Beyond Sight: Probing Alignment Between Image Models and Blind V1

Authors: galen  pogoncheff
jacob  granley
alfonso  rodil
leili  soo
lily marie turkstra
lucas  gil nadolskis
arantxa  alfaro saez
cristina  soto sanchez
eduardo  fernandez jover
michael  beyeler
Venue: ICLR_cc_2024_Workshop_Re-Align
Published: 2024-02-09 03:59:31

Abstract: Neural activity in the visual cortex of blind humans persists in the absence of visual stimuli. However, little is known about the preservation of visual representation capacity in these cortical regions, which could have significant implications for neural interfaces such as visual prostheses. In this work, we present a series of analyses on the shared representations between evoked neural activity in the primary visual cortex (V1) of a blind human with an intracortical visual prosthesis, and latent visual representations computed in deep neural networks (DNNs). In the absence of natural visual input, we examine two alternative forms of inducing neural activity: electrical stimulation and mental imagery. We first quantitatively demonstrate that latent DNN activations are aligned with neural activity measured in blind V1. On average, DNNs with higher ImageNet accuracy or higher sighted primate neural predictivity are more predictive of blind V1 activity. We further probe blind V1 alignment in ResNet-50 and propose a proof-of-concept approach towards interpretability of blind V1 neurons. The results of these studies suggest the presence of some form of natural visual processing in blind V1 during electrically evoked visual perception and present unique directions in mechanistically understanding and interfacing with blind V1.

Main paper: 
 introduction: 
 Despite lacking visual input, neural activity in the visual cortex of blind humans persists after vision loss (Burton, 2003; Fine et al., 2003; Huber et al., 2015; Striem-Amit et al., 2015; Fine & Park, 2018) . Research suggests that cortical plasticity enables the cortex to undertake non-visual tasks (Burton et al., 2002; Sadato et al., 2002) , though the extent to which visual processing capabilities are preserved remains under explored (Lambert et al., 2004; Striem-Amit et al., 2012; Hahamy et al., 2021) . Unraveling how the blind visual cortex represents visual information could unlock profound insights into brain function, cortical plasticity, and sensory compensation (Beyeler et al., 2017) . This knowledge is crucial for advancing rehabilitative technologies, like visual neuroprostheses, which evoke visual sensations (phosphenes) through electrical stimulation (Fernandez, 2018) .Recent studies have shown that the latent representations in DNNs trained for vision tasks align with neural activity in the visual cortex of sighted primates (Yamins et al., 2013; 2014; Cadena et al., 2019; Schrimpf et al., 2020; Marques et al., 2021; Pogoncheff et al., 2023) . This synergy has fueled advancements in visual neuroscience, such as the discovery of maximally exciting images for individual neurons in primate cortex (Bashivan et al., 2019) . Yet, the critical question remains: does this alignment hold for spiking activity in visual cortex of humans or, specifically, the blind? Answering this could pave the way for accurate modeling of visual processing in the blind human cortex, enabling the design of prostheses which deliver more perceptually intelligible visual experiences (de Ruyter van Steveninck et al., 2022; Granley et al., 2022a; b; 2023; Castro et al., 2023) .In this study, we analyze the alignment of DNN representations with neural activity from an awake blind human participant implanted with a 96 channel intracortical prosthesis (Fernández & Normann, 2017 ) measured during a delayed-response working memory (WM) task (Figure 1 ). Neural activity related to visual WM has been found in primates and humans (Albers et al., 2013) , but the role of spiking activity in human V1 during WM to support visual processing is not fully understood. This setup therefore provides a unique opportunity to probe the similarities between DNN-computed visual representations and human perceptual representations. Novel contributions include:• We show a significant representational alignment between DNN activations and V1 in a blind human for both spiking activity and local field potentials. Across 69 DNNs, blind V1 alignment was positively correlated with 1) DNN alignment with neural activity in the visual cortex of sighted primates as well as 2) DNN ImageNet object recognition accuracy. • We demonstrate that DNN activity not only aligns with neural activity during stimulation, but also with local field potentials measured after stimulation or during mental imagery. • We propose a proof-of-concept approach towards enhancing the interpretability of neurons recorded in blind V1, a neural system which cannot be probed with traditional psychophysical experiments that rely on the ability to present visual stimuli (e.g., image or video).These preliminary results mark a crucial step towards understanding visual processing in the blind visual cortex-a key to advancements in both neuroscience and rehabilitative neuroengineering.  
evoking visual perception via intracortical electrical stimulation: 
 The data analyzed in this work was acquired using recordings and behavioral responses from an awake blind human with a 96-channel intracortical visual prosthesis (Fernandez & Normann, 2017) , implanted in right parafoveal V1, during a delayed-response WM task. The participant (61, Male) had no light perception for 5 years due to optic neuropathy following pituitary adenoma surgery.Over 90 trials, one of three chosen electrodes was stimulated with a 167 ms cathodic-first biphasic pulse train, reliably evoking a spatially distinct phosphene ("stimulation period", Figure 1A ). The participant would then remember the evoked visual perception during a 3 -5 s "delay" period, after which an audio cue triggered the participant to visually recollect the phosphene in vivid detail (accounting for shape, brightness, and visual field location; "recall period"). Following ten consecutive trials for a given electrode, the participant was asked to verbally describe and draw the perceived phosphene. These drawings were then refined by a sighted researcher during an extensive discussion with the participant, given the difficulty of replicating the perceived phosphene without vision.Each phosphene drawing was digitized and placed within a 244 × 244 pixel image with black background, assumed to span 8 degrees visual angle (DVA). Based on verbal feedback from the participant, the phosphene drawings were scaled to span 2 DVA and placed within the left visual field of the image (corresponding to the implant's placement in the right occipital lobe). Noise in raw neural activity resulting from stimulation artifacts was suppressed via blanking during the stimulation window and interpolation with a 3rd order polynomial. Multi-unit activity (MUA), entire/envelope spiking activity (ESA), as well as θ, α, β, and γ bands of the local field potential (LFP) were extracted from the filtered neural activity (Appendix A.3).Traditionally, the representational alignment between two systems is measured by inputting the same stimulus to each system, measuring an internal state for each system (e.g., DNN activations, neural activity), optionally projecting the latent activity to a shared space, and quantifying the similarity between embeddings with an alignment score (Sucholutsky et al., 2023) . Common techniques include representational similarity analysis (RSA) (Kriegeskorte et al., 2008) and linear regression followed by a correlational score (Schrimpf et al., 2020; Cadena et al., 2019; Conwell et al., 2022) .For blind subjects, however, there is no natural visual input. Therefore, we investigated neural activity triggered by electrical stimulation or mental imagery. To obtain DNN activations, we used the corresponding phosphene drawings as input, assessing the network's capability to mirror the neural activity associated with the participant perceiving said phosphene (Fig. 1C ). Importantly, these networks were not designed to predict neural activity, but to perform ImageNet classification.To quantify representational alignment between DNN and blind V1 activity, we utilized RSA and correlations of learned linear encodings. For RSA, correlations between trials of neural activity were used to compute dissimilarity matrices, which were compared using correlation score. For linear regression, cross-validated ridge regression from PCA-projected model activity was used to predict biological neural activity measured on each of 96 channels. The alignment between neural representations and encoded DNN representations was then scored according to Pearson's correlation coefficient (Schrimpf et al., 2020) . 
sustained stimulus-selective multi-unit activity in human v1: 
 We first examined how intracortical recordings differed across the three phases of the WM task. We found significant increases in neural activity at the onset of stimulation and the audio cue for recall initiation (t-test, p < 0.001). During stimulation, delay, and recall periods, neural activity was significantly different than spontaneous activity (p < 0.001). Analysis revealed shared neural activity patterns per electrode across stimulation, delay, and recall periods, allowing for the successful classification of stimulating electrodes based on recall-period activity alone, achieving 97% accuracy for the delay and 88% for the stimulation periods using a random forest classifier. These shared signatures could be learned from the delay or recall period activity, but not from the electrically evoked activity, suggesting that WM elicits a subset of the full activity present during electrical stimulation.We then examined how well different brain-like DNNs aligned with intracortical recordings in blind V1. We selected 69 models evaluated on the Brain-Score benchmark suite (Schrimpf et al., 2020) as candidates for measuring DNN-Blind V1 alignment. DNN-blind V1 alignment was measured for all six feature sets (MUA, ESA, and LFP measures) during the stimulation (Figure 2 , top) and recall period (bottom). A baseline similarity score (dashed lines) measures the representational alignment (using regression or RSA) calculated directly from the drawing, without using DNN activations.For electrically evoked activity, blind V1 alignment scores were significantly higher than baselines (t-test, p < 0.001) for all features except LFP γ . For recall activity, only LFP β activity predictivity was significantly higher than the baseline. Across DNNs, we found a significant, moderate positive correlation between blind V1 scores and sighted V1 predictivity scores (Figure 2B ; Freeman et al., 2013) , sighted neural predictivity across V1, V2, V4, and IT (Figure 2C ; Schrimpf et al., 2020) , and ImageNet top-1 accuracy (Figure 2D ; Deng et al., 2009) . The only exception was that sighted V1 predictivity measured using regression was not correlated with blind V1 score (Figure 2B ; bottom).In general, similar trends were observed for recall period activity, but the blind V1 scores were not significantly above baseline.We then specifically examined ResNet-50 (He et al., 2016) , a model known for its shared representations with neural activity in V1 of sighted primates (Schrimpf et al., 2020; Pogoncheff et al., 2023) and which predicted blind V1 activity well. Analysis of alignment scores across the 96 neural recording sites (Fig. 3A ) showed significant correlations between DNN outputs and neural activities during the initial 200 ms of stimulation, except for LFP γ (Fig. 3 , left), with ESA showing the strongest correlation (average r = 0.518). In the post-stimulation phase (167 -367 ms after onset), only LFP θ , LFP α , and LFP β activities showed significant positive correlations with DNN outputs.During phosphene recall (0 -200 ms after the audio cue), only LFP β activity significantly correlated with the DNN (p < 0.01), suggesting that visual representations in LFP activity, even without electrical stimulation, align with findings that alpha activity is influenced by visual imagery and perception (Bartsch et al., 2015; Xie et al., 2020) .Optimization-based feature visualization has become a key method for interpreting DNNs (Erhan et al., 2009; Mordvintsev et al., 2015; Olah et al., 2017) , offering insights into how these networks process visual information. By applying this technique to our dataset of neural recordings and perceived phosphenes from a blind individual, we aim to uncover the functional characteristics of neurons in blind V1, similar to studies in sighted primate V4 (Bashivan et al., 2019) . Our approach involves creating a linear model that maps activations from the first layer of ResNet-50 to blind V1, and computing images that maximally activate specific neural sites according to this mapping.We demonstrate this concept with maximally exciting images (MEIs) for the neural sites most active during each of the 9 phosphenes, (Figure 3B ). Generally, the predicted MEIs share visual features with the phosphene that elicited a strong response at that site. Given our limited sample, we are hesitant to conclude that these MEIs fully capture the neurons' visual representations, but rather, suggest that this technique may help enhance understandings of visual processing in blind V1. 
discussion: 
 In this work, we present exploratory analyses on the representational alignment between neural activity measured in V1 of a blind human and latent DNN activations when presented with the same visual perception. While previous research has quantified representational alignment between DNNs and neural activity in V1 of sighted primates, this work is the first of its kind to extend these studies to intracortical neural activity in human, let alone in a blind participant.Studying blind visual cortex presents unique challenges due to the absence of visual input. To navigate this, we utilized neural responses from electrical stimulation and mental imagery, discovering significant correlations between the activity in blind V1 and both the activity in sighted primate V1 and the performance of DNNs on ImageNet. Specifically, ResNet-50 activations showed strong correlations with neural spiking and various LFP responses during stimulation, with fewer correlations during delay and recall. This opens up future opportunities to delve deeper into these distinct neural activities across different phases. Moreover, we introduce a proof of concept for interpreting blind V1 neurons, suggesting that with a diverse dataset of neural recordings and phosphenes, we could better understand neuron properties and refine stimulation strategies for visual prostheses.The significant, positive correlation between DNN-blind V1 alignment and DNN-sighted neural alignment suggests the potential use of V1-aligned DNN models for studying visual processing in both sighted and blind individuals. Interestingly, however, we observed that DNN-blind V1 alignment was more correlated with DNN-sighted overall neural predictivity than with DNN-sighted V1 neural predictivity. This observation raises two critical questions: 1) whether electrically evoked visual perception in blind individuals activates different processing mechanisms than natural visual processing, and 2) whether cortical reorganization alters the functional role of V1 in the blind.A notable limitation of the present study is that the dataset used for analysis contains a limited number of neural responses and visual percepts. Furthermore, our method relies on phosphene drawings for predicting neural activity. Even in this limited setting, well-aligned DNNs could inform more natural stimulation strategies for visual prostheses or deepen our understanding of neural representations in blindness. Moreover, innovative approaches such as topographic networks (Schrimpf et al., 2024) might predict neural activity without these drawings. Despite these challenges, our findings provide a valuable proof of concept, suggesting that existing techniques for assessing representational alignment could be extended and applied to understanding the visual cortex in blind humans. 
 
 Appendix:  



Reviews: review: title: title: Exposing an Interesting and Important New Domain
main_review: main_review: This paper evaluates the alignment between ANNs and the activities of V1 neurons in a blind subject during/following electrical stimulation. 

Strengths:
- The subject is highly novel and potentially very important to understand for medical applications such as retinal prostheses.
- The weaknesses of the approach are both understandable and clearly laid out in the discussion.
- The conclusions, while not necessarily unexpected, seem rigorous, and the inclusion of null results (i.e. the bottom panel of figure 2b) strengthens some of the other claims in my opinion. 

Weaknesses:
- There exist strong models for V1 in sighted primates that are well understood and do not require learning (i.e. steerable pyramid responses rank highly for many V1 datasets on the brainscore leaderboard [see here](https://www.brain-score.org/model/vision/1121). This would seem like a much more relevant baseline than the pixel inputs for comparing the deepnet predictivity to. 
- The MEI result seems like reading tea-leaves or some sort of memorization thing to me. For example the MEI for multiple phosphenes seem to be swapped. I disagree that this is evidence of a promising approach for gaining understanding of activity in blind V1.
- Reasonable predicitivity is only obtained during the stimulation period (for most forms of neural activity).

Questions: 
- It seems quite strange that overall sighted neural predictivity is more correlated with blind V1 predictivity than sighted V1 predictivity. I would appreciate if the authors could speculate on why this might be so.

Overall I think the work explores a potentially very impactful topic, and bringing awareness to this rather wide open problem to the appropriate community seems like a contribution in and of itself. I therefore recommend acceptance.
strength_weakness: strength_weakness: The paper explores a highly relevant and potentially quite important topic.
title: title: Blind human V1 alignment with DNNs
main_review: main_review: The authors present analyses on shared representations between DNN visual representations (corresponding to drawn phosphenes) and evoked activity in V1 from a blind human. Evoked activity comes from either electrical stimulation or mental imagery. They find that DNNs with better ImageNet accuracy are more predictive of blind V1 activity. Further they provide proof-of-concept for interpreting blind V1 neurons using ResNet-50 alignment. Such results suggest that there is natural visual processing in blind V1 during evoked perception and presents new mechanistic interpretability directions.

The contributions of the paper seem clear and concrete: previous influential work has shown correspondence between human neural activity in visual regions to the latents of pretrained image recognition DNNs, and this work extends such work to intracortical activity and further is the first to do so with a blind participant. 

There are currently zero details on the single subject used in this study except for the description that they are blind. What is nature of the subject’s blindness? Was the subject born blind or did such blindness develop during their lifespan? Also are they completely 100% blind or is there any partial visual information that they can experience (e.g., vague brightness levels)? How old was the participant?

For Figure 2B, why do you think regression showed no statistically sig. positive correlation whereas RSA did? Is there an advantage to RSA that can reveal such relationships that regression cannot?

The authors report that LFP-beta activity significantly correlated with DNN and thus visual representations were present even without any electrical stimulation. But it seems like there was significant anti-correlation in the other feature sets, how do the authors explain this and is the LFP-beta significance computed with correction for multiple comparisons?

MEI is never defined in the paper, it took me awhile to guess that it probably stands for maximally evoked images.
strength_weakness: strength_weakness: Clear and concrete results, aligns with the workshop theme, that I think has potential for lively discussion.
title: title: Cool Idea
main_review: main_review: This is a really neat setup to study V1 processing in blind subjects. There is the surprising finding that neural activity after electric stimulation can be predicted from DNNs processing the drawn phosphenes. I have a few small comments and questions:

Fig1: Can you connect the image of the phosphene that is drawn and the image that is fed into the DNN? Those are the same, right?

How many total phosphenes, trials and subjects did you use? What is the split into training and test data for the regression models?

2.3 last paragraph, typo"="

3.2. what does 'imagined activity' mean? recall?

It is a little confusing that Fig2A bottom is on recall, but then all of B,C,D is on stimulation, right?

3.3 These are some great questions, but I think there is too much going on. You are juggling: 1) V1 activity in blind 2) V1 activity during WM/recall 3) LFP vs spike coding. All of these are interesting, but, for simplicity and to get tractable scientific insights maybe try to condense the complexity to single hypotheses along one of those dimensions. Imho, question 1) alone is already super exciting.

I really like the last sentence in 3.4, great self-reflection!

I am not sure what to make of the MEIs. They look like the optimization is severely under-constraint?

Here is some prior work on inferring response properties without visual inputs: https://iovs.arvojournals.org/article.aspx?articleid=2773648
strength_weakness: strength_weakness: Great idea, well-written, clean execution, good job!