely how that bottleneck is computed. We only consider a linear bottleneck to avoid adding meaningful computations to the model. One could also consider methods that employ vector quantization, which we intend to do in the future. The linear approach we take here has difficulty training with bottleneck sizes that are smaller than one float. A vector quantization method would likely work better for such small bottlenecks. Although, small bottlenecks are perhaps not that interesting given that the goal is to explain more of the brain.

In general, current image and text metrics are limited and make understanding reconstruction performance difficult. This situation is made worse by the fact that random performance can be high when models have strong priors. And even more so when those priors allow models to perform well with little added information from the brain. Computing these quantities is critical for understanding where we are in terms of absolute performance and explaining representations in the brain.

Computational requirement and code availability All mappings and image generations were computed on two Nvidia Titan RTXs over the course of a week. Code is available at https: //github.com/czlwang/BrainBits .

7 Discussion and Conclusion

The fidelity of a reconstruction depends on the priors of the generative model being used and the amount of useful information extracted from the brain. Through visual inspection or from reconstruction metrics, one can easily be fooled into thinking that because the results appear high fidelity, they must leverage large amounts of brain signal to recover such details. BrainBits reveals otherwise. Relatively little of the neural recordings are used, and many of the produced details can be attributed to the generative prior of the diffusion model. To get a clearer understanding of how to evaluate these reconstructions, we propose a realistic random baseline based on the generative prior, as well as a reconstruction ceiling based on what it is possible to decode with perfect latent prediction. The random baseline achieved by generative models is far higher that most would expect and the reconstruction ceiling on some metrics is far lower than expected.

The priors create a much narrower range of performance than one might expect. For example, BrainDiffusers has an effective range of approximately 0.15 to 0.75 DreamSim, 0.48 to 0.88 CLIP, 0.15 to 0.3 SSIM, and 0.05 to 0.7 Pixel Correlation. These ranges depend entirely on the models employed, and are a reflection of the priors of the models. Reporting at least the floor is important for contextualizing results; this is not reported in most prior work.

Bottlenecks appear to exploit information in order, from low-level features, brightness and contrast, to mid-level features, edge energy, to high level features, object class. Vision models focus on the same region of the brain regardless of bottleneck: early visual cortex. Higher-level features should become more disentangled and easier to take advantage of in later parts of the visual system. This does not appear to be useful to current models.

Goodhart's law states: 'When a measure becomes a target, it ceases to be a good measure'. With the advent of high resolution reconstructed image stimuli, we as a field may be tricked into believing that we have become better at understanding visual processing in the brain. We may be tempted into further optimizing the quality of the reconstructed images in this service. But this is an inappropriate target if neuroscientific insight is our goal. We emphasize the importance of a BrainBits analysis for all neuroscientific studies of stimulus reconstruction to quantify the true contribution of the brain to reconstructions.

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A Appendix

A.1 Training bottlenecks

Case study: BrainDiffuers The original BrainDiffusers method learns separate mappings to the VDVAE, CLIP-text and CLIP-vision latents. We predict all embeddings simultaneously. We use an MSE objective, and weight the loss on the predicted VDVAE, CLIP-text, and CLIP-vision targets with [1 , 2 , 4] respectively. We train our network with a batch size b = 128 , an AdamW optimizer [15], a weight decay of wd = 0 . 1 and a learning rate of lr = 0 . 01 . We train for 100 epochs and use the weights with the best validation loss at test time.

Case study: Tang et al. 2023 Tang et al. use the fMRI data to both predict the timing and the content of language. Because we are interested in the semantic decoding, we use the original models of timing, and use the bottleneck representations to predict the words itself. We train our bottleneck with a batch size of b = 512 , learning rate lr = 5 e -4 , and the AdamW optimizer. We train for 100 epochs and use the weights with the best validation loss at test time. For evaluation, we report the average of scores across windows, (see [28] for details).

Takagi et al 2023 Takagi et al. learn mappings from the early visual area to V AE latents of the stimuli [27]. They also learn mappings from the early, ventral, midventral, midlateral, laterial, and parietal regions to the latents of a BLIP image encoder [13]. We learn a separate bottleneck for each mapping with the MSE objective. We use the AdamW optimizer [15] and perform hyperparameter search over learning rates in [1 e -5 , 1 e -4 , 1 e -3 , 1 e -2] and weight decays in [1 e -3 , 1 e -2 , 1 e -1 , 5 e -1] .

A.2 brain-diffuser bottlenecks

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Iruth

A.3 Projecting bottlenecks onto the brain

(a) Regression weight plot for subject 1

(b) Regression weight plot for subject 2

(c) Regression weight plot for subject 5

0.00

0.250.500.751.00

Model weight le-6

SRSC

RSC

OPA

OPA

EBA

EBA

OFA

EarlyVis

OFA

OFA

EarlyVis

FBA-1

UCEFASS

FBA-RT

FBA22

PPA

PPA

aTL-face

aTL-faces-face-selective region in anterior portion of temporal lobe

EarlyVis-Earlyvisualcortex

EBA-extrastriate body area

FBA-1- posterior section of fusiformbodyarea

FBA-2-anterior section of fusiform body area

FFA-1-posterior section of fusiform face area

FFA-2-anterior section of fusiformface area

OFA -occipital face area

OPA-occipitalplace area

PPA- parahippocampal place area

RsC-retrospenial cortex

A.4 Text reconstruction examples

Table 2: Expanded full text for the same stimulus as shown in Figure 2c and Table 2, but for subject 2

Table 3: Expanded full text for the same stimulus as shown in Figure 2c and Table 3, but for subject 3

A.5 Two-Way Evaluation

A.6 Takagi Image Grid with BrainDiffuser Image Indices

Takagi

Ceiling

Truth

A.7 Effective dimensionality of the fMRI inputs

(a) brain-diffuser.

(b) Takagi et al.

Takagi fMRI Dimensionality

Subject 1

Subject2

Explained

100

100

75

75

1

1

50

50

25

25

0

-4632/26688

0

- 5092 / 24395

0

5000 10000150002000025000

5000 10000 15000 20000 25000

Subject 5

Subject7

Percentage

100

100

75

75

50

50

25

25

===

4039 / 22649

0

I

= ==

4177/21064

0

500010000 1500020000

0

5000

10000

1500020000

Principal Components

===

95% Index

BrainDiffuser fMRI Dimensionality

Subject 1

Subject 2

Explained

100

100

75

75

50

50

25

25

-2709 /15724

-2344/ 14278

0

0

5000

10000

15000

5000

10000

Subject 5

Subject 7

Percentage

100

100

75

75

50

50

25

25

0

-- 1795 / 13039

1

---2178 /12682

0

2500 5000 7500 1000012500

0

2500 5000 7500 1000012500

Principal Components

===

95% Index

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: Our claims in the abstract are supported by careful bottlnecking of multiple reconstruction methods as described and analysis of their reconstrucion performance.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: See Limitations section in section 6 Discussion and conclusion

3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA] .

Justification: A rough theory framing is given, but the findings are empirical.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: The purpose of our paper is to describe a general purpose approach that other researchers can apply to their own stimulus reconstruction methods. For this work, we describe how to implement our approach on the three case studies mentioned in Section 4.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: The paper uses the publicly available Natural Scenes Dataset [1] and the publicly available data from [28]. We release our code, which builds on the publicly available code in [17].

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: This information is included in Section 4 and Appendix A.1.

7. Experiment Statistical Significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: Where multiple subjects are used, results are reported for all subjects. On plots that give reconstruction results, standard error bars are shown to reflect variance across samples.

8. Experiments Compute Resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: See discussion/conclusion. All mappings and image generations were computed on two Nvidia Titan RTXs with 24GB of GPU RAM over the course of a week.

9. Code Of Ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We adhere to the neurips code of conduct

10. Broader Impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA] .

Justification: The paper discusses generative model priors, but in the context of a narrow neuroscience application with no salient societal impact.

11. Safeguards

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

Answer: [NA]

Justification: We use publicly availible data and pretrained models. We train linear mapping networks for evaluation which poses no risk.

12. Licenses for existing assets

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

Answer: [Yes]

Justification: We use publicly availible data published for academic use. We also use pretrained generative models published under MIT opensource licenses.

13. New Assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: Code will be released after acceptance with MIT license.

14. Crowdsourcing and Research with Human Subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: We do not ccollect new data from subjects.

15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA] .

Justification: We do not collect new data.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to reduce the dimensionality of brain signals using linear layers to determine the minimal dimension required to preserve most of the reconstruction quality. The study involves experiments with three existing methods: two for brain-to-image reconstruction and one for brain-to-language reconstruction.\n\nSTRENGTHS:\n- The paper is easy to follow.\n- The paper aims to explore the extent of information necessary for brain decoding.\n\nWEAKNESSES:\n1. The paper suggests that better reconstruction may be achieved with less brain signal input, yet all brain signals are utilized by the reconstruction models. While compressing signals to lower dimensions is possible, it does not guarantee that fewer signals can be used as input.\n2. Only diffusion-based models are employed for decoding images. The relatively favorable results may stem from well-trained diffusion models capable of operating with minimal semantics. The discrepancy in bottleneck dimensions between image and text decoding (50 vs. 1000) could support this. In reality, diffusion models exhibit significant randomness in image generation, enabling the selection of images resembling ground truth stimuli from various generation runs. Consequently, drawing general conclusions based on the use of diffusion-based models may be challenging.\n3. The performance of brain-to-image generation remains unsatisfactory. While subject1 shows promising results in some instances, the images generated using whole brain signals lack the structure and detail seen in the stimulus images. Furthermore, in additional poor cases not illustrated in this paper, the decoding results may be even more unsatisfactory. Therefore, achieving good image decoding results with fewer signals may be challenging.\n4. The number of samples for different subjects is limited. Literature suggests significant performance variations in image generation among subjects, with some individuals exhibiting poor reconstruction outcomes.\n5. The paper asserts that the method can be adapted to various neural recording modalities; however, differences in temporal and spatial resolution as well as signal characteristics among recorded signals pose uncertainties about BrainBits' adaptability. Notably, no experiments support this claim.\n6. The second paragraph in the introduction appears somewhat contradictory.  It mentions that higher quality reconstruction may require same or less signal from the brain, while also acknowledging the scarcity of open neuroscience datasets, especially those of sufficient scale to support this type of research.\n7. The brain regions depicted in Figure 5 are not clearly labeled, making it difficult for readers to fully understand.\n8. The effective dimensionality in Figure 4 lacks explicit clarification.\n9. Text occlusion is apparent in both Figure 3 and Figure 11, affecting the clarity.\n\nQUESTIONS:\n1. Given that lines 4-7 of the abstract lack a clear connection with the following sentences, and the paper does not tackle issues related to understanding stimulus distribution and enhancing text or image reconstruction, what meaning does it actually convey?\n2. Why were two fMRI plus diffusion models chosen for image decoding instead of experimenting with other signal types or generative models?\n3. Do different subjects activate different voxels for the same stimulus image? Or do different voxels get activated for different stimulus images within a subject? If so, utilizing a fixed subset of voxel recordings as input becomes challenging due to the uncertainty of which voxels will be utilized later.\n4. Can BrainBits operate with fewer brain signals as input? If not, what specific contributions does this paper offer to the community despite this limitation?\n\nLIMITATIONS:\nThere is no potential negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce BrainBits, an information-bottleneck pipeline that measures reconstruction performances from brain signals (fMRI datasets) as a function of bottleneck size by (linearly) projecting the data into a lower dimensional space of controlled dimensionality. The rationale is to disentangle the contributions to improved reconstruction quality seen in recent  works into (1) improvements in actual decoding (better use of neural information - the actual goal of the decoding techniques) and (2) general improvements in generative models (more powerful architectures with better priors). Indeed the authors reveal that modern improvements are cause by the latter, with recent models making very little use of neural signals. Furthermore, the BrainBits pipeline enables inspection of which brain areas are mostly relied upon. The technique has broad applicability to both vision and language decoding and different signal modalities (however, only fMRI is reported).\n\nSTRENGTHS:\n*a. Originality:* The work provides a novel method to rigorously characterize decoding performances which surpasses previous metrics on critical aspect and is resilient to simply scaling decoder complexity.\n*b. Quality:* The work quality is generally high, with enough experimental result to support the author's claim.\n*c. Clarity:* The work is well written and properly organized.\n*d. Significance:* The results presented are of high significance for the brain-decoding community. Recent years have witnessed a wealth of novel contributions, each time showcasing evermore detailed brain reconstructions, implying significant advancements in our ability to decode neural signals. This work offers a much needed warning that we need to guard against misleading metric improvements granted by more powerful generative models and provides the tools to do so. The proposed pipeline is flexible and easy to build upon, offering a valuable contribution to the community at large.\n\nWEAKNESSES:\n- Figure 3c is hardly readable. Why are the axes y-scale range in [0, 1] (which then requires smaller inset to actually inspect the data)? Can't the range by set according to data dynamic range?\n- Regarding the analysis on brain regions (Figure 5). The authors claim that the BrainDiffuser model \"*As the bottleneck size goes up models exploit those original areas but do not meaningfully expand to new areas*\". By looking at the (small) image, however, bottleneck size 50 seems to have far fewer \"silent voxels\" (dark purple) than bottleneck 1 or 5 for example. This appears to hold also for other examples presented in Figure 11 in the Appendix (Note that Figure 11 has cut-out subplot titles that are illegible). Why do authors claim that the measured expansion is not \"meaningful\"? What would a meaningful expansion look like?\n\nQUESTIONS:\n- What are the implications of neural coding redundancy on the analysis presented in Fig. 4 where it is shown that a small fraction of the bottleneck dimension are effectively used by the models? Could it be that indeed the underlying neural code is highly redundant hence the model can make effective use of neural signals by using a much lower dimensionality?\n- Authors propose that each method should report a reconstruction ceiling. However it appears that such ceiling is not always clear how to measure. For example authors say: \"*No analogous ceiling procedure exists for the language reconstruction method, Tang et al 2023, [...]*\".  It is my understanding that authors do not offer a general procedure to deal with this problem. If that's correct, how would they suggest to approach this problem in general?\n\nLIMITATIONS:\nThe authors have adequately addressed the limitations of their study by presenting a dedicated section (6) with extensive discussion.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a method called BrainBits which aims at answering if the progress of the fMRI-to-Image/Text field of research comes from a better signal extraction from the brain or from other sources such as having better generative models or exploiting bad metrics. Their method introduces a bottleneck between the fmri data and various fMRI-to-Image/Text methods. Their method is thus directly applicable to basically every reconstruction method, and allows to change the dimensionality of the bottleneck and see whether one can obtain a substantial percentage of the original performance even with the bottleneck.\n\nSTRENGTHS:\n- The questions asked by the authors is very important to the field. Much progress has been made recently, but the causes of that progress remain unclear. Everyone would love the cause to be better brain signal extraction, and thus better understanding of the brain activity by the model. By providing a way to establish whether it is the case or not, the authors tackle a crucial issue. It is a known fact that the metrics used in the field lack a way of knowing how the brain data has been exploited. \n\n- The results obtained by the authors are surprising: in most cases, even with a narrow bottleneck, we can obtain a substantial percentage of the performance of the original model. \n\n- The paper is well written and easy to follow. The experiments are clear.\n\nWEAKNESSES:\n- The main weakness is that the bottleneck introduced in the paper does not definitely answer how much of the brain signal has been exploited in the process. Indeed, the MLP projecting the fMRI data to the bottleneck actually learns to identify the most important features within the brain data, in order to obtain the best reconstruction. Much like a VAE, it learns to compress information as efficiently as possible in order to have the best performance possible. Thus, the compression ratio, even if it is generally very high in the authors' results, is also a result of the best effort of the MLP to identify the best features. This crucial point kind of defeats the point of the paper: isolating how much of the brain signal was extracted and used. However, given the size of the compression ratio (300 in the case of BrainDiffusers for instance), I still believe that the authors rightfully identified that not all of the brain signal is used, and that their research is on the right track. However this point could be further improved and disentangled.\n\nQUESTIONS:\nPlease answer to the main weakness I have identified. Any convincing clarification would result in a improved score of my review.\n\nLIMITATIONS:\nI would add the limitation identified in the weakness part, if it still stands after the rebuttal.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents a method called BrainBits that aims to assess the extent to which generative image reconstruction based on fMRI data is based on the neural data itself, versus some spurious contribution of the reconstruction model itself (e.g., a stronger prior over natural images, or overfitting to the distribution of images used in benchmarks). The method involves introducing a bottleneck of varying size, and assessing how reconstruction performances varies based on the size of the bottleneck. They apply their method to two image reconstruction tasks and one language reconstruction task. They find that performance plateaus at a surprisingly small bottleneck size, and use this finding to argue that neural stimulus reconstruction approaches are only using a fraction of the information available in the neural data, such that we should worry that recent improvements in reconstruction are due to contributions from models, rather than more effective extraction of information from the brain.\n\nSTRENGTHS:\nThe motivation of the paper is timely, sound, and convincing: we need to interrogate the possible sources of improved stimulus reconstruction performance, and we need ways to assess the contribution of the model versus the neural data itself. The authors argue this point clearly, and I could imagine this paper playing a useful role in raising awareness of this problem in the field, and making a first stab at addressing this issue. \n\nOn a methods level, the paper appears to be sound: including the random performance and reconstruction ceiling provides helpful context, and the analyses linking the bottleneck activations to brain topography and decodable features were illuminating. The figures were well-chosen and clearly presented. The analyses support the case that reconstruction performance plateaus at a small number of dimensions (although I think the authors draw inferences from this that aren't warranted; see weaknesses section).\n\nWEAKNESSES:\nWhile I found the motivation for the paper to be convincing, and I believe the authors effectively make the case that a relatively small number of dimensions is sufficient to achieve maximum reconstruction performance, I think the authors' argument ignores an important piece of the puzzle: what is the effective dimensionality of the actual neural activations? For the sake of argument, suppose that the dimensionality of the neural responses (over the space of stimuli sampled) is 50: then, the fact that reconstruction plateaus with a bottleneck of size 50 is not due to the method using a small proportion of the underlying information available in the neural signal, but rather due to the fact that the neural activity patterns are themselves low dimensional (e.g., due to correlations in the underlying neuronal firing patterns, or the fact that the BOLD signal in each voxel reflects the aggregate activity of many neurons). Without addressing this issue, I don't think the authors' conclusions follow from their results. \n\nThe writing style sometimes borders on overly informal (\"Although, small bottlenecks are perhaps not that\ninteresting given that the goal is to explain more of the brain\"), though I believe this is easily fixed with further edits.\n\nQUESTIONS:\n1. How can we know that the plateau in performance at a small bottleneck size reflects the model ignoring usable information in the neural measurements, versus exhausting the usable information available in the neural measurements? As mentioned in the weaknesses section, this is hard to assess without measuring the dimensionality of the neural activations. \n\n2. Under the hypothesis that the prior in generative models is strongly contributing to reconstruction performance, it is odd to me that higher-level visual areas don't seem to be used by the reconstruction pipeline, even at higher bottleneck sizes: given that their activation is highly informative regarding object category, and given that these models presumably have a strong prior for how objects tend to look, I wouldn't have predicted this. It would be interesting (though perhaps too much for the scope of this paper) to restrict the pipeline to particular sectors of the visual system, and examine how this affects performance.\n\nLIMITATIONS:\nThere are no negative societal impacts that I can think of, and the limitations section provides helpful context regarding the practical application of their method.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-26"}