BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?
Authors: David Mayo
Christopher Wang
Asa Harbin
Abdulrahman Alabdulkareem
Albert Eaton Shaw
Boris Katz
Andrei Barbu
Venue: NeurIPS 2024 poster (2024)
Keywords: diffusion models
fMRI
computational neuroscience
generative AI
Primary area: neuroscience_and_cognitive_science
TL;DR: Image stimuli can be reconstructed from the brain without actually using a lot of the brain signal

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?
David Mayo
Christopher Wang""
Asa H
Abdulrahman Alabdulkareem'
Albert Shaw
Boris Katz!
Andrei Barbu'
MIT CSAIL, CBMM
MIT Lincoln Laboratory
Google DeepMind
Abstract
When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation due to an improved understanding of the brain 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 current image and/or text evaluation metrics. Here disentangle how much of the reconstruction due to these other factors vs. productively using the neural recordings. We introduce BrainBits, method that uses bottleneck to quantify the amount of signal extracted from neural recordings that is actually necessary reproduce method's reconstruction fidelity. We find that it takes surprisingly little information from the brain to produce reconstructions with high fidelity. In these cases, clear that the priors of the methods' generative models are 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 into thinking we are improving the former. We propose that methods should report a method-speciff random baseline, reconstruction ceiling, and curve of performance as function of bottleneck size, with the ultimate goal of using more of the neural recordings.
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 model inputs. race between publications is driving down the reconstruction eтror to produce higher fidelity images and text [17, 27, 28]. It could be easy assume that the field gets better at reconstructing stimuli, we will simultancously be getting better at modeling vision and language processing in the brain. We argue that this
necessarily the case.
There are several reasons why a method might have higher quality reconstructions yet actually require the same less signal from the brain. For example, a much larger model can learn 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, generative model might become more tine-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
"qual contribution. Code available https: tttbttttttttt/ttt///////tt/egttt www.////wwa//////// Bttelllwwn//Baainiiin Brainniit.... Correspondence DM and CW at (dmayo2, czw) edu and AH asaharbingll mmi. edu.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Intermediate reconstruction
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Figure 1: BrainBits bottlenecking framework as applied to BrainDiffuser. The goal of image reconstruction to generate an image based brain signal. The brain signal is mapped a hidden vector (gold) by a compression mapping Ir., which is then used to predict VDVAE, CLIP-text, and CLIP-vision latents via mapping Jr. As in [18], these latents are used to produce the final reconstruction. In our studies, we restrict the information available from the brain by varying the dimension of the hidden vector.
enable this research. It is easy inadvertently overfit 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, 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 smaller-dimensionall space, optimizing the reconstruction objective of each method.
The result of rumning BrainBits state-of-the-ar reconstruction methods is striking: bottleneck that is small percentage of the full brain data size 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 the visual area, which is what the methods we report here use. We find that reduction through 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: produce method-specific random baseline that uses 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 few bits of information from the brain 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 decoding function of the bottleneck size and making the activity these regions available for probing via decoder.
Our contributions are:
1. BrainBits, a novel method that uncovers that models very little of the neural signal to achieve their performance.
2. An application of BrainBits three recent stimulus reconstruction methods, two for vision and one for language decoding.
3. An investigation of which brain areas most relied upon for reconstruction, made possible
by our interpretable linear bottleneck design
4. An analysis of features which 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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we both lived in different countries she moved out of state to college to study abroad 100
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the same university we had just moved from the us to a different state she had a degree in psychology and a job that i not one for but...
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we' re both from up north we' 're both kind of newish to the neighborhood this is in florida both went college not great colleges but we graduated and...
(c) Tang et al. 2023
Figure 2: High quality stimuli can be reconstructed from fraction of the data. Shown here are images and text reconstructed for several bottleneck sizes using our BrainBits approach. Images and text shown for subject I for all three methods. Examples where the original methods could reasonably reconstruct the stimuli were chosen; the same images for both visual methods are shown in the appendix. As the bottleneck dimension increases, the accuracy of the reconstruction increases.
Although there are differences between the full and bottlenecked (d = 50) results, the reconstructions are surprisingly comparable, despite the fact that the full reconstruction methods have 14, 000 voxels available to them. Text reconstructions are harder to evaluate in this qualitative manner, later we present a quantitative evaluation.
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(c) Tang et al. 2023 with insets zooming in for clarity
Figure 3: Quantifying the fraction of the data needed to reconstruct stimuli. While different metrics present slightly different pictures of model performance, most performance is reached by about 20 32-bit floating point numbers and essentially all performance is reached by about 50. Vision reconstruction methods (a.b) are significantly better than language reconstruction methods (c). And although language methods appear to use very large bottlenecks, they are comparable (vision methods presented here use only voxels in the visual cortex). Relative to vision, language methods are much closer the random baseline and have longer way go. as shown by the inset. This also reveals limitation of the resolution of the BrainBits approach: there is not much room for bottlenecking when performance is near the random baseline and metrics lack well calibrated scale. Across different metrics, both low level metrics (like pixel correlation and word error rate) and high level metries (like DreamSim and BERT) the message is the same: models asymptote quickly.
fraction of the data available.

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2
Related work
Several recent works have focused on predicting the latent features of deep, pretrained, generative models from IMRI data in order to reconstruct corresponding stimuli. Han et al. [9] introduced 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, 30J. 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 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 result, a popular choice for many recent methods [6, 29], and we select 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 BERTSScore [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 = 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 dimension. Specifically, let } where yt is an individual original image corresponding the brain data response F,, Then, as stated, our aim is to find the best reconstruction achievable for given restriction L, where reconstruction quality scored by some metric s);-)):
that compresses the brain data to a vector of smaller
max
((f(()())) vb)
This allows us produce curve of reconstruction quality function of L. We restrict attention to linear transformations, gL, to find the interpretable mappings that yield the best reconstruction quality.
Model performance lies in a range between model-specific randomly generated images and model- specific ceiling. To compute a model's 'sss random performance, we run bottleneck training and recon- struction, substituting the original brain data with synthetic data generated according to (0, 1). The purpose of this baseline is to obtain 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.
Experiments
We adapt three state-of-the-un 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 not a simple pre- post-processe processrnn step function call, it must be integrated into the method, which at times can
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require updates to the optimizer being used, effectively calling for 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 map the fMRI data latent representations of the corresponding images, namely VDVAE [3] and CLIP [21] embeddings of the images. An additional regression is fitted 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-diffusii Complete details are given in the original paper [17].
[31], which produces the final predicted image.
Our approach to bottlenecking BrainDiffusers is shown in Figure 1. For a given bottleneck size L, we learn a mapping gL from the IMRI input to L-dimensional vector. From this vector, we learn 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 VAE, and the outputs of this mapping are passed through the VAE's decoder produce 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 learned, 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 IMRI-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 input and uses GPT to rute-segresyyvely 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 GPT text embedding. Instead of using the raw brain activity input, 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 al. reconstruct language from perceived speech, imagined speech, perceived movie, and perceived multi-speaker speech (see [28]), and report performance averaged across these tasks. Additional details 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 constant scaling factor.

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Effective dim. of vision bottlenecks
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Figure 4: How large are the bottlenecks? Even though the bottleneck representations have dimensions, it is not necessarily the case that all dimensions are used by the bottleneck mapping. For both language and vision, we can mcasure the effective dimensionality to get a sense for how much of the channel capacity is being used. For BrainDiffuser, the effective dimensionality is comparable to the bottleneck size, showing that information is being extracted from the neural recordings up to about 15-20 dimensions For language bottlenecks, effective dimensionality remains low showing that little of the channel capacity, and therefore little of the neural signal, is being used.
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, insert information bottlenecks into two vision and one language recon- struction method and vary the bottleneck size while optimizing on the original objectives. Having learned these bottleneck mappings, study the representations learned by the bottleneck mappings themselves, and we compute the effec- tive 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.
then investigate the resulting reconstructions. We can also
How much information is needed to reconstruct image text? Qualitative results 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
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, achieve most 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% the original performance, as measured by BLEU and METEOR respectively, and the WER is comparable. Chance performance 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 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, 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 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 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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bottleneck size 1
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bottleneck size 25
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0.00
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Figure 5: What areas of the brain help reconstruction the most? Models quickly zoom in on useful areas even at low bottleneck sizes. Note that for clarity the color bar cuts off at 1e-6, values above that are all orange. In this case BrainDiffuser early visual system. As the bottleneck size goes up models exploit those original areas but do not meaningfully expand to areas. Ideally, one would hope to more of the brain playing an important role with larger bottleneck sizes; this is not what BrainBits uncovers.
subject I attends to peripheral areas of the
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Figure 6: What information do bottlenecks contain? For the BrainDiffusers approach we compute the decodability of four different features (object class, brightness, RMS contrast, and the average gradient magnitude)
function of bottleneck size. Object class refers decoding the class of the largest object in the image; often the focus of the image. The average gradient magnitude is proxy for the edge energy in the image. Dashed lines in plot (a) indicate 1-out-of-61 classification chance, 1.6%. Dashed lines on plots (b, c, d) indicate the metric's 's MSE distance from the average metric value on the training set. Larger bottlenecks are needed to extract more object class information above chance. Edge energy, brightness and contrast are mostly exhausted early. Looking at features as function of bottleneck size can reveal what types of interpretable features models learn, offering some explanation as to why performance goes up as a function of bottleneck size.
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.
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6 Limitations
BrainBits has 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 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 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 per- formance 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 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: //gi thub. com/ cttp///wwggg//////
ccllwan/Brainnits.. BrainBitss.
7 Discussion and Conclusion
The fidelity of a reconstruction depends the priors of the generative model being used and the amount of useful information extracted from the brain. Through visual inspection or from reconstruction metries, 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 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 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.
used, and many of the produced details can
The priors create 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 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: carly 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 's law states: "When a measure becomes target, it ceases to be a good measure". With the advent of high resolution reconstructed image stimuli, we as 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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7.1 Acknowledgements
We would like to thank Colin Conwell and Brian Cheung for their helpful feedback and discussion about our experiments.
This work supported by the Center for Brains, Minds, and Machines, NSF STC award CCF- 1231216, the NSF award 2124052, the MIT CSAIL Machine Learning Applications Initiative, the MIT-IBBM Watson AT Lab, the CBMM--Semens Graduate Fellowship, the DARPA Artificial Social Intelligence for Successful Teams (ASIST) program, the DARPA Mathematics for the Discovery of ALgorithms and Architectures (DIAL) program, the DARPA Knowledge Management Scale and Speed (KMASS) program, the DARPA Machine Common Sense (MCS) program, the United States Air Force Research Laboratory and the Department of the Air Force Artificial Intelligence Accelerator under Cooperative Agreement Number 148750-19-2, 1000, the Air Force Office of Scientific Research (AFOSR) under award number FA9550-21- 1000-21-----9,,,,--111--1-----19 the Office Naval Research under award number N00014-204 101-20011222 20-1-2589 and award number N00014-20- 1-2643, and this material based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2141064. This material is based on work supported by The Defense Advanced Research Projects Agency under Air Force Contract No. FA8721-05-C-0N6 LA8702-15-D-000 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views The Defense Advanced Research Projects Agency. The views and conclusions contained in this document are those of the authors and should not interpreted as representing the official policies, either expressed or implied, of the Department of the Air Force the U.S. Government. The U.S. Govermment is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
and/or
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12

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A Appendix
A.1 Training bottlenecks
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 batch size b 128, an Adam W optimizer [15], weight decay of wd = 0.1 and learning rate of ir = 0.01. We train for 100 epochs and use the weights with the best validation loss at test time.
Case study:
BrainDiffuers
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 batch size of 512, learning rate Ir 5e 4, and the Adam optimizer. We train for 100 epochs and use the weights with the best validation loss at test time. For evaluation, we report the average scores across windows, (see [28] for details).
Takagi et al 2023 Takagi et al. learn mappings from the early visual area to VAE latents of the stimuli [27]. They also learn mappings from the early, ventral, midventral, midlateral, laterial, and parietal regions to the latents of 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 [le 5, le 4, le 3, le 2] and weight decays in [le 3, 1c
2, le 1, 5c 15e-11. 1].
13

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A.2
brain-diffuser bottlenecks
Brain Diffuser
Reconstruction
Ground Truth
1
5
10
25
50
Ceiling
Subject 5 Subject Subject
Subject
Figure 7: Samples for all subjects in BrainDiffuser, for single image
Reconstruction Ceiling
Ground Truth
1
10
25
50
Diffuser
Figure The same images shown for subject in Figure 2a are shown here for subject 2
14

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Brain Diffuser
Reconstruction Ceiling
Ground Truth
1
5
10
25
50
Figure 9: The same images shown for subject in Figure 2a shown here for subject 5
Brain Diffuser
Reconstruction Ceiling
Ground Truth
1
5
10
25
50
Figure 10: The same images shown for subject 1 in Figure 2a are shown here for subject 7
15

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A.3
Projecting bottlenecks onto the brain
bottleneck size
bottleneck size 5
bottleneck size 25
bottleneck size 50
0.00
0.25
0.75
1.00
Model weight 1e-6
(a) Regression weight plot for subject
bottleneck size
bottleneck size
bottleneck size 25
bottleneck size
0.00
0.25
0.50
0,75
1.00
Model weight
(b) Regression weight plot for subject 2
bottleneck size
bottleneck size
bottleneck size
bottleneck
0.00
0.25
0.50
0.75
1.00
Model weight
(c) Regression weight plot for subject 5 bottleneck
bottleneck size
bottleneck size
bottleneck
0.00
0.25
0.50
0.75
1.00
Model weight 1e-6
(d) Regression weight plot for subject
Figure 11: Regression weight plots for each subject
16

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aTL-faces sece-selec
region in anterior portion of temporal lobe
EarlyVis Early visual cortex EBA extrastriate body area
FBA-1 posterior section of fusiform body area
FBA-2 anterior section of
fusiform body area
204
FFA-1 posterior section
fusiform face area
FFA-2 anterior section of fusiform face area
OFA occipital face area
OPA occipital place area
PPA parahippocampal place
area RSC retrospenial cortex
Figure 12: Legend of ROIs for main text salience maps in Figure 5 and Figure 11.
17

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A.4 Text reconstruction examples
Bottleneck
Text output
didn't 't want be alone i had to get out of there ran the bathroom and threw up was so ashamed didn't I even know what to do i just sat there for a few minutes and then i went to the bathroom and was crying i had never been this one incident that will never be forgotten and it s only ever been reported in the us and i' I'm sure it's been told to me and probably all of my family have never been asked for my name don't 't have one i am only allowed to show my id have no high school after the summer after graduating from a university with no major life we were both a bit socially awkward but it was definitely one of the best things ever and we both loved each other for that alone we never got married or anything so that was first and i
5
50
100
we both lived in different countries she moved out of state to college to study abroud in the us we never dated for more than year and we only talked on the phone and don't 't remember her name know her from my online profile which is pretty cool because i can
500
went to college in a town about ten hours away from campus and lived there for years and never met anyone who was good looking interesting but i think there were a few that were got the idea to date someone with a degree in math and i had to learn that same city had just graduated from a university in a state i lived in on scholarship that was much higher than what most schools have and the system was very strict so my mom basically forced me pay for college degree in math and couldn't 't afford the same in
1000
Full
the same university had just moved from the us different state she had a degree in psychology and a job that was not one for but it seemed she was the kind of girl that got into college for her degree in computer science had learn the language we re both from up north we' re both kind of newish to the neighborhood this is in florida we both went to college not great colleges but man we graduated and i i'm actually finding myself a little jealous of her because she has this really cool job washing dogs she had horses back home and she really
Ground truth
Table 1; Expanded full text for the subject samples shown in Figure 2c
18

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Bottleneck
Text output
worst she doesn't 't want to have sex with you and you end up getting hurt worse if you don't have girlfriend and she decides to go out with another guy then you have to be the one to tell her that you are not interested in her and that she should just go back to
5
and now i'm not so sure i should have just given up the whole idea of living oul of a truck in trailer my first thought is to buy house and move oul the second is that it's not even that big a deal we live in the same state as you i can't 't understand why phd program at a small college in a suburb of my hometown in the city have lived in for years and am the youngest female graduate there i' I've never been married but have done a few times i was wondering if you have ever had girl come to you hard and it felt
50
100
same city had just gotten married to a girl who has recently graduated from a university in a state she has no money for a lawyer who will never be able to afford one i also know this because my sister has an aunt who has degree in accounting that has the ability to pul up
500
up in a small rural town in the west coast of the state lived in not an alcoholic nor a drug addict didn't smoke weed was never a bad person but i had a problem with drinking for example my father drank more than a bottle of wine because he didn't 't like his body
1000
high school was in a small town near where grew up in my hometown was the biggest city in the state knew most people were good looking and all that but had no idea the average guy was this skinny nerd with no social skills or brain and he was afraid of heights because
Full
grew up the south and the area live was a major city my country we went to college at the same university i am pretty smart kid but was never the kind of guy to actually work computer science lab or study programming and think he should be a
Ground truth
we' re both from up north we' re both kind of newish to the neighborhood this is in florida we both went to college not great colleges but man we graduated and i'm actually finding myself a little jealous of her because she has this really cool job washing dogs she had horses back home and she really
Table 2: Expanded full text for the same stimulus as shown in Figure 2c and Table 2, but for subject
19

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Bottleneck
Text output
1
name but she didn't 't answer went to the bathroom and i was crying i had no idea what to do didn't 't know what i could do to help her just wanted to get her out of there was so scared couldn't even look at her i just damn movie theater and watch my wife die right there and feel so horrible but i ''m just so ungry and so incredibly sad hate her for not telling me that am the best and the most beautiful and that she should never have to work so hard and so the manager talking to a guy i wasn' I Jamiliar with on the phone we made to the bar and there were at least of us and remember some of the conversations we had and the whole place seemed to be filled with the same stuff the only difference was that in large city with about hundred students from variety of major and regional universities all graduating with degrees in physics history and math in engineering and biology all of this was based around very big and complicated system of programming that had been built by very very small and a university which was in suburb of city with small town of about million students all graduating with degrees in physics was one of the youngest of them all of sudden very powerful and intelligent individual with a phd in physics from small liberal arts
5
50
100
500
1000
in a city with about thousand students all graduating from a prestigious college in the middle east there are hundreds of universities all over the country where i was raised from what saw was a very large area with lots of very liberal arts majors and an average high school level
Full
he was born a city that was mostly rural but was in the south of the us and was an engineer and teacher all of which i had good relationship with she was also very interested in working in a computer science program but had a job in another we' re both from up north we' re both kind of newish to the neighborhood this is in florida we both went to college not great colleges but man we graduated and i'm actually finding myself a little jealous of her because she has this really cool job washing dogs she had horses back home and she really
Ground truth
Table 3: Expanded full text for the same stimulus as shown in Figure 2c and Table 3, but for subject 3
20

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A.5
Way Evaluation
alexnats
alesnet22
alooma188
0.9
Incoption
No bottleneck
0.8
Accuracy
0.7
0.6
0.5
10
20
25
30
35
40
45
50
Bottleneck
Figure 13: BrainDiffuser identification accuracy. We evaluate the agreement between latent em- beddings of of ground-trui truth and decoded images of the BrainDiffuser method using the identification accuracy protocol described [26].
aloener18
0.85
cip, hb
clp_h12
A
0.80
0.75
Accuracy
0.70
0.65
0.60
0.55
0.50
Bottleneck
Figure 14: Takagi identification accuracy. We evaluate the agreement between latent embeddings of of ground-truth and decoded images of the Takagi method using the identification accuracy protocol described in [26].
21

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A.6 Takagi Image Grid with BrainDiffuser Image Indices
Bottleneck Size
Reconstruction Ceiling
Ground Truth
10
25
50
Takagi
Figure 15: Generalization other reconstruction methods: Shown here are images reconstructed for several bottleneck sizes applying our BrainBits approach to Takagi & Nishimoto et al.
22

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A.7 Effective dimensionality of the fMRI inputs
Talo 19W Discrslociity
traebifygee (ON Dinenskeally
Parcentage of warahce Expaan
12611/201
300 1100
2009/12011
X000 LIK 19l0
5400
2006
1560
SWN
2010
Subject
Subject
Subject
200 1803 01:00 30803
5006
1000 Lic
2000
1500 200
Phinappa DCE 500 Nit Lei LOCC
Comporr ents
(a) brain-diffuser.
(b) Takagi el.
Figure 16: Effective dimensionality (dashed line) of fMRI inputs used in brain-diffuser (a) and Takagi et al. (b). Here, effective dimensionality is computed the same was as the main text, as the number of dimensions needed to explain 95% % of the variance (as determined by PCA).
23

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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 described and analysis of their reconstrucion performance.
2. Limitations
Question: Does the paper discuss the limitations 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 of assumptions and complete (and correct) proof?
Answer: [NA].
Justification: 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 ex- perimental results the paper to the extent that 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 our paper 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 instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
The paper 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].
Justification:
6. Experimental Setting/Details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary 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 all subjects. plots that give reconstruction results, standard error bars shown to reflect variance across samples.
8. Experiments Compute Resources
Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
24

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Answer: [Yes]
Justification: See discussion/conclusion.. All mappings and image generations were com- puted Nvidia Titan RTXs with 24GB of GPU RAM the course of week.
9. Code Of Ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: http:/:///euuipp/pee hwtps///cppp..cccccccccceeeeeee
Answer: [Yes]
Justification: We adhere to the neurips code of conduct
10. Broader Impacts
Question: Does the paper discuss both potential positive socictal impacts and negative societal impacts of the work performed?
Answer: [NA].
The paper discusses generative model priors, but in the context of a narrow neuroscience application with no salient socictal impact.
Justification:
11. Safeguards
Question:
Does the paper describe safeguards that have been put in place for responsible release of data models that have 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 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 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 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 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 study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [NA].
Justification: We do collect new data.
25


Reviews:
[{"review_id": "7PTc7cp3zU", "reviewer": "Reviewer_BfHp", "summary": "The 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.", "questions": "1. 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.", "limitations": "There are no negative societal impacts that I can think of, and the limitations section provides helpful context regarding the practical application of their method.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The 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).", "weaknesses": "While 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your time and feedback. We're glad you found our work timely, sound, and convincing. We appreciate your questions, which have helped us sharpen our descriptions.\n\n- _What is the effective dimensionality of the actual neural activations? 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?_\n  - The reviewer asks a good question! What is the dimensionality of the input fMRI signal and are we simply recovering this number? For brain-diffuser, the average effective dim of the inputs is 2,257. For Takagi et al, the average is 4,485. In comparison, 50 is small. (See additional PDF). We will add this figure to the appendix and note this computation in the main text.\n  - We are glad the reviewer found our paper well motivated. We would like to further add that our main contention is not simply that the amount of needed brain data is small in an absolute sense, but that, as a field, we should always use a metric that is sensitive to the amount of brain data used for reconstruction, so as not to be misled by methods that improve on the image prior without improving on the extraction of neural signal.\n\n- _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  - We suspect that higher level areas aren’t being used in part because they are redundant with early areas. Representations in early areas are retinotopic and potentially simpler than those in later areas which may be neither.\n  - And yes! This is exactly what we aim to do in subsequent work and the main other application of our method: probing the information available in the decodings conditioned on different areas of the brain. We plan to automate this by having models look at the resulting images and then quantify the information that is being decoded: like albedo, lighting, shape, texture, class, relationships, etc.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "254f4YkZ1E", "reviewer": "Reviewer_khsL", "summary": "The 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.", "questions": "Please answer to the main weakness I have identified. Any convincing clarification would result in a improved score of my review.", "limitations": "I would add the limitation identified in the weakness part, if it still stands after the rebuttal.", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- 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.", "weaknesses": "- 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing the importance of our work and for their time and feedback.\n\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\n  - First, to clarify, we use a single linear layer, not an MLP, for projection. The nice thing about using a single linear mapping is that we can be fairly confident that no extra expressive power has been added to the reconstruction method. A single linear layer has much less representational power than a VAE. Of course, it is likely that better compressions may be possible with more powerful projection mappings, but these would be less interpretable as the reviewer correctly points out.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "h8yLHRDwDV", "reviewer": "Reviewer_eceS", "summary": "The 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).", "questions": "- 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?", "limitations": "The authors have adequately addressed the limitations of their study by presenting a dedicated section (6) with extensive discussion.", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "*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.", "weaknesses": "- 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?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer's time and good questions, which helped us clarify our interpretations. We're glad that the reviewer found our paper important to the field and easy to follow. Below, we answer the posted questions.\n\n- _Can't the range be set according to data dynamic range?_\n  - The inset is to emphasize the fact that for the text decoding case, reconstructed text is still very close to chance in an absolute sense. We will enlarge the label text for legibility and include a larger plot in the supplement. \n- _Why do authors claim that the measured expansion is not \"meaningful\"? What would a meaningful expansion look like?_\n  - In the 5 vs 50 case we meant to point out that no new significant cluster areas of activity emerge. You are correct in that “meaningfully expand” is not very precise, we will update the language in our paper to better describe our observations. We merely meant to observe that the most salient regions remained the brightest across all scales, and few new islands of activity were highlighted for the larger bottlenecks.\n- _Figure 11 has cut-out subplot titles that are illegible_\n  - Thanks for the catch! We’ll fix the spacing. To clarify, the rows of that figure were in subject ID order: 1, 2, 5, 7.\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  - For comparison, for brain-diffuser, the average effective dim of the fMRI inputs is 2,257. For Takagi et al, the average is 4,485 (see additional PDF). This suggests that the underlying neural code is far higher dimensional than our bottleneck size, despite any redundancies that may exist.\n- _Authors propose that each method should report a reconstruction ceiling. How would they suggest to approach this problem in general?_\n  - The very simplest version of incorporating a ceiling, would be to always show the evaluation metrics, as they are with the ground truth inputs. For example, we show the complete scale from 0.0 to 1.0 on Figure 3c. We argue that looking at the distance to this simple ceiling is an important part of gauging whether reconstruction results can be called successful or not. A slightly more sophisticated ceiling involves using the ground truth image latents as conditioning information for the diffusion model. This is what we do in Figure 3a-b. Both of these procedures should always be feasible.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fpkl9Fagzw", "reviewer": "Reviewer_eVVq", "summary": "This 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.", "questions": "1. 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?", "limitations": "There is no potential negative societal impact.", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper is easy to follow.\n- The paper aims to explore the extent of information necessary for brain decoding.", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their time and extensive feedback. We answer questions and clarify a few points below.\n\n- _The paper suggests that better reconstruction may be achieved with less brain signal input, yet all brain signals are utilized by the reconstruction models._\n  - Yes, while all brain signals are available at input, the dimension of the bottleneck puts a hard upper bound on the amount of information available at reconstruction time. Notably, increasing the size of this bottleneck leads to better performance, from which we infer that these bounds are tight.\n  - We have preliminary analysis of which parts of the brain are likely important (Figure 5), but truly selecting inputs by dropout is left for future work. This procedure is complicated by differences between subjects and significant computational costs, since dropout is a combinatorially difficult problem.\n\n- _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. Diffusion models exhibit significant randomness in image generation, enabling the selection of images resembling ground truth stimuli._\n  - We do not allow selecting images resembling the ground truth stimuli. We agree with the reviewer that doing so would be highly problematic. Instead, models get one chance to produce an image: one image for each of the 1000 stimuli. Then the average performance is computed. The variance on that average performance is small, since over so many images the stochastic nature of the diffusion models averages out.\n  - Regarding the power of diffusion models, we agree entirely! Our entire point is that these models operate with minimal semantics, rather than with extremely rich representations needed to actually encode the entire image. This is not a particular property of diffusion models, it is instead a property of any large model trained on data that is similar to the test data shown to subjects.\n- _The performance of brain-to-image generation remains unsatisfactory._\n  - I think we’re in agreement here! The main thrust of our argument is this: prior work has claimed that brain-to-image reconstruction is satisfactory to some degree, according to existing metrics. \n  - And whatever flaws these images have is up for discussion, but our experiments show that you can achieve the same performance on those metrics with much less information from the brain. This is so little information that clearly something must be missing from the images. Yet this point has not been made in the literature and it is critical for further progress.\n- _The number of samples for different subjects is limited._\n  - Our paper should be thought of as a meta-study over the field of work in brain-to-image reconstruction. To this end, we consider those subjects and samples used by existing works.\n- _Differences in temporal and spatial resolution as well as signal characteristics among recorded signals pose uncertainties about BrainBits' adaptability._\n  - Our method is very general: and simply entails adding a restriction on information flow through the reconstruction pipeline. This is applicable, no matter the modality.\n- _Authors claim reconstruction may require same or less signal from the brain, while also acknowledging the scarcity of open neuroscience datasets._\n  - To clarify, there is a distinction between the signal available in a single brain reading and the data available in the training dataset. The first is relevant to the quality of reconstruction, the second is relevant to the ease of training a reconstruction method.\n\n- _Regions depicted in Figure 5 are not clearly labeled_\n  - We created a legend with labeled regions (see additional PDF) that we will add to the main text.\n- _The effective dimensionality in Figure 4 lacks explicit clarification._\n  - Lines 201 through 204 explain the effective dimensionality method used. We will update the caption to include this.\n- _Text occlusion in both Figure 3 and Figure 11._\n  - Thanks! We will add labels, clarification in the caption for Figure 4, and we will fix the spacing in Figures 3 and 11\n- _What meaning do lines 4-7 convey?_\n  - The reasoning is this: currently only measuring the quality of the reconstructed images allows for models to improve for reasons that do not involve extracting more signal for the brain. Lines 4-7 simply list a few of these possible reasons. The purpose of our work is to introduce a metric that cannot be “gamed” in such ways.\n- _Experimenting with other signal types or generative models?_\n  - The overwhelming number of recent state of the art publications in image reconstruction use fMRI enabled by the NSD dataset. Since this is by far the most common paradigm, we decided to adopt it. There is nothing about our method that is signal-specific. Nor is there anything that is specific to diffusion generative models. Given the state of the field, we chose the most representative methods.\n- _Do different voxels get activated for different stimulus images within a subject?_\n  - All of our learned maps and results are per subject. This avoids this problem.\n- _Can BrainBits operate with fewer brain signals as input? If not, what specific contributions does this paper offer to the community?_\n  - We are not sure if we understand the question. BrainBits is not a reconstruction method in itself. BrainBits is a method for measuring the dimensionality of the neural recordings required to perform reconstruction. And it is applicable, no matter the input size. It addresses a key problem: large networks can reconstruct images well with little information by exploiting the similarity between their massive training sets and relatively restricted test sets. Without BrainBits, one cannot disentangle why a model is performing better. Is it explaining more of the brain or taking more advantage of its priors? We show that these priors can be extreme.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank the reviewers for their time and helpful feedback! We address each reviewer's concerns individually.

Meta-review: This study introduces a low-dimensional bottleneck on the information utilized for seen-image reconstruction from fMRI responses. The study demonstrates that even low-dimensional bottlenecks can yield seemingly good reconstruction performance, suggesting that much of the success of current methods may rely more on strong priors of the image-generating models than on detailed neural information readout. Most reviewers and the AC agreed that this work is important, as it is the first to tackle a major problem plaguing the subfield of seen-image reconstruction.

Questions remain about the approach taken in this work to limit the read-out information. In principle, a low-dimensional bottleneck does not constrain the amount of information utilized; while achieving good reconstruction performance with a compressed, low-dimensional readout is remarkable, it does not prove that only a specific amount of information was used. As suggested in the paper's discussion, a quantized bottleneck would have provided stronger control over the information used by restricting it to a defined number of bits of Shannon information, aligning more closely with the paper's title. Additionally, one reviewer expressed concerns about the quality of the reconstructions and the general applicability of the approach.

Given the importance of disentangling the contributions of image priors and actual neural decoding, most reviewers and the AC agree that this work should be accepted to NeurIPS, despite its limitations. We anticipate a fruitful debate and follow-up studies that will refine the methodology proposed in this work.
Decision: Accept (poster)