Model-Guided Microstimulation Steers Primate Visual Behavior
Authors: Johannes Mehrer
Ben Lonnqvist
Anna Mitola
Paolo Papale
Martin Schrimpf
Venue: ICLR 2026 Poster (2026)
Keywords: causal interventions
topographic deep artificial neural networks
brain modeling
Primary area: applications to neuroscience & cognitive science
TL;DR: Topographic brain models with perturbation modules predict monkey behavioral responses in a visual recognition task.

Abstract: Brain stimulation is a powerful tool for understanding cortical function and holds the promise of therapeutic interventions to treat neuropsychiatric disorders such as impaired vision. Prototypical approaches to visual prosthetics apply patterns of electric microstimulation to the early visual cortex and can evoke percepts of simple symbols such as letters. However, these approaches are limited by the number of electrodes that can be implanted in early visual regions. Instead, higher-level visual regions are known to underlie the representations of complex visual objects such as faces and scenes and thus constitute a promising target for stimulating the cortex to elicit more complex visual experience. We developed a computational framework composed of two main components to address the challenge of stimulating cortex in high-dimensional object space spanned by higher-level visual cortex: 1. a causally predictive model that predicts primate behavior from image and stimulation input via topographic models and perturbation modules. 2. a mapping procedure that translates optimal model stimulation sites to monkey cortex. Testing our approach in two macaque monkeys that perform a visual recognition task, our results suggest that model-guided microstimulation is a promising approach to steer complex visual behavior. This proof-of-principle establishes a foundation for next-generation visual prosthetics that could restore complex visual experiences by stimulating higher-level visual cortex.

Published as a conference paper at ICLR 2026
MODEL-GUIDED MICROSTIMULATION STEERS PRI- MATE VISUAL BEHAVIOR
Johannes Mehrer' Ben Lonn Anna Mitola Paolo Papale* Martin Schrimpf EPFL, "Netherlands Institute for Neuroscience, *University of Parma, Dandelion Science
ABSTRACT
Brain stimulation is a powerful tool for understanding cortical function and holds promise for therapeutic interventions in neuropsychiatric disorders. Initial visual prosthetics apply electric microstimulation to early visual cortex which evoke percepts of simple symbols such as letters. However, these approaches are funda- mentally limited by hardware constraints and the low-level representational prop- erties of this cortical region. In contrast, higher-level visual areas encode more complex object representations and therefore constitute a promising target for stimulation but determining representational targets that reliably evoke object- level percepts constitutes major challenge. We here introduce a computational framework to causally model and guide stimulation of high-level cortex, compris- ing thrce key components: (1) a perturbation module that translates microstimula- tion parameters into spatial changes neural activity; (2) topographic models that capture the spatial organization of cortical neurons and thus enable prototyping of stimulation experiments; and (3) mapping procedure that links model-optimized stimulation sites back to primate cortex. Applying this framework in two macaque monkeys performing a visual recognition task, model-predicted stimulation ex- periments produced significant in-vivo changes in perceptual choices. Per-site model predictions and monkey behavior were strongly correlated, underscoring the promise of model-guided stimulation. Image generation further revealed a qualitative similarity between in-silico stimulation of iace-selective sites and a patient's 's report of facephenes. This proof-of-princip for model-guided microstimulation and points toward next mext-generation visual pros- thetics capable of inducing more complex visual experiences.
establishes a foundation
INTRODUCTION
Vision is fundamental to human experience, enabling navigation, object recognition, and social interaction. For individuals with visual impairments, restoring even basic visual function could dra- matically improve quality of life. Visual prosthetic devices represent a promising approach to bypass damaged tissue along the visual processing hierarchy (e.g. retina, optic nerve, lateral geniculate nu- cleus) and directly stimulate the visual cortex to shape or evoke visual percepts.
Current visual prosthetic approaches are in prototypical development stage, but have already achieved remarkable successes: microstimulation of primate early visual areas can reliably evoke percepts of simple geometric shapes and even letters (Chen et al., 2020b; Beauchamp et al., 2020; Fernandez et al., 2021). These approaches to visual prosthetics rely on the spatial arrangement of neurons in early visual cortex, which mirrors the layout of the visual field: nearby points in the visual field and thus on the retina correspond to nearby points on cortex, a principle referred to as retinotopy (Hubel Wiesel, 1962; 1968; Engel al., 1997).
However, these approaches are fundamentally limited by the number of electrodes that can be im- planted in early visual cortex, and by the representational properties of early visual areas: neurons in primary and secondary visual cortex (V1, V2) encode simple local features such as location or orientation of bars and simple combinations thereof (Hubel Wiesel, 1962; 1965; 1968; Hegdé
"Equal supervision by P.P. and M.S. "Coresponndece: [johannes mehrer, martin.schrine mp @epf1 chfs

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existing approaches
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retinatopic cortical organization (no computational model)
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Figure I: Overview of approach. Existing approaches to visual prosthetics microstimulate early cortical areas els for the selection of stimulation sites, and instead rely on retinotopic organization where nearby locations in the visual field represented in nearby locations in the early visual system. These approaches have successfully been shown to elicit percepts of simple visual symbols such as letters, but are limited by the low-level representational properties of early visual regions. We propose model-guided approach that targets higher-level visual cortex via computational simulations with the goal of eliciting percepts of complex visual objects.
earlier parts of the visual processing hierarchy, do not computational mod-
& Van Essen, 2000; Anzai et al., 2007). Stimulation of these regions elicits elementary percepts such phosphenes or simple shapes, and is currently not capable of evoking complex object-level representations required restore rich visual experience. For individuals with profound visual im- pairments, the ability to perceive and recognize objects such as faces, tools, scenes would open the possibility of richer and possibly more useful visual perception. Achieving this level of per- ceptual complexity might therefore require targeting cortical regions that explicitly encode complex visual objects - but effective stimulation of such high-level and complex representations remains an unsolved challenge.
Here, we propose to use computational models to guide microstimulation directly targeting higher- level visual cortex. Higher visual regions are known underlie the representations of complex visual objects such as faces and scenes. However, the influence of retinotopy on object representa- tion decreases strongly from early to higher-level visual regions (Issa & DiCarlo, 2012; Silson al., 2015; Yue et al., 2020; Poltoratski et al., 2021). Thus, higher-level visual regions, retinotopy is much less useful guiding principle for causal intervention techniques. Rather, the organization of higher-level regions is shaped by more complex visual and semantic features such as animacy vs. inanimacy and high-level category selectivity (Kriegeskorte et al., 2008: Kanwisher, 2017). These more abstract principles alone do not give clear guidance with respect to eliciting more complex visual percepts.
To address this challenge, we develop model approach to microstimulation in higher-visual cortex (Fig. 1). We present early successes of applying model predictions experimentally to two macaque monkeys. Specifically, show that optimizing the combination of visual stimuli and stimulation parameters via simulations in brain-mapped topographic networks allows for predicting monkey visual behavioral responses in complex object recognition task. to behavior are strongly correlated with actual experimentally observed changes in monkey behavior, although the model tends to overestimate the behavioral effect. Model-predicte experiments also lead to a substantial shift in monkey behavior along a target direction. To qualitatively interpret the effect of stimulation in-silico, employ image generation the simulated neural activity patterns during microstimulation and observe the emergence and enlargement of faces and face-features when stimulating in face-selective regions.
changes
BACKGROUND & RELATED WORK
Visual Cortex Stimulation. A common implant for intracortical recording and stimulation pri- mates is the Utah array: a 96- or 64-channel microelectrode grid that allows simultaneous multi-site
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recordings and the application of electrical pulse trains to focal patches of cortex. In early visual cortex, site selection and interpretation of stimulation are guided by retinotopy a roughly point- to-point mapping from visual field locations to cortical locations, that stimulating an electrode tends to evoke a phosphene at the receptive-field position represented beneath that electrode (Hubel & Wiesel, 1962; 1968).
Using retinotopy as orientation principle, existing prototypes of visual prostheses target sets of elec- trodes whose receptive fields tile desired visual-field positions and then induce static or dynamic stimulation patterns to elicit percepts of simple shapes or letters (Chen et al., 2020b; Beauchamp al., 2020; Fernandez al.. 2021). Behavioral outcomes are typically quantified with forced- choice tasks that probe detection, localization, or identification, yielding psychometric functions over current amplitde, pulse rate, or stimulus strength and associated summary statistics such as thresholds or changes in area under the curve. While effective for low-level percepts, this retinotopy- based strategy inherently constrained by electrode count and by the representational granularity of early areas, which primarily encode local features. As such, it does not naturally extend to evoking object-level percepts which are more closely associated with higher-level visual cortex.
Models of the Brain. Over the past decade, artificial neural networks (ANNs) have emerged as powerful system-level models of the visual brain that explain substantial variance in neural and behavioral responses (Yamins et al., 2014; Saaligh-Razavii & Kriegeskorte, 2014; Schrimpf et al., 2018; Mehrer et al., 2021; Gokce & Schrimpf, 2025). Recently, these models have been endowed with explicit cortical topography: topographic ANNs place units on 2D sheet and are trained with spatial regularizers. yielding smoothly organized maps across layers (Lee et al., 2020; Keller et al., 2021; Lu et al., 2023; Margalit al., 2024; Deb al., 2025; Rathi & Mehrer al., 2025). In deeper layers, they exhibit category-selective patches (Margalit et al., 2024) reminiscent of the functional organization of high-level visual cortex (Kanwisher, 2017: Tsao al., 2003: 2006; Freiwald al., 2009).
Because their representations are spatially embedded, topographic models can simulate the focal neural effects of currents applied via causal intervention techniques: localized perturbations can be applied to model tissue and the model can predict how the induced neural activation changes propa- gate across the simulated cortical sheet to predict downstream behavioral consequences. Prior work has evaluated such perturbation modules offline, showing that topographic models can anticipate the behavioral effects of different causal intervention techniques including microstimulation (Schrimpf et al., 2024). Building on this foundation, we move from offline evaluation to prospective model-in- the-loop use: optimize stimulation sites and stimuli in-silico and, to our knowledge for the first. time, test these model predictions in-vivo to run visual cortex stimulation experiments.
Visualization of visual representations. Recent work links deep image synthesis to the neural code in high-level vision. Bashivan et al. (2019) used a discriminative deep network (AlexNet) in a generative way to synthesize images that maximally drive or selectively control V4 population re- sponses, and Ponce et al. (2019) used closed-loop optimization in the latent space of deep generators (DeePSim, BigGAN) to evolve images that strongly excite IT neurons. Building on GANs, Dado et al. (2024) and Papale et al. (2024) learned linear mappings between neural activity and GAN la- tents to reconstruct and optimize category-selective stimuli, while Shahbazi al. (2024) introduced "perceptograms" GAN- -egeerrrtd images that animals behaviorally confuse with the state of being optogenetically stimulated in IT. We follow similar strategy, but - lacking monkey neural record- ings during perturbations visualize model IT representations: simulated microstimulation perturbs topographic model IT activity, which we then map into GAN and diffusion latent spaces to illustrate the predicted perceptual consequences of stimulation. Our approach may thus provide intuitions about model IT representations.
METHODS
We combine electrophysiological recordings, model-guided microstimulation, and primate behav- ioral testing in 3-stage process that closes the loop between computational models and primate experiments (Fig. 2). The experimental setup involves 2 macaque monkeys performing a two- alternative forced choice (2AFC) visual recognition task, with Utah electrode arrays implanted in their inferior temporal cortex (for details, see Appendix Sec. A.2.1). The goal of the stimulation is to bias monkey behavior towards one of the two visual choice targets.
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Figure 2: Model-guided microstimulation. 1. Model-brain mapping. To align model tissue and monkey brain recordings, we use passive-viewing responses of 4,000 images recorded 2-4 days before each experimental session. We then simulate various positionings of electrode grid the topographic tissue of model candidates, selecting the model grid position and orientation that maximizes correlations between model and monkey recording sites. This yields fixed one-to-one mapping between sites in the model and brain- wrain-implanted electrode grid. 2. Prototype experiments in model. For each candidate site we generate sequences of seven images varying smoothly along
GAN latent space, rank them by selectivity score (slope-to-noise), and test the effect of microstim- ulation on model-predicted 2AFC behavioral choices. Deepest-layer representations are converted to wwweerrnnatiie--oreed-------e
responses via similarity comparisons. 3. Test model-selected pa- rameters in primate. We select the top site-sequence predictions, mapping model neural sites back to the corresponding IT electrodes, and deploy the monkey experiment in 2AFC recognition task. Biphasic trains of electric stimulation are delivered on designated trials, interleaved with sham. Full details in Appendix Sec. A.2.1.
To model this experiment, we first align topographic models to subject-specific passive-viewing clectrophysiolggica recordings. Second, we prototype microstimulation experiments in the model identify the most promising experimental setting. And third, we experimentally test whether the model-optimized combination of visual stimuli and stimulation sites yields predicted behavioral shifts when testing them in-vivo.
3.1 MODEL-BRAIN MAPPING
Topographic models. For modeling the effects of microstimulation and behavior we first train topographic deep artificial neural networks (TDANNs) based on the ResNet18 architecture (He al., 2015) using approach from Margalit et al. (2024) to incorporate spatial organization principles observed in biological vision. Before training the units of a given layer are assigned to a unique location 2D- 2DD-plane that serves the model cortical sheet. This spatial arrangement of model units allows us during training to compute a spatial loss (details below) and during inference to represent the neural changes induced by simulated microstimulation (Schrimpf et al., 2024).
optimized TDANNs using a combined self-supervision (Laski SimCLR, Chen et 2020a) and spatial loss (SL)
TDANN Loss
Lusk
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For each layer l and batch, we sample local cortical neighborhoods on the layer's 2D sheet and, within each sampled neighborhood, we sample unit pairs (i.j). For each selected pair we compute (1) response similarity rij as the Pearson correlation between the units' activation vectors across stimuli, and (2) an inverse-distance weight Diy 1/(d3 + 1), where dij is the Fuclidean distance between their fixed cortical coordinates. By repeating this procedure for all sampled pairs, we obtain vectors and D. Following Margalit et al. (2024), we instantiate the spatial term as the relative spatial loss (SLx):
SI = 1- -Crr((, D);
which encourages nearby units to have more correlated responses, yielding smoothly varying maps across layers. For example, model early visual regions show orientation preference maps form- ing 'pinwheels' where the preferred orientation of neurons rotates smoothly along all possible orientations from 0 to 180 degrees that are known to exist in early visual areas across species (Kaschube et al., 2010). Additionally, model higher-level visual regions in the deepest layer show category-selective regions similar to higher-level visual cortex in humans and non-hune oon-hmman primates (Kanwisher, 2017; Tsao et al., 2003; 2006; Freiwald et al., 2009).
We trained candidate topographic models on combinations of image datasets including ccoset (Mehrer et al., 2021), ImageNet (Russakovsky et al., 2015), Labeled Faces in the Wild (LFW, Huang et al. 2008), and VGGFaces2 (Cao et al. 2018; for details, see Appendix Table 1). For all model training, we used the same set of hyperparameters as Margalit et al. (2024): 200 epochs, initial learning rate: 0.6 (cosine decay), momentum: 0.9, batch size: 512. Weights are frozen after training such that the models' neural activity only depends on visual and stimulation input.
Topographic mapping procedure. Using passive-viewing responses of 4,000 images randomly sampled from GAN latent space, recorded 2-4 days before each stimulation session, from Utah ar- rays in monkey inferior temporal cortex, we map neural sites in the model to neural sites in the brain implants. do so, we first computed a linear predictivity score from the TDANN's deepest layer to each Utah array in IT. Concretely, for each model instance and each array we extracted model activations to the 4,000 GAN images and fit a 10-fold cross-validated ridge regression. We quan- tified predictivity using the explained variance (R2²) across folds and retained those model-array pairs with the highest R² averaged across folds for subsequent topographic alignment. Impor- tantly, linear predictivity (R²) showed a large range actoss combinations of models and monkey arrays (monkey I: [-0.06, 0.27], monkey 2: [0.05, 0.19]), indicating that the model and monkey array selection through this mapping procedure is indeed important. In control analysis with an otherwise-matenn matched non-topographic ResNet-18 trained without spatial loss (a* 0), we observed comparable IT predictivity in monkey 1 (February session: R²2 0.27, June session: 122 0.19 on the same IT array). This indicates that introducing topography does not substantially change the linear model brain alignment in this dataset.
We then aligned simulated arrays and arrays used in-vivo by comparing their responses to a subset of the same 4,000 reference images. Specifically, we presented the images to both the monkey and the model, and for each candidate placement of simulated Utah array on the model's 's cortical sheet we correlated responses site by site with the monkey array. We averaged these correlations across all 64 electrodes and selected the placement and orientation that yielded the highest overall match, thereby establishing a one-to-one correspondence between model and monkey electrodes.
3.2 PROTOTYPING EXPERIMENTS IN-SILICO
Our goal was to select, for each monkey, a specific electrode in inferior temporal cortex and a specific image sequence that together produce the strongest commulationinduced behavioral outcome in complex visual recognition task. To this end, we optimized experimental parameters in model space before mapping them back to the animal (Fig. 2). For more detailed implementation details, see Appendix Sec. A.1.
Perturbation modules. Recent evidence shows that topographic deep artificial neural networks can predict the behavioral outcomes of causal intervention techniques such as microstimulation (Schrimpf et al., 2024). We adapt this approach in stimulation modules that simulate the effects of microstimulation changes to model units, simulating the magnitude and spatial spread electrical microstimulation observed in experimental studies. We parameterized the perturbation modules based em-
nearby neural tissue. These modules operate by applying localized activity
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Figure 3: Model predictions correlate with stimulation-evokedd behavioral shifts. A) Model- predicted behavioral shifts (AAUC) correlate with stimulation-evoker shifts in the monkeys' be- havioral responses (AAUC), both when combining across the two subjects (Pearson r = 0.53, p = 0.0012). B) Example psychometric functions from two stimulation sites (gray symbols in A).
pirical data from prior microstimulation experiments in primate inferior temporal cortex (Stoney al., 1968; Histed et al., 2009; Majaj et al., 2015; Kumaravelu al., 2022). The magnitude of in- duced activity changes followed established current-distance relationships, with stimulation effects decreasing function of distance from the stimulation site.
Formally, following Schrimpf et al. (2024), the perturbation of a unit at cortical distance d from the stimulation electrode defined as
d ((T)
Ar(d) = min (Ybase + fpube; ('mxx exp
(1)
where Thase denotes the baseline firing rate (set 30, Hz, consistent with primate IT recordings), fuulee is the stimulation pulse frequency (Hz), and y gain factor linking pulse frequency to fir- ing rate increase under linear assumption. To prevent unrealistically high activity (Ponce al., 2019), firing rates are clipped at T'ax = 200Hz. The distance-dependent decay is captured by d, the cortical distance (mm) from the electrode, and X()), a spatial decay constant (mm) that increases with stimulation current I (uA),, reflecting the broader spread of activity at higher currents. Thus, stimulation increases activity proportionally to the pulse rate at the electrode, saturates at a maxi- mum firing rate, and falls off exponentially with cortical distance, consistent with current-distance relations from empirical studies.
Stimulus generation. We adopted the stimulus generation method of Papale et al. (2024) based on StyleGAN-XL (Sauer et al., 2022), which links neural activity patterns in inferotemporal (IT) cortex to generative adversarial network (GAN) latent space. Specifically, a linear mapping be- tween multi-unit activity (MUA) from inferior temporal cortex recordings and the GAN's latent vectors (512 dimensions) was estimated from 4,000 reference images. This mapping enabled recon- struction of seen stimuli and, critically, of systematic perturbation of neural activity at individual cortical sites. By linearly adding subtracting up to five standard deviations of the response at targeted site to the 4,000 reference images, while keeping activity at other sites fixed, we gener- ated naturalistic seven-image sequences in GAN image space. Each sequence thus corresponded to a parametric modulation of the targeted site's response, reflecting its neural tuning dimension. These GAN-derived image sequences then served candidate stimuli for both in-silico and in-vivo microstimulation experiments, where they allowed us to test whether stimulation could bias neural activity and perceptual choices along the dimension to which the targeted site was tuned.
Procedure. For each candidate site, we (1) generate sequences of seven images to systematically modulate a site's activity: (2) rank sequences by simple selectivity score (slope-to-noise), favoring monotonic, site-specific modulation, and (3) run in-silico perturbation experiments via the micros- timulation module. To predict behavioral outcomes, we convert penultimate layer representations to 2AFC responses via similarity comparisons between the sample image and the two alternatives of given trial (for details of the experimental design in model and monkey, see Appendix Fig. 7). To approximate trial variability in otherwise deterministic models, aggregate across 30 top-ranked sequences per site to obtain smooth psychometric curves and summarize stimulation strength by AUCC (perturbed unperturbed trials).

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3.3 TESTING MODEL PREDICTIONS IN-VIVO
The final step is to test model predictions experimentally. To do so, we select the top site-sequence pairs predicted by the model to evoke the highest behavioral change and map the neural sites back to monkey electrodes via the model-brain mapping established in step 1. Following Papale et al. visual recognition task. During each
(2024) monkeys performed a salllermatives-fored-hhoooo trial we first present a target stimulus (one of the possible seven images of a sequence) followed by the simultaneous presentation of two alternative images (always two images at the two extremes of the given sequence). To indicate which of the two alternative images the animal perceived as more similar to the target image, they are trained to make a saccade to where the selected alternative image was presented. We applied biphasic microstimulation trains using chronically implanted Utah arrays (for details, see Appendix Fig. 7).
Stimulation and sham trials were randomly interleaved (50% 50%) and choices were read out from the alternatives used in model prototyping (sequence extremes). We quantified stimulation-evoked shifts in choice probability between unperturbed and perturbed trials and report effects as AAUC. Due to degrading signal quality (Appendix Fig. 10) the implants are now explanted from both animals, thus no additional experimental runs can be performed using our setup.
3.4 VISUALIZZATIONS
We visualize the perceptual consequences of model-guided microstimulation using two complemen- tary generative pipelines (see Appendix A.3). First, adapt the Brain2GAN framework (Dado et al., 2024) to our setting by replacing neural recordings with the deepest-layer activations of a topographic vision model, and by pairing these activations with images generated by pretrained StyleGAN-XL (Sauer et al., 2022) in its feature-disentangled w-space. We learn a ridge regres- sion on topographic activations to w-latents using only unperturbed activity, and then synthesize images from model states with and without simulated microstimulation. Because the GAN and ridge regression are fixed, differences between unperturbed and perturbed reconstructions directly reflect the changes in the model's internal representation induced by stimulation at specific cortical locations and simulated current levels.
Second, we use diffusion-basee pipeline built on Stable Diffusion v1.5 with IP-Adapter (Rombach et al., 2022; Ye et al., 2023), which conditions the denoising process on both text prompt and a CLIP (Radford et al., 2021) vision embedding. We generate train/test image set from prompts based on the ecoset category structure (Mehrer et al., 2021), and fit a ridge regression from deepest- layer topographic features to CLIP vision embeddings. At test time, we hold the text prompt, noise latent, and diffusion hyperparameters fixed and vary only the simulated microstimulation in the model, decoding the resulting perturhed activations into CLIP space and re-running the diffusion process. In both the GAN and diffusion pipelines, we additionally perform a nhuffldd-perturattin control: the targeted perturbation feature dimensions before decoding. This control preserves the overall perturbation magnitude but destroys its topographic structure, allowing us to test whether the emergence or amplification of face-like content in the generated images specifically depends on spatially structured stimulation of face-selective regions rather than on non-specific global modulation.
maximally face-selective site randomly permuted across
RESULTS
We evaluated two complementary measures of stimulation effectiveness. First, we asked whether the magnitude of stimulation-evokl evoked behavioral shifts in the monkey was predicted by the magnitude of shifts in our model (model-monke) Pearson-r correlation). Second, we asked whether stimulation in the monkey induced a consistent behavioral shift away from baseline (monkey AAUC significantly greater than zero).
We performed two experiments with the same experimental setup that differ slightly in the way pre-selected the monkey stimulation sites (Appendix Sec. A.1). Electrode implants were rel- atively stable for monkey 1 where we could perform both experiments, but the absolute number of tested sites still decreased from experiment (13) to experiment 2 (9 sites). Decreasing ral signal quality (Appendix Fig. 10) only allowed us to perform one experiment in monkey 2. The two experiments only differ in the spatial constraint applied select candidate electrodes of
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Figure 4: Model-guided stimulation biases monkey behavior. A) In experiment 2, model-guided stimulation induced a significant behavioral shift monkey (Wilcoxon signed rank test: = 0.043; Cohen's d 0.67). Due to declining signal quality, experiment 2 could not be conducted with monkey B) Three example GAN-generated image sequences used for stimulation (images 1, 4, and 7 shown from each seven-image sequence; corresponding sites highlighted in A).
given Utah-array. Specifically, in experiment 1 we used Manhattan distance between candidate stimulation sites of 16mm, whereas we reduced this limit to I. 1.2mm in experiment to allow for a larger number of candidate stimulation sites. In other words, the spatial constraint was loosened in experiment to allow for a higher percentage of stimulation sites to be tested.
4.1 PREDICTING STIMULATION-EVOOKE BELIAVIORA SIIFTS (EXPERIMENT 1)
We found that predecepe behavioral shifts were positively associated with stimulation-evokee shifts measured in both monkeys in experiment (Fig. 3). Specifically, the model predictions (in AAUC, x-axis) versus monkey behavior (in AAUC, y-axis) revealed robust correlations in both animals (monkey Pearson = 0.58, p = 0.024, 22 0.34, monkey 2: Pearson = 0.53, p 0.019, ,2 0.28). Bootstrap resampling yielded confidence intervals confirming that the estimated effect is reliably above zero both animals while their range reflects the small sample size (monkey 1: 95% CI: [0.20, 0.89], R E [0.04, 0.79]; monkey 2: 95% CI: r E [0.16, 0.85], R E [0.03. 0.73). To quantify how much of these effects could arise by chance, we shuffled behavioral labels and recomputed R² 1,000 times. This yielded null distribution with medians close to 0 (monkey 1: median R² = 0.034; 95% CI [6.8 x 10-5 3: monkey 2: median I²2 = 0.029; 95% CI [1.3 10-1, 0.24]). For both animals, the observed Rº exceeded nearly all null samples (monkey 1: permutation = 0.019; monkey 2:
0.017) This indicates that combinations of stimulation site and GAN image sequence predicted by our model yield stronger behavioral effects, tended to have stronger behavioral effect in the monkey. However, the monkey behavioral responses were not significantly greater than zero (Wilcoxon signed rank test, p > 0.05) in this first experiment.
4.2 INDUCING BEHAVIORAL BIAS ALONG A TARGETED DIRECTION (EXPERIMENT 2)
Due to degrading signal quality of the implanted neural recording devices in monkey 2 (Appendix Fig. 10), we were only able to perform experiment 2 with more candidate stimulation sites in monkey 1. In this experiment, monkey behavioral responses were significantly shifted above null baseline (Wilcoxon signed-rau -rank test; p- 0.043, effect size Cohen's 's d 0.671). indicating that parameters predicted by the model indeed yielded reliable behavioral effect in-vivo (Fig. 4). However, the larger number of candidate stimulation sites available in this experimental setting longer yielded evidence for per-site and wer-image-sequenee predictive behavioral power of our model (p 0.05). We believe this reduced effeect is mainly due to degraded signal quality (Appendix Fig. 10).
For both experiment I and 2 we cannot fully exclude contributions from global state changes, but several aspects of our design make explanations mainly based attention/arousal effects un- likely. Stimulation and non-stin stimmllutaiiin trials were randomly interleaved at 50% / 50%, minimizing expectation-driven state changes. What is more, ral microstimulation induces non-specific attentional or arousal effects that would be able to steer behavior in targeted way.
not aware of evidence that inferior tempo-
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A simulated
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Figure Visualizing perceptual effects of microstimulation. We visualize the effects of stimula- tion using a GAN-based approach (A), and diffusion based approach (B) and show two examples for each. Simulated current amplitude increases from left (0 μA) to right (1000 μA) in both panels. A) In the first row, stimulation transforms a cat's tail into an additional face, while in the second row enlarges the face of bear. B) The first row shows how stimulation transforms a bug-like creature into a face, while in the second row fruit is transformed into human face.
4.3 VISUALIZATION OF IN-SILICO PERCEPTUAL EFFECTS OF PERTURBATION
Beyond shifts in behavioral choices, key question is how neural stimulation alters the percep- tual content of visual experience. For instance, Schalk et al. (2017) examined a 26-year-old patient with intractable epilepsy who was implanted with 188 subdural electrodes also covering higher-level visual cortex to localize seizure foci. During stimulation, electrodes over a face-selective cortical re- gion evoked illusory faces ("facephenes") that appeared superimposed on any object the patient was viewing, whereas stimulation of color-selective sites produced illusory "rainbows". These observa- tions provide causal evidence, based on subjective report, that stimulation of category- cortex can induce highly specific perceptual changes.
Inspired by this approach, we aimed visualize the perceptual consequences of stimulation in our mosel- framework. Papale et al. (2024) recently introduced a method for reconstructing per- ceived images by projecting neural activity patterns into the latent space of generative adversarial network (GAN). Here we adapted this technique to examine the effects of perturbations in face- selective regions of our topographic model.
Mapping model states to image space. We trained linear mapping from the deepest layer of the topographic model (model inferior temporal cortex, 25,088 units) into the latent space of the GAN used for stimulus generation (512 dimensions). This mapping was calibrated 30,000 GAN- generated images, enabling us to project topographic model activity states into image space (for details, see Appendix Fig. 9). Reconstructions of unperturbed responses (simulated stimulation current= OμaA) closely matched the original stimuli (ground truth), confirming that the mapping preserved key visual content (see two leftmost columns "ground truth' vs. 'simulated stimulation current OμAA in Appendix Figs. 12,13,15).
Qualitative stimulation effects. We then perturbed model sites with high face-selectivity as defined by functional face localizer from neuroscience (Stigliani et al., 2015) while presenting objects in an independent set of 5,000 images not used establish the linear mapping between the topographic model and the GAN. In several cases, the reconstructions revealed face-like features superimposed on the original object, resembling the "facephenes" reported in human stimulation studies (Schalk et al., 2017). For example, stimulating a site at the center of a face-selective region in model inferior temporal cortex with simulated current level of 1000 μu added an illusory second face to an image of a cat or increased the area of image occupied by bear's face (Fig. 5). Similar effects were observed for other objects, whereas stimulation at control sites with low face-selectivity did not reli- ably introduce such face-like structure. To provide an overview, of stimulation site coordinates corresponding to varying levels of selectstlettvity of the underlying simulated cortical sheet and simulated current levels in the appendix (Appendix Figs. 12,13).
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Interprefation. These visualizations provide an interpretable window into the otherwise inaccessi- ble perceptual consequences of microstimulation. They suggest that activating sele-selective regions can bias representations of unrelated objects toward the preferred category, echoing the patient ports of "facephenes".
5 DISCUSSION
We introduce model-guided framework that links topographic deep networks, in-silico perturba- tions, and an explicit model-to-monkey electrode mapping to steer primate visual behavior via mi- crostimulation in inferior temporal (IT) cortex. Across two animals, model-derived combinations of stimulation site and image sequence yield positive correlations between model and monkey behavior in experiment 1, and lead to a ciiuultioon-rriver invo behavioral shift in experiment 2. Together, these findings establish a proof-of proof-of-principlee principli: topographic models with perturbation modules can guide causal interventions that bias in-vivo hehavior in response to complex visual objects.
Limitations. The main limitation of this study is the degrading signal that prevents more thorough testing to support our claim that model-guidance can steer primate behavior. With stable signal quality we could have performed additional experiments in both animals to test whether further im- provements on our model-guided microstimulation framework result in larger effect sizes than those describe. Without additional experiments our results are split between two experimental ses- sions, where either the monkey behavioral effect is not significantly different from zero (experiment 1), or where there is no clear correlation between model and monkey behavior (experiment 2).
Next steps. Additional experiments would allow for further baselines, e.g. testing whether a random selection of both stimulation site and image sequence does indeed not result the same behavioral changes we observed. Future experiments could further investigate alternative topographic models et al., 2023; Deb et al., 2025), including the relevance of topography the first place, how to best perform model-brain mapping, and details of the perturbation module (additive vs. multiplicative modulation, optimal current level, single- vs. multi-site stimulation).
Toward clinical impact. By shifting the target from early retinotopic codes to higher-level object codes and using models to plan interventions, our framework outlines a computational backbone for next-generation visual prosthetics aimed restoring percepts of complex visual objects. In other words, we believe is possible that IT representations are not only necessary, but sufficient to rep- resent complex visual objects such as faces and scenes. If so, the effect of inducing activity patterns in IT would not be limited to changes at a categorical level, but could capture a more fine-grain level allowing for e.g. the modulation of single face-features by stimulating face-feature-selettve regions (Issa & DiCarlo. 2012). Whether this holds true or whether simultaneous stimulation of earlier visual areas is required elicit percepts of complex visual objects on detailed level, is, in our eyes, in our eyes an empirical question. More broadly, model-guided stimulation may be appli- cable beyond vision for example, selecting input stimuli and stimulation patterns for a range of causal intervention techniques such as microstimulation, but also transcranial magnetic stimulation, or focused ultrasound to diagnose and treat neuropsychiatric disorders.
6 CONCLUSION
By aligning topographic models to higher-level visual cortex, optimizing stimulation sites and stim- uli in-silico, and testing the -predccde experimental parameters in-vivo, we establish practi- cal model-in-the-lle -n-the-loop framework for guiding causal interventions in vision. Model predictions were associated with dimulation-evooke shifts in behavioral responses, and with a bias along targeted perceptual dimension. Image reconstructions from perturbed model activity further illustrated per- ceptual consequences, with stimulation at face-selective sites biasing representations toward faces. Together, these results define a pipeline in which topographic models with perturbation modules inform experimental design and predict behavioral outcomes. Our approach might extend to more advanced protocols such as multi-site stimulation, and to other causal intervention techniques. By targeting higher-level visual regions rather than early visual cortex, our framework offers a compu- tational foundation for prosthetic strategies aimed at eliciting richer, object-level visual experiences and supports closed-loop optimization with translational promise.
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ACKNOWLEDGMENTS
We thank Picter Roelfsema and the EPFL NeuroAl lab for useful discussions. We are grateful for funding from Schmidt Sciences (G-25-69784), SNF (10.003.772), SNF Spark (CRSK-3.228579) and ETH Domain Open Research Data (5th and 6th call).
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A
APPENDIX
ImageNet
ecoset
LFW
VGGFaces2
# of instances
5
10
3
10
10
5
Table 1: Training data of candidate models. Before model selection (see Sec. A.1) models
trained on different combinations of image sets. We refer to two models with the same architecture trained with the same hyperparameters and on the same sets of images and only differing in their set of initial weights as two model instances.
A.1 OPTIMIZING MICROSTIMULATION IN MONKEYS THROUGH SIMULATIONS OF EXPERIMENTS IN MODELS
Passive viewing data recording. Two days prior to stimulation experiments we conducted passive viewing sessions to calibrate the models. Each monkey passively viewed 4,000 reference images randomly sampled from GAN latent space while we recorded activity from all available IT electrodes in each animal.
Stimulation site pre-selection in monkey array. We selected electrodes in monkey arrays based on signal quality (split-half reliability across 4,000 reference images) and a spatial constraint avoiding tissue damage (minimum spacing of 1.6mm (experiment 1) 1.2mm (experiment 2) Manhattan distance between any two electrodes used in experiments).
Selection of model, and of monkey array. For each model instance, we computed valss-valddtted linear predictivity from model inferior temporal cortex to monkey arrays using the 4,000 reference images. We then selected the best combinations of monkey array and model with regard to the expected behavioral outcome.
Stimulus generation. We used pale et al. 2024 to generate sequences of 7 images optimized to modulate neural activation level at a targeted stimulation site in monkey inferior temporal cortex.
approach pioneered by Pa-
Placing simulated Utah array on the model cortical sheet. We identified the simulated Utah array location and orientation on the model equivalent of inferior temporal cortex that best correlate with monkey array of interest using a subset of the responses to the 4000 reference images.
Ranking image sequences by selectivity index. We sorted stimulation site-specific image se- quences by selectivity index reflecting its ability to modulate neural activation levels at the targeted site in monotonically increasing or decreasing way. By favoring monotonic changes, we attempt to bring the neural changes resulting from visual stimulation as close possible to the neural changes resulting from the stimuation.
In-silico perturbation experiment and behavioral readout. We performed the experiment in the model using the selected combination of image sequence and stimulation site in model IT. We considered the model behavioral response (AAUC of psychometric curves: perturbed unperturbed trials) as the prediction of the monkey behavioral response.
Mapping from model to monkey stimulation device. We projected model stimulation sites yield- ing strongest behavioral model responses to the monkey cortex using the 1:1 mapping between simulated model and monkey electrodes used for placing the simulated Utah array (see above).
Model-guided microstimulation in monkeys. Monkeys performed the delayed match-to moatch-to-sampe to-samlle- task using the model-selected stimulation sites and image sequences.
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Stimulation
Passive viewing data recording
site pre-selection (signal reliability)
time
fixation (300ms
fication (200ms)
(200ms)
Selection of model, and monkey array
linear predictivity
topographic model candicates
fixation point
reward
monkey
repeltlions
GAN stimulus generation
Papale et al. 2024
reference
stimulus (SDs)
Placing in-silico stimulation module on model cortex
Ranking image sequences by selectivity index
model cortical
topographic model
In-silico experiment and behavioral readout
Mapping from model to monkey
micra
similarity
stimulttion
similarity
behavior
stimulation device
model
sim. >> sim. 2
sim. 1 sim. 2
sóimlus
alteraative
allerrative 2
Model-guided microstimulation in monkeys
Figure 6: Simulating and optimizing monkey behavioral responses via topographic models For details, please see list in main text.
A.2 EXPERIMENTAL PARADIGM
A.2.1 MONKEY SETUP
The experimental design follows the experimental paradigm established by Papale et al. (2024), with modifications to integrate model-guidance for stimulation site and stimulus selection (Fig. 7A). For all information on animal care and housing, surgeries for implanting stimulation devices, elec- trophysiology including multi-unit-activity pre-processing, intracortical microstimulation stimulus presentation, and ethical approval for animal testing we refer to (Papale et al., 2024).
Two male macaque monkeys were implanted with Utah arrays in posterior inferior temporal cortex and trained to perform a delayed match-to taache task. On each trial, the monkey fixated a central presented for 200ms, followed by 100ms gray sereen. The
point before sample stimulus sample stimulus was one sequence of images optimized drive the response profile of the stimulation site in the direction of the vector induced by stimulation. After this delay of 100ms, two
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choice images appeared opposite sides of the screen, corresponding to the extremes of the GAN- generated image sequence. After 400ms, the fixation point disappeared, cueing the animal to make a saccade to the image judged most similar the sample stimulus presented before. Correct responses were rewarded with juice. When the central reference image the associated GAN sequence images serves the sample stimulus, rewards delivered randomly 50% of trials avoid bias in either direction.
To assess the influence of microstimulation on visual behavior, electrical microstimulation was ap- plied in 50% of trials via one IT electrode (200ms, 50uA, 300Hz biphasic pulses) during and shortly after the sample presentation window (75-275ms after stimulus onset). Trials without stimulation served as a baseline conditions (sham), allowing direct comparison of behavioral choices with and without stimulation.
A
time focation (100 ms]
Sicaton
(300 me!
(200 ms)
alternatives (402 ms)
monkey
behavior
reward
response function
100
% of HIGHT
fication point
50
random 50%
LEFT/ RIGHT saccade
mtcrostim,
microstimulation duration: 200ms
microstim.
micro-
similarity
etiullttion
similarity
% of Cam cm
model
sim. 1> sim. 2
50
n.a.
sim. sim. 2
stimulus
attemative
atternative
stmuluus (SDs)
Figure 7: Experimental setup in monkey and model. A) Monkeys were trained to perform visual delayed saacc-tt-sampl--tae The animals had to select which of two alternative images they perceive more similar to single stimulus shown the beginning of trial. Correct responses were rewarded with juice. We computed a psychometric function based on the responses across multiple stimuli. Monkey experimental design including stimulus generation in (Papale et al., 2024). B) Model simulation of monkey experiment. We used topographic model to simulate the experimental design shown in A follows. We extracted model activations in response the two alternative images and the sample stimulus. If the similarity between alternative and the sample stimulus was higher than the similarity between alternative 2 and the sample stimulus, this was scored as behavioral response towards alternative and vice versa. The psychometric response function across multiple stimuli was computed the same way as in the monkey experiment.
A.2.2 TOPOGRAPHIC MODEL SETUP
To simulate the perturbation experiment in-silico, we employed topographic vision models with an explicit perturbation module (Fig. 7B). As there no notion of time in our feed-forward models, a trial was simulated by presenting the stimulus of interest one of the seven images constituting GAN-derived sequence together with the first and the last image of that sequence. We then extracted the activations from the deepest layer and computed the similarity between the stimulus representation and each of the two extremes. The model's behavioral choice was defined by the extreme with higher similarity: if the stimulus more similar to the last image than the first, the model was scored as having chosen the last image, whereas the reverse was true was scored having chosen the first.
Trials without activating the perturbation module provided the baseline condition, corresponding to trials without microstimulation in the monkey experiment. For stimulation trials, we applied a local perturbation to the model units corresponding to the targeted cortical site during stimulus presentation, thereby simulating the effect of electrical microstimulation. We compared the resulting
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choice probabilities between baseline and stimulation conditions following the same analysis in the monkey experimental setup.
A key difference in-vivo experiments is how treat variability across trials in our models. In the monkey experiment, each trial is a repeated event in time: the same stimulus can yield different choices due biological variability such varying levels of attention, fatigue, of noise. In contrast, our models are fully deterministic: the same input image and perturbation always produce the same output. As consequence, the psychometric function derived from single image sequence is binary in shape consisting entirely of zeros, entirely of ones, or describes sharp step function rather than the smooth, graded functions typically observed in behavior or biological organisms, To approximate biological variability and thus mimic trials, we therefore consider multiple GAN-generated sequences that are all optimized modulate activity at the same monkey stimulation site. Each distinct sequence constitutes an in-silico trial, and averaging across these sequences provides a model analogue of the across-trial variability observed in the animal. We introduce variability purpose the data side so that any future model can be evaluated on the same image sequences without requiring model-specific noise mechanisms, variability would be introduced by e.g. dropout noise directly applied to weights activations, each model would require its own implementation for it to tested and compared on our data.
A.3 VISUALIZATION OF PERCEPTUAL EFFECTS OF STIMULATION
We use two complementary generative pipelines to visualize the perceptual consequences of sim- ulated microstimulation in the topographic model: GAN-based reconstruction approach and diffusion-base approach based on Stable Diffusion with IP-Adapter. In both cases, the core idea is to learn linear decoder from topographic model activity into the conditioning space of a pretrained generator, and then compare images synthesized from unperturbed versus perturbed model states.
A.3.1 GAN-BASED VISUALIZATIONS
Our GAN-based visualization procedure closely follows the Brain2GAN framework (Dado et al., 2024), with the primary difference being the source of the encoding features: we use the deepest layer of a topographic vision model as the encoder, whereas Brain2GAN uses neural recordings. As in Brain2GAN, we work in the feature-disente ceature-disentangled w-latent space of a pretrained StyleGAN-XL. generator (Sauer et al., 2022), rather than its original z-space.
Dataset construction. We first generated synthetic image dataset using a pretrained StyleGAN- XL generator. We sampled latent codes from the StyleGAN-XL. prior and mapped them through the StyleGAN-XL mapping network to obtain corresponding w-latents. For each sampled latent, image and recorded:
* the StyleGAN-XL w-latent w, E RRd, and
* the deepest-layer activation vector of the topographic vision encoder fa E IR4,
where du = 512 and d, 25,088. In total, we generated 30,000 training images and 6,000 test images. All decoders are trained exclusively on unperturbed encoder activations.
Linear mapping from topographic model to w-space. To map topographic model states into the StyleGAN-XL, latent space, we fit ridge regression from laepest-layer activations to w-latents. For training set {(fi, w.) we learn a weight matrix W e Rdd Xdy by minimizing
N
((W)
Σ
Ell w. - W + a [
where a > is the ridge regularization parameter and || ||| denotes the Frobenius norm.
The regularization parameter is selected via leave-one-out cross-validation on the 10.0000image
training set. After selecting o, we keep this linear map fixed for all subsequent visualizations.
Given new encoder state f, the corresponding predicted StyleGAN-XL w-latent is
W.
(3)
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discriminator network
no stimulation
image (612 512 x INPUT
naturalistic images
classification: of OUTPUT
generator network
topographic model
deepest layer (512 x 7 OUTPUT
stimulation
image (512 512 3)
linear mapping
OUTPUT
latent vector (1 512) INPUT
no stimulation
stimulation
Figure 8: GAN-based visualization procedure. Neural activity patterns from the deepest layer of the topographic model are linearly mapped into the latent space of generative adversarial network (GAN). This mapping enables reconstruction of unperturbed responses as well as visualization of the perceptual consequences of simulated microstimulation by comparing GAN reconstructions with and without perturbation.
Baseline reconstructions. assess the quality of the decoder, we reconstruct test images from unperturbed encoder activations. For each test image with deepest-layer feature vector fuupect (I), we compute
W uuppert (ar) W fumpent (z).
and pass w Wuppert (z) through the StyleGAN-XL synthesis network to obtain reconstructed im- age Funpert. These reconstructions (current level 0 pA) serve as the baseline for the perturbation visualizations shown in the appendix.
Perturbation conditions. To visualize the perceptual effects of microstimulation, we apply the perturbation module to the topographic model different stimulation sites and current levels. We focus on positions along the y-axis of the deepest-layer cortical sheet, with
= 42 corresponding to a highly face-selective region,
= 20 corresponding to a region with low face-selectivity,
and intermediate values (e.g. y 34) interpolating between these extremes. For each chosen site (y) and simulated current level {0, 100, 500, 1000} μA obtain perturbed deepest-layer activation
fpert (z; 1,
(5)
by applying the perturbation module the unperturbed state fumpert (x) for image x.
GAN-based visualizations. For each test image x, stimulation site and current level I, we compute
Wpert (1; 1,y) W S (x; T,y):
(6)
and synthesize the corresponding image via StyleGAN-XI. Because the generator and decoder are fixed, any systematic differences between kumpert and ppert can be attributed to the changes topo- graphic model state induced by simulated microstimulation, rather than to changes in the generative model itself.
Control perturbation ablation. As control, we test whether the observed effects depend on the spatial specificity of the perturbation pattern the topographic model. For each current level J, we first compute targeted perturbation at the maximally face-selective location Vlace = 42. We then construct a "shuffled" perturbation by randomly permuting the entries this perturbation vector across feature dimensions. This preserves the marginal distribution and overall magnitude of the
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A
no stimulation
stimulation
stimulation
lopographic model
W d
Text Enooder
IP-Adapter
topographic COOD model
CLIP Image Encoder
VAE Decoder
X
X
stimulation
W
Denoising U-Net
ZCIP linear mapping
TTOOO
Figure Diffusion-basee visualizations procedure. A) Neural activity patterns from the deepest layer of the topographic model are linearly mapped into the latent space of CLIP. B) This linear mapping allows for transforming changes in TDANN activity from simulated microstimulation into changes in CLIP latent space that use to condition the diffusion-basee image synthesis via pre- trained IP- -d-aapeer weights.
perturbation but destroys its topographic structure. The shuffled perturbation is then applied to the unperturbed deepest-layer features and visualized through the same StyleGAN-XI. generator. Com- paring targeted and shuffled perturbations allows us to dissociate effects driven by feature-selective, spatially localized stimulation from those that could arise from non-specific global modulation with matched statistics.
A.3.2 DIFFUSION- BASED VISUALIZATIONS WITH IP-ADAPTER
To complement the GAN-based reconstructions, also visualize perturbation effects using a text- to-image diffusion model. Specifically, we use Stable Diffusion v1.5 with IP-Adapter (Rombach al., 2022; Ye al., 2023), which conditions the diffusion process CLIP Radford al. (2021) vision embedding of an image addition to a text prompt.
Text prompts and generated dataset. We construct text prompts by sampling ecoset category labels (Mehrer et al., 2021) and enriching them with quality and style modifiers, for example:
"a high-quality photo of [category], studio lighting, sharp focus"
For each prompt, we run Stable Diffusion v1.5 with IP-Adapter to generate images. During genera- tion record, for each image:
the text prompt Pi.
the initial noise latent 6; that seeds the diffusion process,
the resulting synthesized image X4.
As before, split the resulting dataset into training and test sets.
Linear mapping from topographic model to CLIP vision space. To couple the topographic model the diffusion pipeline, fit ridge regression from deepest-layer activations to CLIP vision em- beddings. For a training set ((o, 3, we learn a weight matrix WELIP E Rddexdd by minimizing
L(WCLIP) Elee WELIP fel aCLIP [
where ACLIP is selected via cross- cross-validation on the training set. After training, we obtain predicted CLIP embeddings from topographic states via
è WcLmf.
(8)
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At inference time, è is used drop-in replacement for the CLIP vision encoder output in IP- Adapter.
Controlled diffusion runs. To visualize perturbation effeets for given test image and its associ- ated text prompt p and noise latent e, we proceed two stages.
Baseline (unperturbed) condition.
1. Extract the deepest-layee activation fumpert (z) from the topographic model.
2. Compute the corresponding CLIP embedding
Cumpert (a)) = WCLIP Junpert (2).
3. Run Stable Diffusion v1.5 with IP-Adapter using:
the stored text prompt
the stored noise latent and
Cumpert (2) the image-conditioning input.
This yields baseline image Zumpert that approximates the original generated image under our topographic decoder.
Perturbed condition.
1. Apply the perturbation module at a chosen stimulation site y and current level / to obtain pett (2; I, y).
2. Compute the perturbed CLIP embedding
Epert (1; I. WeLIPfperts (x; I,
(10)
3. Run Stable Diffusion with IP-Adapter again, using exactly the same text prompt and noise latent but replacing the image-conditioning embedding with (p; T.)
Because the text prompt, noise latent, and diffusion hyperparameters are held fixed between the un- perturbed and perturbed runs, any systematic differences between Sunpert and the perturbed image peert (1, y) can be attributed to changes in the topographic model state induced by simulated micros- timulation. As in the GAN-based case, we visualize these effects by varying both the stimulation site (e.g., along the face-selective axis of model IT) and the simulated current level, yielding image series that illustrate how perturbations bias the generative model's outputs.
Control perturbation ablation (diffusion). We perform analogous shuffled- pertturatpttttttiiinnnnn control in the diffusion setting. For each current level I, we reuse the targeted perturbation at the maximally face-selective location yface 42, randomly shuffle its entries across feature dimensions, and apply this shuffled perturbation to the unperturbed deepest-layer features. We then apply the same linear transformation obtain CLIP embedding and run Stable Diffusion with IP-Adapter using the same prompt and noise latent Comparing targeted and shuffled perturbations in this setting again controls for non non-specific global modulation and tests whether the emergence of face-like structure depends on the spatially structured perturbation of face-selective regions.
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A.4
PASSIVE VIEWING DATA QUALITY
monkey 1
0.3
* *
neural data mean reliability
0.25
0.2
0.15
0.1
0.05
exp. 1
exp. 2
Figure 10: Passive viewing data quality. Using the passive viewing dataset from monkey 1, we quantified the reliability of each electrode following Papale et al. (2024). For each recording site, reliability estimated by repeatedly splitting the 20 repetitions of each test image into two halves and computing the correlation between the resulting split-half response averages. Repeating this procedure yielded 190 reliability estimates per electrode, whose mean defines the electrode's reli- ability. At the population level (averaging reliability across electrodes), we observed a significant decrease from experiment 1 (February 2025) to experiment (June 2025; 6.84, 1.03x10", Cohen's 's = -0.30), indicating a decline in overall signal stability. At the single-electrode level, 735 out of 1024 channels (72%) showed significant reduction in reliability after Benjamini-Hochhber FDR correction. The mean within-electrode effect size (computed on the 190 split-half estimates per electrode) was large (Cohen's d -1.25), reflecting robust and widespread degradation of reliability over the four-month interval.
A.5
USE OF LLMS
We used ChatGPT (GPT-5, OpenAl, 2025) to help polishing the writing of this document. The authors reviewed, edited, and are fully responsible for the final content.
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monkey 1
*
Cohen's d 0.17
Linear model-monke mapping [R]
0.15
0.10
0.05
train Feb train June
test June
Figure 11: Stability of model-brain alignment across months. We quantified how stable the linear mapping between TDANN IT activations and neural responses remains the four-month interval between February (experiment 1) and June (experiment 2) 2025 using the responses to the same passive viewing stimuli presented to monkey N. For each TDANN variant and seed, fit a ridge-regression either on passive viewing data from February 2025 (experiment 1) or from June 2025 (experiment 2) and evaluated its predictions in both cases the June data using 10-fold cross-validation with image splits shared across time points. The bars show the average variance explained (IR²) across all TDANN models, seeds, and folds when the mapping was trained on February data and tested on June data (left) versus when it was both trained and tested on June data (right). The February-trainee mapping achieves R 0.186, compared R 0.182 for the June-trained mapping, i.e. about 102% of the variance explained by the within-session paradigm. Despite the decline neural reliability documented Fig. 10, the TDANN-IT mapping remains highly stable across months, suggesting that single passive-viewing session can provide model- brain alignment that remains usable over extended periods and thus reduces the need for repeated model-mapping sessions before each causal experiment.
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simulated current level (uA).
ground truth
100
500
900
1000
34
28
sdimuussi sille
y-zoordin alae::
random randonn see 3
random seed
Figure 12: Visualizing model perceptual effects simulated micrustimulation image #1. Rows (from top bottom): Y-coordinate in topographic model inferior temporal cortex (deepest layer) corresponding to high vs. low face-selectivity. Visualizing Columns: ground truth image ran- domly sampled from generaaive-advvesaralleevvv (GAN) latent space, simulated current levels: 0, 100, 500, 1000μA.
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simulated
ground truth
(μA)::
100
500
34
28
stimulation site
y-coordinate: 20
random serrdd
random seed 2
random seed
Figure 13: Visualizing model perceptual effects simulated microstimulation image #533. Rows (from top to bottom): Y-coordinate in topographic model inferior temporal cortex (deepest layer) corresponding to high vs. low face-sclectivity. Visualizing Columns: ground truth image ran- domly sampled from generaaive-adveeraiall-eewvv (GAN) latent space, simulated current levels: 0, 100, 500, 1000μA.
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simulated current level (WAje
ground butth
100
500
900
1000
42
28
stimulation site
random random y-coordinate:
Figure 14: Visualizing model perceptual effects of simulated microstimulation image #530. Rows (from top to bottom): Y-coordinate in topographic model inferior temporal cortex (deepest layer) corresponding to high vs. low face-selectivity. Visualizing Columns: ground truth image ran- domly sampled from genecative-avverarial--evvvv (GAN) latent space, simulated current levels: 0, 100, 500, 1000μAA.
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simulated level (uA)):
ground truth
100
500
900
1000
42
34
28
stimulaiion site
secordinnal 20
Fandonn seed 3
random seed 2
random seed
Figure 15: Visualizing model perceptual effects of simulated microstimulation image #3680. Rows (from top to bottom): Y-coordinate in topographic model inferior temporal cortex (deepest layer) corresponding to high vs. low face-selectivity. Visualizing Columns: ground truth image ran- domly sampled from generaaive-advvesarall-evvvv (GAN) latent space, simulated current levels: 0, 100, 500, 1000μA.
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simulated current level (wA).:
ground truth
100
500
900
1000
34
28
stimulatiion site
y-coordinate: 20
random seed
random seed 2
random seed
Figure 16: Visualizing model perceptual effects of simulated microstimulation image 1116.. Rows (from top to bottom): Y-coordinate in topographic model inferior temporal cortex (deepest layer) corresponding to high vs. low face-sclectivity. Visualizing Columns: ground truth image ran- domly sampled from aececative-advvvaaiall-eevvv (GAN) latent space, simulated current levels: 0, 100, 500, 1000μAA.
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Y:
Y: 34
Y:
Y: 20
random
random random 2
Figure 17: Visualizing model perceptual effects of simulated microstimulation image #1894. Rows (from top to bottom): Y-coordinate topographic model inferior temporal cortex (deepest layer) corresponding high vs. low face-selectivity (top bottom).The last three rows are random stimulations of model IT that follow the distribution of multiplicative factors of standard stimula- tion whose results are shown the first 4 rows. Columns: ground truth image randomly sampled from diffusion model latent space, simulated current levels: 0, 100, 500, 1000μA.
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Ground Truth
CL:0
CL: 100
500
CL: 800
CL: 1000
42
Y:34
Y:
Y:20
random
Figure 18: Visualizing model perceptual effects of simulated microstimulation image #2339. Rows (from top bottom): Y-coordinate in topographic model inferior temporal cortex (deepest layer) corresponding to high vs. low face-selectivity (top to bottom), last three rows are random stimulations of model IT that follow the distribution of multiplicative factors of standard stimula- tion whose results are shown in the first 4 rows. Columns: ground truth image randomly sampled from diffusion model latent space, simulated current levels: 0, 100, 500, 1000μA.
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Reviews:
[{"review_id": "AEQRl9rIHB", "reviewer": "Reviewer_GiCt", "summary": "This paper introduces a model-guided microstimulation framework that combines topographic deep neural networks (TDANNs), in-silico perturbation modeling, and primate behavior testing to steer visual recognition behavior in macaques. The authors target higher-level visual cortex (IT) instead of early visual areas to evoke complex object-level percepts, bypassing the limitations of traditional retinotopy-based prosthetics. They demonstrate that model-predicted stimulation sites and image sequences can bias perceptual choices in a 2AFC task, and that simulated stimulation of face-selective sites qualitatively reproduces human-like facephenes. This is a proof-of-concept for closed-loop, model-driven neural intervention in vision.", "questions": "1.Why did the model-behavior correlation collapse in Exp 2? It seems that the experimental results are not satisfactory. What were the author's remedial measures?\n\n2.Was any other stimulation baseline run? The choice of the baseline of \"no stimulation\" in this article is too weak. It is insufficient to prove the effectiveness of the method proposed in this article.\n\n3.How do you rule out that the observed bias is not due to generic attention or arousal from microstimulation? Welcome the author to share their thoughts.\n\n4.Why not test multiple current levels or multi-site stimulation?\n\n5.How sensitive are results to model choice?\n\n6.Did you test other TDANN architectures or ensemble predictions to assess robustness?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "Originality: First to use topographic ANNs to prospectively guide microstimulation in IT cortex, moving beyond retinotopy-based V1 stimulation.\n\nQuality: Rigorous model-brain alignment, GAN-based stimulus generation, and behavioral validation with correlation analysis and effect size reporting.\n\nClarity: Extremely well-structured, with intuitive visuals, clear task design, and accessible neuroscience context.\n\nSignificance: Opens a new path for next-generation visual prosthetics targeting object-level percepts, and establishes a model-in-the-loop framework for causal neural control.", "weaknesses": "1.Sample size & signal degradation: Only 2 monkeys, with one excluded from Exp 2 due to signal loss, limiting statistical power and generalizability.\n\n2.Model correlation drops in Exp 2: While behavioral bias is induced, the per-site correlation between model and monkey behavior disappears (r = 0.09), raising questions about robustness and model generalization.\n\n3.Lack of control ablation: No random stimulation baseline or non-selective site control to rule out generic attention or arousal effects.\n\n4.Stimulation parameters fixed: Only one current level (50 μA) and single-site stimulation tested; no dose-response or multi-site exploration.\n\n5.Model variability not explored: Only one TDANN instance is used for mapping; no ensemble or uncertainty quantification across model initializations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "224kcwXwV4", "reviewer": "Reviewer_LktB", "summary": "The paper proposes a practical pipeline for model-guided microstimulation of macaque inferior temporal (IT) cortex that links: 1) a spatially explicit perturbation module that translates microstimulation parameters to local activity changes, 2) topographic DNNs aligned to each animal’s IT array via passive-viewing data, and 3) a mapping that projects model-optimized sites back to electrodes for in-vivo testing. In Experiment 1, per-site model predictions of behavioral bias in a 2AFC recognition task correlate with stimulation-evoked shifts in both monkeys, although mean shift is not above zero. In Experiment 2, the authors observe a significant stimulation-driven bias in one monkey but the per-site correlation vanishes. They also visualize in-silico \"facephene-like\" effects by mapping perturbed model states into a GAN latent space. Together this is pitched as proof-of-principle that topographic models can steer causal interventions in higher-level vision.", "questions": "* What fraction of observed neural variance at and around the stimulated sites is captured by the perturbation module when fitted to pre-stimulation passive-viewing data?\n* How stable is the model-brain alignment across days. If the passive-viewing alignment is from 2 to 4 days prior, what is the drop in predictivity if reused without re-alignment?\n* Can you include ablations over decay constant, gain, clipping rmax, additive vs multiplicative perturbations ...? Which parameters most strongly affect predicted \\Delta AUC?\n* For Experiment 2, do you have sham-only and random-site controls matched in number of trials? Was there a pre-registered stopping rule or power analysis?\n* Please clarify the intended prosthetic use case. IT stimulation may bias recognition choices rather than evoke stable object percepts. Can you propose measurable milestones that connect this method to clinically meaningful endpoints?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Originality: Introduces a prospective, model-in-the-loop pipeline that uses animal-specific topographic alignment to guide microstimulation in IT. Integrates a spatial perturbation module with a topographic DNN and adds generative reconstructions for interpretable inspection. This combination is new in the context of causal tests in higher visual cortex.\n* Quality: Implements a clear alignment procedure on passive-viewing data, then prospectively tests model-chosen site-stimulus pairs in primates. Reports statistics transparently and separates exploratory modeling from in-vivo confirmation. The overall methodology is careful and reproducible in principle.\n* Clarity: Explains the 3-stage workflow with readable figures and step-wise descriptions. The mapping from electrodes to model units and the procedure for stimulus selection are described in sufficient detail to follow.\n* Significance: Provides a practical blueprint for using topographic models to steer causal interventions in the ventral stream. Establishes a path toward systematic, theory-driven stimulation studies.", "weaknesses": "* The paper does not deliver both a significant mean effect and reliable per-site predictivity within the same experimental setting. This weakens the claim that the same model instance robustly predicts and drives behavior\n* The authors report r values but not R2. For the combined correlation r=0.53, the model explains about 28% of the variance in stimulation-evoked behavioral shifts. This should be stated with confidence intervals and compared to baselines. There is also no estimate of the fraction of neural variance captured by the perturbation module in IT units\n* The current-distance form is sensible, but the paper does not report sensitivity analyses over decay constants, gain factors, clipping, or non-linearities that are known to matter in IT microstimulation. Readers need to know whether the observed behavior is robust to these choices\n* Helpful for intuition, but there is no quantitative link to percept reports or neural selectivity beyond anecdotal examples\n* Stimulation of IT is unlikely to yield stable, retinotopically anchored phosphenes or letter-like percepts. At best it may bias category representations, consistent with facephene overlays, which limits near-term translatability to clinical visual prostheses that aim for placeable, compositional percepts. The paper should temper scope and clarify that this is about biasing recognition, not restoring structured visual qualia", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "e46VTCDohd", "reviewer": "Reviewer_cYUD", "summary": "### Summary\n\nThe authors link the neuronal responses in monkey visual cortices to topographical ANN, and GAN latent space, and then using the in silico model to screen for effective experimental design that stimulation will have strong perceptual effect. Although the net perceptual effect of model predicted perturbations is not super strong, the model could in some case predict which image x perturbation site has stronger behavioral bias. \n\nAlthough many components of the pipelines (Topographic ANN, perturbation model, GAN visualization, linking) have been established before, this is still a nice combination of all of them, and a stress test of all the components. Overall, it is a worthy addition to the literature, esp. the behavior modulation effect, and future works can build on top of it!", "questions": "### Questions\n\n- Why perform linking between CNN model and brain based on GAN images instead of natural image samples? is it because the available latent codes make the visualization easier?\n- Which GAN did you use? There seems to be no details about it in the appendix. The architecture, training data etc. of GAN are critical and worth mentioning, since those are the prior of your visualization. Are they some StyleGAN2?\n    - One reason I ask is that the latent space of many GANs have intriguing geometric structures [^0,^1], so in some cases perturbation along random ish direction could produce perceptually relevant change in images, as long as it has non zero inner product with those interpretable direction.\n    - Because of it, you may want to do some random perturbation as control for Figure 5 visualization and see if they are still this interpretable.\n\n[^0]: Voynov, & Babenko, (2020). Unsupervised discovery of interpretable directions in the gan latent space, ICML\n\n[^1]: Wang, & Ponce, (2021). The geometry of deep generative image models and its applications. ICLR\n\n- Regarding the link between GAN latents and visual neural code, i feel the authors could discuss more on the related works from the Ponce et al. threads. For example, the alignment of neuronal tuning in higher visual cortices with the latent space of different GANs (DeePSim & BigGAN), which allow the neurons to steer the latent codes of GAN and generate images that maximize their activations [^2, ^3]. I feel this is one of the bases for the GAN visualization method besides Papale 2024. In those approaches, they also found some behavioral effect of the images that optimize neuron activation in GAN space [^4].\n\n[^2] Wang, Ponce, 2024, **Neural Dynamics of Object Manifold Alignment in the Ventral Stream** https://www.biorxiv.org/content/10.1101/2024.06.20.596072v1.abstract\n\n[^3] Wang, B., & Ponce, C. R. (2022). Tuning landscapes of the ventral stream. *Cell reports* [https://www.cell.com/cell-reports/fulltext/S2211-1247(22)01460-7](https://www.cell.com/cell-reports/fulltext/S2211-1247(22)01460-7) \n\n[^4] Rose, O., Johnson, J., Wang, B., & Ponce, C. R. (2021). Visual prototypes in the ventral stream are attuned to complexity and gaze behavior. *Nature communications* \n\n- Why generating sequence of 7 images and requiring the image sequence to have monotonic effect? What’s the conceptual interpretation of it? Is that the authors are hypothesizing the effect of stimulation at various strength is equivalent to this sequence of image?\n- What do we make of the null result in Sec. 4.2? Isn’t it that more stimulation sites should make a stronger correlation?\n- Sec. 4.3 is very cool visualization, although we don’t have evidence that those are what monkeys actually perceive right? This is not an objection to the paper. \nI think Afraz lab, has more extensive work testing the perceptual effect of stimulation, and it’s quite an expensive loop to validate these prediction (c.f. Perceptogram https://www.nature.com/articles/s41467-024-47356-8).\n- Simulation of biological variability “To approximate biological variability and thus mimic trials, we therefore consider multiple GAN-generated sequences that are all optimized to modulate activity at the same monkey stimulation site. Each distinct sequence constitutes an in-silico trial, and averaging across these sequences provides a model analogue of the across-trial variability observed in the animal.”\n    - It’s curious the authors simulate the neuron and choice variability by different image sequences generated by GAN. Admittedly this is a tractable way to generate variance, what’s the interpretation of it? Why not have some drop out / stochasticity in the topographical ANN?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "### Strength\n\n- Predicting the perceptual effect of visual cortical stimulation is an important and hard problem in neuroscience, and one with significant translational application value.\n- Many previous model optimized stimuli study only touches on manipulation of neuron firing, but this work advanced this direction in predicting the stimulation effects on behavior.\n- The authors did a very comprehensive work with statistical rigor, and being very honest about many weaker effects and none effects. Kudos on that!", "weaknesses": "### Weakness\n\n- **Clarity of the method**\nI feel part of methods is too vague from the current main text.\n    - Figure 6,7,8 are very clear and informative, moving some details from them to Figure 1 and Figure 2 will be great, currently Figure 1,2 are a bit too high level, and could be compressed a bit.\n    - The topological ANN part is quite detailed in the text, maybe too detailed, but GAN, the regression method and other parts are much less so.\n    - I feel mentioning and describing the task of monkey is quite important in the main text. Otherwise i don’t understand the stimulation, the sequence and perception paradigm.\n- The behavior bias seems to be relatively small and non-robust. (Fig. 4) Although, we all know monkey behavior is very noisy and hard to get large effect size, so it is what it is.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "lYPY328jk4", "reviewer": "Reviewer_pN2f", "summary": "The authors introduce a framework for guided microstimulation of the high-level visual cortex.  The authors use brain-aligned, topographic image-models as in-silico models of the brain, and prototype in-vivo microstimulation experiments based on in-silico observations.  Applying these prototype experiments in two macaque monkeys, the authors observe positive correlations between model-predicted behavior and observed monkey behavior.  The authors further report qualitative similarities between perceptions predicted by the model following microstimulation and those that have been previously reported by humans.", "questions": "- Is it necessary that multiple models are used in the mapping procedure?  What is the variability in site predictivity between these different models?\n- What was the average predictivity (e.g., correlation) between model-simulated neural site activity and measured in-vivo activity to the passive-viewing images? How do you think results may change when using models that are less/more predictive of neural activity in the brain?  Can this be quantified post-hoc?\n- As suggested, TDANNs are seemingly critical for microstimulation modeling due to current spread to neighboring neurons.  How big of a role does this play in the models evaluated in this work?  Do non-TDANN models similarly have positive behavioral correlations with the monkeys (or with the TDANN models)? This supplemental analysis could quantitatively ground the choice for TDANN models in such a framework.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "- Authors address an important open question in the neural engineering space: how to simulate neural stimulation of the visual cortex and perceptual outcome.\n- All in-silico modeling is grounded in realistic (at least, most realistic for current state of the art) modeling of microstimulation (current spread, etc.) and cortical organization (through TDANNs)\n- In experiment 1, positive correlations were observed between in-silico and in-vivo behavior changes with and without stimulations.  This provides evidence for the utility of such a closed-loop framework for studying perceptual effects of micro-stimulation.\n- In experiment 2, stimulation parameters that were predicted to be effective by the modeling framework contributed to greater changes in AUC.  If results like this can be replicated in more studies, current work in microstimulation for visual perception could benefit greatly from this experimentation strategy.", "weaknesses": "- Simulating electrode placement and alignment with each model is unclear to me.  How are different positions of an array mapped onto a 512x7x7 latent space of a model?  Since each Utah array has 96/64 electrodes, does one spatial dimension of the latent space map to multiple electrodes?  What are the limitations of this?\n- Correlation between model and monkey behavior diminishes in experiment 2.  Further discussion about potential reasons for this (e.g., is it because the TDANN is failing to predict activity accurately, too much noise in the in-vivo neural signal, etc.)  would be valuable.\n- Qualitative visualizations of in-silico perceptual effects of perturbation are an interesting proof of concept but leave many open questions in terms of how perception should change with stimulation.  For example, in Figure 5, why does a second cat face appear in the picture of a cat while the single bear face enlarges in the bear picture (as opposed to the cat’s face enlarging or another face forming on the bear, for instance)?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)