SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We used CRAFT to extract features from harmonized and regular ResNet-50s trained on ImageNet that explain their predictions of neural activity in PL of Monkey 1 (Fig. 5). To do this, we decomposed a DNNs predicted activities for image patches into the most important and distinct features using non-negative matrix factorization, then scanned over every single image patch to find those which most strongly explained each discovered feature. While the harmonized ResNet-50 predicted that parts of faces, arms, and heads explained the majority of variance in IT, the less accurate and unharmonized
ResNet-50 predicted that the background, oriented-edges, and a feature combining head, body, arm, and hand parts were most important for IT. Thus, hDNNs not only break the trade-off between ImageNet and neural prediction accuracy of ImageNet-trained DNNs, they can also generate testable hypotheses on the relative importance of different features for visual system neurons.
The scaling laws of deep learning The immense gains of DNNs in computer vision [13] and natural language processing [71] over recent years share a common theme: they have relied on unprecedented computational resources to train models with billions of parameters on internet-scale datasets. These benefits of scale have been studied over the past several years across a number of domains and often yield predictable improvements on standard internet benchmarks [71, 72]. Surprisingly, scale has also caused models to exhibit human-like behavior in psychophysics experiments [13, 57, 73, 74]. In other words, many aspects of primate behavior are captured progressively better by DNNs as they scale-up and improve in accuracy on benchmarks like ImageNet.
Scale is sufficient for modeling internet benchmarks, but not primate intelligence In parallel, there's a growing body of research suggesting that large-scale DNNs are becoming less aligned with human perception in multiple ways. For instance, the representations and visual decision-making behavior of DNNs are inconsistent with humans, and this problem has worsened as they have improved on ImageNet [7]. DNNs are also becoming less capable at predicting perceptual similarity ratings of humans, and they remain vulnerable [75] to so-called "adversarial attacks." Our work adds to this body of research, indicating that current scaling laws are incompatible with modeling primate behavior and poorly suited for explaining the neural computations that shape it.
Aligning DNNs with primate vision There have been a number of methods proposed for aligning DNNs with primate vision beyond the neural harmonizer that we leverage here [7]. It was found that co-training DNNs for object recognition and minimizing representational dissimilarity improved the adversarial robustness of a recurrent DNN [76]. Others have shown that similar forms of representational alignment improve few-shot learning and predictions of human semantic judgments in DNNs [77,78]. The successes of these behavioral and representational alignment methods highlight the real limitations of current DNN training routines and data diets for generating artificially intelligent systems that act like biological ones.
Biological learning routines There have been many efforts to align the object functions, learning rules, and data diets of DNNs more closely with biological systems. It was found that DNNs trained with self-supervision on ImageNet instead of supervised recognition achieve similar accuracy in predicting V1, V4, and IT responses to images [68]. DNNs trained with self-supervised learning on head-mounted camera video from infants instead of ImageNet were approximately as accurate at predicting neural data as DNNs trained on Imagenet but needed far less data to do this [68]. Others have found that adversarial training of DNNs yields models that are more accurate at predicting neural responses to images [79] and have similar tolerance to adversarial attacks as neurons in IT [69].
A revised approach for reverse engineering visual cortex Biological data is expensive to gather and often noisy. This makes the prospect of accurately modeling the responses of IT to complex stimuli especially daunting. DNNs optimized for object recognition represented a potential solution for this problem: pretraining on internet datasets alleviated training issues associated with small-scale neural data, and the architectures and training routines of accurate DNNs could offer insights into the circuits that underlie visual perception as well as the developmental principles that shape those circuits. Our findings suggest that while DNNs still hold great potential for predicting image-evoked responses from IT neurons, new training routines and data diets are necessary for continued improvement.
The neural harmonizer is a partial solution to the problems that DNNs face in modeling primate IT. The success of hDNNs in breaking the pareto-front faced by 135 different DNNs indicates that significant aspects of primate perception and the neural circuits that shape it cannot be divined from internet data alone. We believe that the neural harmonizer and learning constraints provided by
large-scale human behavior data is only a short-term solution to this problem, and that if DNNs were able to learn about the visual world more like primates do, they would be even better at predicting neural data. In support of this goal, we release our code and data at https://serre-lab.github.io/neural_harmonizer/.
Limitations. One limitation of our work is that we use the responses of neurons with face-selectivity to replicate and understand the trade-off between ImageNet accuracy and neural predictivity faced by DNNs. As faces (and even humans) are not a category in ImageNet, it is possible that this dataset [6] could bias our results in ways that are difficult to predict†. However, we find the same ImageNet accuracy and neural predictivity trade-off on this dataset (Fig. 3) as we did on the three recordings of object selective neurons hosted on the Brain-Score website (Fig. 1), indicating that DNNs face similar issues in predicting each set of recordings. Moreover, the neural harmonizer was successful in breaking this trade-off, even though it involves ImageNet training using human feature importance maps for objects. Finally, it was noted in the original paper where our recordings came from that the neurons, while localized based on their face-selectivity, responded to non-face stimuli as well [6]. In summary, our work reliably demonstrates that new paradigms are needed to advance DNNs as models of IT, and spatially-resolved recordings such as those used in this work support this goal.
Another limitation of our work is that while we found that hDNNs are significantly more predictive of IT neuron responses than any other DNN, they still explain only  $50 - 60\%$  of the variance in neuronal activity. One straightforward way of doing better is by expanding the dataset of human feature importance maps we used for harmonization from annotations for approximately 200,000 images to the entire 1.2M ImageNet dataset.
Broader impacts. By building better models of primate IT, we are taking significant steps toward the reducing the reliance of visual neuroscience on animal models for experimentation, supporting the development of neuroprosthetic devices that resolve visual dysfunctions, and providing vision scientists with a richer understanding of how IT works. Our findings also highlight a main limitation of the scaling laws that are guiding progress throughout artificial intelligence today: scale is not sufficient for explaining biological intelligence.
This work was supported by ONR (N00014-19-1-2029), NSF (IIS-1912280 and EAR-1925481), DARPA (D19AC00015), NIH/NINDS (R21 NS 112743), and the ANR-3IA Artificial and Natural Intelligence Toulouse Institute (ANR-19-PI3A-0004). Additional support provided by the Carney Institute for Brain Science and the Center for Computation and Visualization (CCV). We acknowledge the Cloud TPU hardware resources that Google made available via the TensorFlow Research Cloud (TFRC) program as well as computing hardware supported by NIH Office of the Director grant S10OD025181.
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†Note, however, that there are many animal and human faces in Imagenet images, giving DNNs ample opportunity to learn about them.
Distribution shifts One possible explanation for the trade-off we observed between DNN accuracy on ImageNet vs. predictions of neural data is that there was a distributional shift between the images used in the two evaluations. To test this possibility, we extracted activity vectors for a large sample of images from ImageNet and all images from the standard Brain-Score benchmark dataset for IT [14] and the images we used in our experiments [6]. For ImageNet, we used all images in the validation set that also had human annotations from ClickMe.
We measured distribution shifts by measuring the accuracy of a linear classifier in discriminating between ImageNet and either the standard Brain-Score benchmark dataset for IT [14] or the images we used in our experiments [6]. We began by taking equal-sized random samples of ImageNet image encodings and encodings from either the Brain-Score dataset (28 of 191 images) or our dataset (all 28 images). We then trained a linear SVM using scikit-learn and performed leave-one-out cross-validation on either set of samples. Finally, we repeated this procedure 1,000 times, using a new random sample of ImageNet encodings each time, and measured the average accuracy for discriminating between ImageNet and either neural data image set. We found that images used in the standard Brain-Score benchmark for IT [14] are significantly further out-of-distribution from ImageNet than the images that we used in the main text [6] ( $T(999) = 98.12$ ,  $p < 0.001$ , Fig. S1).
Projection of stimuli features
Figure S1: Stimuli from [14], but not [6], are out-of-distribution from ImageNet. Penultimate-layer activities from a ResNetv2-50 trained on ImageNet were extracted for 1916 images from the validation set of ImageNet, 191 images from [14], and 28 images from [6]. A principle components analysis decomposed those activities into a 2-Dimensional projection, which demonstrated a distribution shift for stimuli from [14], but not [6]. Indeed, while stimuli from [14] could be reliably discriminated from ImageNet (98.7 acc,  $T(999) = 98.12$ ,  $p < 0.001$ ), images from [6] were not discriminable from ImageNet (56.5 acc,  $T(999) = 98.12$ , n.s
Figure S2: Noise ceilings at every 40ms bin of activity from 0ms - 250ms. We binned every 40ms of neural responses following the original procedure for processing this data [6]
B Neural data analysis
We measured the maximum explainable variance, or noise ceiling, in our neural data recordings in every 40ms bin of activity from 0ms - 250ms. We binned every 40ms by following the original procedure for processing this data [6]. Because images were not repeatedly shown to the same population receptive field, we computed each neuron's noise ceiling for an image as the highest correlation between it and another neuron for that image. We then focused our analyses in the main text on the time bin where each neurons in each animal/brain area achieved the highest average noise ceiling (Fig. S2).
DNN Model Zoo We comprehensively evaluated the ability of a myriad of DNNs from the TIMM toolbox. These DNNs, available under the Apache 2.0 license, are intended for non-commercial research purposes. The complete list of DNNs we evaluated can be found in Table S1.
D Neural Harmonizer Training
We relied on the original neural harmonizer recipe [7] to train and harmonize 14 DNNs for object recognition on the ImageNet [9] dataset. For each model, we searched for settings of the regularization terms  $\lambda_{1}$  and  $\lambda_{2}$ , which control the relative importance of losses for object recognition and human feature alignment during training, that maximized accuracy on the ImageNet validation set. In total, we trained a VGG16, ResNet50_v2, ViT_b16, EfficientNet_b0, ConvNext_Tiny, and MaxViT_Tiny (Table. S2). We did not attempt to apply the neural harmonizer to models which relied on pretraining with datasets other than ImageNet because the ClickMe feature importance dataset we used only contained annotations on a subset of images in ImageNet. Thus, we expect that
Architecture	Model	Versions
CNN	VGG	4
ResNet	24
EfficientNet	8
ConvNext	10
MobileNet	6
Inception	7
DenseNet	4
RegNet	3
Xception	4
MixNet	4
DarkNet	2
NFNet	2
TinyNet	4
LCNet	2
DLA	2
MnasNet	2
Resnext101	6
GhostNet	2
ViT	General ViT	21
leViT	4
MaxViT	7
DeiT	4
Hybrid	CoAtNet	5
Table S1: Our DNN model zoo, taken from the TIMM library.
Model	Accuracy	Human Alignment	Note
VGG	69.3	61.5	λ = 2
ResNet 50	77.17	45.0	λ = 2
EfficientNet B0	77.51	52.3	λ = 20
ViT B16	75.7	72.6	λ = 5
ConvNext Tiny	75.9	73.2	λ = 1
MaxViT Tiny	78.6	45.3	λ = 1
Table S2: DNNs trained with the neural harmonizer.
more work is needed for expanding ClickMe to larger-than-ImageNet scale data before these DNNs can be harmonized.
E Extended results
Cross validation One limitation of the evaluation approach used in the original Brain-Score work [11] is that model selection is done on the same dataset as testing (i.e., there is no partitioning of the data into separate training, validation, and test sets). Thus, it is possible that the results we describe in the main text are a byproduct of "double dipping." To test for this possibility, we repeated our experiments using separate training, validation, and test splits of the data. For each round of cross validation in this procedure, we held out one image for testing. We also held out half of the training data for selecting the model layer that best predicted neural responses (validation). Our results from this approach strongly correlated with the ones reported in our original submission and our overall conclusions remain the same (Fig. S3; Monkey 1 ML  $\rho = 0.98$ ,  $p < 0.001$ ; Monkey 2 ML  $\rho = 0.97$ ,  $p < 0.001$ ; and Monkey 2 PL  $\rho = 0.98$ ,  $p < 0.001$ ).
Figure S3: DNN neural prediction accuracy after evaluating on a test set held out of training. Unlike the original approach introduced in Brain-Score [11], here we train, validate, and test models on separate partitions of the dataset. DNNs trained and evaluated in this way follow the trends described in the main text.
Task	ML	PL
Autoencoding	0.005	-0.009
Class object	0.091	0.075
Curvature	0.019	-0.009
Denoising	-0.014	0.013
Edge occlusion	0.029	-0.0002
Edge texture	0.025	0.014
Egomotion	0.002	-0.018
Fixated pose	0.118	0.101
Jigsaw	0.099	0.097
Keypoints 2D	0.024	0.015
Keypoints 3D	-0.057	-0.006
Nonfixated pose	0.164	0.086
Normal	0.065	0.056
Reshading	0.019	0.001
Room layout	0.131	0.087
Segment semantic	-0.011	0.029
Segment unsupervised 25D	0.041	0.008
Segment unsupervised 2D	0.052	0.043
Vanishing point	0.046	0.076
Alternative objective functions and datasets To better understand how ImageNet per se affected DNNs' abilities to predict neural responses, we evaluated a zoo of them trained on the Taskonomy dataset and task set [80] and also the Ecoset [81] naturalistic object categorization dataset and task. We took 19 DNNs pretrained on each task in the Taskonomy, and 4 DNNs trained for classification on ecoset, and applied the standard Brain-Score fitting procedure that we describe in our original submission Methods to derive prediction accuracy scores for each model. These models were far less effective at explaining neural activity than any of the ImageNet-trained models we provided in the main text (Tables S3 and S4). To summarize, our human behavior-aligned harmonized DNNs are more accurate at predicting neural responses than other DNN we tested, regardless of the dataset or task that was used to train them.
Table S3: Neural predictions for ML and PL in Monkey 1 using Taskonomy-trained DNNs.
Model	Best time bin	Score
Alexnet	90	0.046
ResNet50	130	0.111
VGG16	130	0.086
Inception	210	0.109
Table S4: Neural predictions for ML in Monkey 1 using Ecoset-trained DNNs.
Model	Neural	ImageNet
VGG baseline	0.523	67.23
VGG harmonized	0.524	67.369
ResNet50v2 baseline	0.501	76
ResNet50v2 harmonized	0.502	77.17
ConvNext baseline	0.519	74.3
ConvNext harmonized	0.524	75.8
MaxViT baseline	0.541	78.6
MaxViT harmonized	0.543	78.7
EfficientNetB0 baseline	0.506	75.4
EfficientNetB0 harmonized	0.508	77.51
LeViT baseline	0.472	74.9
LeViT harmonized	0.473	75.4
ViT baseline	0.493	73.2
ViT harmonized	0.499	75.7
Table S5: Performance of select DNNs on inferotemporal (IT) recordings from Brain-score. org [14]. For each DNN class, models with higher accuracy at predicting IT responses and ImageNet recognition are bolded.
Performance on Brain-Score As a point of comparison, we tested a subset of our DNN on a standard inferotemporal (IT) recordings benchmark on Brain-Score.org. In each case, harmonization marginally improved performance over baseline (Table S5).
Neural dynamics We also present modeling results on other time bins (with lower noise ceilings) than those considered in the main text (Figs. S4- S6). For ML IT in monkey 1 and monkey 2, we present 90ms-130ms, 130ms-170ms, and 210ms-250ms. For PL IT of Monkey 1, we present 130ms-170ms, 170ms-210ms, and 210ms-250ms, windows. In each case, we found a similar trade-off in these data as we did in those highlighted in the main text, as well as a significant improvement for harmonized vs. unharmonized DNNs. This similarity extends to the qualitative predictions made by each model on ML IT neurons (recall that we show the image-evoked activities of PL IT neurons in the main text, S6).
Figure S4: Results using different time bins than in the main text for the same region (ML) for both monkeys. The pattern of results for each time-bin are consistent with the main text, despite these data having lower noise ceilings.
Figure S5: Results using different time bins than in the main text for the IT PL in Monkey 1.. The pattern of results for each time-bin are consistent with the main text, despite these data having lower noise ceilings.
Figure S6: DNNs optimized for object recognition on ImageNet rely on different features than those encoded by neurons in primate inferior temporal (IT) cortex. The activity of ML IT neurons of Money 1 is plotted next to the predicted activity of a model representing each class of DNNs: harmonized DNNs (hDNNs), visual transformers, self-supervised DNNs, convolutional neural networks, DNNs trained on more data than ImageNet, and adversarially robust DNNs.