Biology subjects

Ecker, A. S.

Publications and source records attributed to Ecker, A. S..

At least 19 recordsLinked to original sources

bioRxiv2026

LVentiView: An Open-Source Software for Automated 3D Left Ventricular Mesh Reconstruction and Analysis from Cardiac MRI

Patient-specific cardiac modeling requires accurate three-dimensional representations of the left ventricle (LV) reconstructed from cardiac magnetic resonance imaging (MRI). Here, we present LVentiView, an open-source software that bridges medical imaging and cardiac simulation by automating the full pipeline from MRI segmentation to simulation-ready volumetric meshes, with integrated tools for volumetric analysis and regional myocardial thickness calculation. We validate LVentiView on the Sunnybrook Cardiac Dataset, comprising healthy subjects and three cardiac pathologies. LVentiView achieves blood pool segmentation at the inter-expert level. The generated meshes are verified by comparing LV volumes extracted from the meshes to those computed from expert manual segmentation masks, with volumes and cardiac parameters agreeing within inter-expert variability across all four cardiac pathologies. In addition, mesh-derived regional thickness maps capture pathology-specific patterns, including wall thickening in hypertrophic cases. LVentiView is freely available on GitHub and provides an accessible, validated foundation for patient-specific cardiac modeling. HighlightsO_LILVentiView automates the full pipeline from MRI segmentation to simulation-ready meshes. C_LIO_LIMesh-derived cardiac volumes and parameters match expert manual segmentation accuracy. C_LIO_LIThickness maps capture pathology-specific patterns, validating geometrical fidelity. C_LIO_LISegmentation runs at {approx} 0.07 s per slice; meshing under 3 min per frame. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/727166v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@f10d08org.highwire.dtl.DTLVardef@18eab94org.highwire.dtl.DTLVardef@1a298e9org.highwire.dtl.DTLVardef@1e52347_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Interpreting convolutional neural networks to study wide-field amacrine cell inhibition in the retina

Wide-field amacrine cells (ACs) play a unique role in retinal processing by integrating visual information across a large spatial area. Their inhibitory influence has been implicated in multiple retinal functions such as differential motion detection and the suppression of retinal activity during eye movements. However, a coherent understanding of their general function is lacking due to difficulties in directly recording from these cells and identifying effective visual stimuli to activate them. In this study, we used convolutional neural networks (CNNs) to investigate widefield inhibition mediated by wide-field ACs in the marmoset retina. We trained CNNs to mimic the function of the retina by predicting retinal ganglion cell (RGC) population responses to naturalistic movie stimuli and optimising the most exciting inputs (MEIs) to visualise RGCs receptive field (RF) structures. We then optimized suppressive surrounds beyond classical RGC RF boundaries, intended to capture the inhibitory effect of wide-field ACs on RGC activity. These optimized surrounds reduced MEI-elicited activity by 10% to 30%, demonstrating that CNNs not only mimic retinal responses but can also reveal hidden computational aspects of wide-field inhibition. However, suppression strength and generalization varied across architectures and datasets, indicating potential model-specific effects, high-lighting the importance of cautious interpretation. Overall, our approach illustrates how interpretability methods applied to artificial neural networks can offer new hypotheses regarding biological retinal computation, paving the way for targeted experimental validation. The code is available here.

neuroscience↗

bioRxiv2025

Foraging competence and scrounging tolerance enhance social relationships in a socially tolerant wild primate

Social interactions are crucial for individual health and ultimately fitness, making the choice of social partners particularly important. Previous research has shown that individuals who succeed in foraging tasks often receive increased affiliation from group members. Similarly, in a social learning context, individuals who possess valuable information become more attractive social partners. Thus, an individuals role in a foraging context-specifically, whether it is a successful producer-can influence its social relationships. Therefore, we examined the interplay between social learning, producing and scrounging behavior, and social relationships in four groups of wild redfronted lemurs (Eulemur rufifrons). We conducted an open diffusion experiment with food boxes that required animals to learn one of two techniques to open them. 27 out of 29 individuals participated in the experiment, 24 interacted with the boxes and 16 learned to open them. Initial success was better predicted by use of individual than social information, i.e., manipulating the food boxes vs. time spent watching successful individuals or scrounging. Older males were less successful than females. Scrounging occurred in about 26% of events, with on average 1.3 individuals scrounging. The technique used, age and sex of the producer did not predict scrounging frequency. Learners and males scrounged more often than non-learners and females. Among learners, less successful individuals scrounged more often and this effect was more pronounced in males. More successful individuals and those that were scrounged more often received more affiliative behavior. Thus, cognitive skills and scrounging tolerance may strengthen social relationships in this primate species.

ecology↗

bioRxiv2025

Spatial Adaptation of Primate Retinal Ganglion Cells between Artificial and Natural Stimuli

The retina encodes a broad range of stimuli, adapting its computations to features like brightness, contrast, or motion. However, it is unclear to what extent it also adapts to spatial frequency content - as theories of efficient coding would predict - for instance, when switching between natural scenes and white noise. To address this, we analyzed neural activity of marmoset retinal ganglion cells (RGCs) in response to white noise and naturalistic movie stimuli. We trained linear-nonlinear models on both stimuli, evaluated their performance and compared their receptive fields (RFs) across the stimulus domains. We found that the models with spatial filters trained on either one of the stimulus ensembles were not able to predict the neural activity on the other as accurately as the models trained on the target stimulus. This suggests that spatial processing adapts to stimulus statistics. Different RGC types exhibited distinct changes: the midget OFF cells RFs became enlarged under natural movie statistics, resulting in a lower cutoff frequency. Parasol cells did not change their RF size significantly. Large OFF cells RFs decreased in size. All cell types exhibited stronger surrounds under natural movies, resembling the whitening filters predicted by efficient coding. However, quantifying the effect of the filter adaptation on the stimulus power spectrum showed a significant contribution towards whitening only in ON parasol cells. The whitening effect emerged regardless of the training stimulus. These results suggest that while RGCs adapt to the spatial frequency content of the input, efficient coding can only partially account for this adaptation. Significance statementNatural scenes differ from artificial stimuli like white noise, in spatial frequency structure. How the retina adapts to these differences remains unclear. To explore this, we studied responses of four primate retinal ganglion cell types to artificial and natural stimuli. Our results show that some cell types, like midget cells, enlarge their receptive fields and surrounds under natural stimuli, while others, like parasol cells, only enhance surrounds. These changes align qualitatively with the efficient coding theory, which posits redundancy reduction. However, in three cell types, the enhanced surrounds did not significantly whiten responses to natural stimuli, contrary to efficient coding predictions. These findings challenge how fully efficient coding explains retinal adaptation, suggesting that other principles underlie the processing of visual inputs.

systems biology↗

bioRxiv2024

PriMAT: A robust multi-animal tracking model for primates in the wild

O_LIDetection and tracking of animals is an important first step for automated behavioral studies using videos. Animal tracking is currently done mostly using deep learning frameworks based on keypoints, which show remarkable results in lab settings with fixed cameras, backgrounds, and lighting. However, multi-animal tracking in the wild presents several challenges such as high variability in background and lighting conditions, complex motion, and occlusion. C_LIO_LIWe propose a multi-animal tracking model, PriMAT, for nonhuman primates in the wild. The model learns to detect and track primates and other objects of interest from labeled videos or single images using bounding boxes instead of keypoints. Using bounding boxes significantly facilitates data annotation and robustness. Our one-stage model is conceptually simple but highly flexible, and we add a classification branch that allows us to train individual identification. C_LIO_LITo evaluate the performance of our model, we applied it in two case studies with Assamese macaques (Macaca assamensis) and redfronted lemurs (Eulemur rufifrons) in the wild. We show that with only a few hundred frames labeled with bounding boxes, we can achieve robust tracking results. Combining these results with the classification branch for the lemur videos, our model shows an accuracy of 84% in predicting lemur identities. C_LIO_LIOur approach presents a promising solution for accurately tracking and identifying animals in the wild, offering researchers a tool to study animal behavior in their natural habitats. Our code, models, training images, and evaluation video sequences are publicly available1, facilitating their use for animal behavior analyses and future research in this field. C_LI

animal behavior and cognition↗

bioRxiv2024

Convolutional neural network models of the primate retina reveal adaptation to natural stimulus statistics

Understanding the nonlinear encoding mechanisms of retinal ganglion cells (RGCs) in response to various visual stimuli presents a central challenge in neuroscience, driving the development of increasingly complex predictive models. Here, we systematically evaluate linear-nonlinear (LN) models - applying various regularization techniques - and convolutional neural networks (CNNs) of increasing depth, to predict RGC responses to white noise and natural movies. Our analysis includes publicly available datasets from marmoset and salamander retinas. We demonstrate that LN models, when equipped with appropriate inductive biases, can achieve robust predictive performance on neural responses to both white noise and natural movie stimuli. The optimal inductive biases vary substantially across datasets and stimulus types, indicating that the LN models performance is susceptible to these choices. This warrants care when using LN models as baselines: their performance is not fixed, and inappropriate design choices can lead to "unfair" comparisons. However, even in the optimal inductive bias scenario, CNNs consistently outperform LN models across conditions, confirming the advantage derived from their nonlinear representation capacity. Investigating cross-stimulus generalization, we observe that models trained on white noise generalize better to natural movies than vice versa. Notably, LN models exhibit a smaller performance gap between in-domain and cross-domain predictions compared to CNNs, suggesting that the nonlinear processing captured by CNNs is more stimulus-specific. Overall, this study provides valuable benchmarks and methodological insights for neuroscientists designing predictive models of retinal encoding.

neuroscience↗

bioRxiv2023

Deep learning-driven characterization of single cell tuning in primate visual area V4 unveils topological organization

Deciphering the brains structure-function relationship is key to understanding the neuronal mechanisms underlying perception and cognition. The cortical column, a vertical organization of neurons with similar functions, is a classic example of primate neocortex structure-function organization. While columns have been identified in primary sensory areas using parametric stimuli, their prevalence across higher-level cortex is debated, particularly regarding complex tuning in natural image space. However, a key hurdle in identifying columns is characterizing the complex, nonlinear tuning of neurons to high-dimensional sensory inputs. Building on prior findings of topological organization for features like color and orientation, we investigate functional clustering in macaque visual area V4 in non-parametric natural image space, using large-scale recordings and deep learning-based analysis. We combined linear probe recordings with deep learning methods to systematically characterize the tuning of >1,200 V4 neurons using in silico synthesis of most exciting images (MEIs), followed by in vivo verification. Single V4 neurons exhibited MEIs containing complex features, including textures and shapes, and even high-level attributes with eye-like appearance. Neurons recorded on the same silicon probe, inserted orthogonal to the cortical surface, often exhibited similarities in their spatial feature selectivity, suggesting a degree of functional organization along the cortical depth. We quantified MEI similarity using human psychophysics and distances in a contrastive learning-derived embedding space. Moreover, the selectivity of the V4 neuronal population showed evidence of clustering into functional groups of shared feature selectivity. These functional groups showed parallels with the feature maps of units in artificial vision systems, suggesting potential shared encoding strategies. These results demonstrate the feasibility and scalability of deep learning-based functional characterization of neuronal selectivity in naturalistic visual contexts, offering a framework for quantitatively mapping cortical organization across multiple levels of the visual hierarchy.

neuroscience↗

bioRxiv2023

Towards a Foundation Model of the Mouse Visual Cortex

The complexity of neural circuits makes it challenging to decipher the brains algorithms of intelligence. Recent break-throughs in deep learning have produced models that accurately simulate brain activity, enhancing our understanding of the brains computational objectives and neural coding. However, these models struggle to generalize beyond their training distribution, limiting their utility. The emergence of foundation models, trained on vast datasets, has introduced a new AI paradigm with remarkable generalization capabilities. We collected large amounts of neural activity from visual cortices of multiple mice and trained a foundation model to accurately predict neuronal responses to arbitrary natural videos. This model generalized to new mice with minimal training and successfully predicted responses across various new stimulus domains, such as coherent motion and noise patterns. It could also be adapted to new tasks beyond neural prediction, accurately predicting anatomical cell types, dendritic features, and neuronal connectivity within the MICrONS functional connectomics dataset. Our work is a crucial step toward building foundation brain models. As neuroscience accumulates larger, multi-modal datasets, foundation models will uncover statistical regularities, enabling rapid adaptation to new tasks and accelerating research.

neuroscience↗

bioRxiv2023

Bipartite invariance in mouse primary visual cortex

A primary goal of sensory systems is to extract robust and meaningful features that are invariant to variations in the sensory input. Characterizing these invariances at the neuronal level is crucial for understanding how the visual system supports generalization, but the high-dimensional nature of ecological stimuli poses major challenges. Consequently, our understanding of how the brain represents invariances has historically depended on a few examples, such as phase invariance to grating stimuli in V1 complex cells. Here, we leverage the inception loop paradigm --iterating between large-scale recordings, deep learning neuronal predictive models, and in silico experiments with in vivo verification--to characterize neuronal invariances in mouse V1. Using a neuronal predictive model, we synthesized Diverse Exciting Inputs (DEIs) that strongly drive target neurons while differing substantially in image space. These DEIs revealed a novel bipartite invariance: one portion of the receptive field encodes shift-invariant, high-frequency textures, while the other encodes a fixed, low-frequency spatial pattern. This subfield division aligned with object boundaries defined by spatial frequency differences in highly activating stimuli, suggesting bi-partite invariance contributes to segmentation. Our analysis of computational models and anatomical data from the MICrONS dataset revealed a hierarchical organization of excitatory neurons in mouse V1 Layers 2/3: We found that postsynaptic neurons exhibited greater invariance than their presynaptic inputs, while neurons with lower invariance formed more connections. These findings suggest a synaptic-level hierarchy that progressively increases neural invariance within the primary visual cortex. Intriguingly, similar high-low frequency bipartite patterns strongly activate certain units in artificial neural networks, suggesting that universal visual representations govern both biological and artificial systems, potentially aiding in the extraction of visual features from complex backgrounds.

neuroscience↗

bioRxiv2023

Pattern completion and disruption characterize contextual modulation in mouse visual cortex

Vision is fundamentally context-dependent, with neuronal responses influenced not just by local features but also by surrounding contextual information. In the visual cortex, studies using simple grating stimuli indicate that congruent stimuli--where the center and surround share the same orientation--are more inhibitory than when orientations are orthogonal, potentially serving redundancy reduction and predictive coding. Understanding these center-surround interactions in relation to natural image statistics is challenging due to the high dimensionality of the stimulus space, yet crucial for deciphering the neuronal code of real-world sensory processing. Utilizing large-scale recordings from mouse V1, we trained convolutional neural networks (CNNs) to predict and synthesize surround patterns that either optimally suppressed or enhanced responses to center stimuli, confirmed by in v ivo experiments. Contrary to the notion that congruent stimuli are suppressive, we found that surrounds that completed patterns based on natural image statistics were facilitatory, while disruptive surrounds were suppressive. Applying our CNN image synthesis method in macaque V1, we discovered that pattern completion within the near surround occurred more frequently with excitatory than with inhibitory surrounds, suggesting that our results in mice are conserved in macaques. Further, experiments and model analyses confirmed previous studies reporting the opposite effect with grating stimuli in both species. Using the MICrONS functional connectomics dataset, we observed that neurons with similar feature selectivity formed excitatory connections regardless of their receptive field overlap, aligning with the pattern completion phenomenon observed for excitatory surrounds. Finally, our empirical results emerged in a normative model of perception implementing Bayesian inference, where neuronal responses are modulated by prior knowledge of natural scene statistics. In summary, our findings identify a novel relationship between contextual information and natural scene statistics and provide evidence for a role of contextual modulation in hierarchical inference.

neuroscience↗

bioRxiv2022

A chromatic feature detector in the retina signals visual context changes

The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour-opponency in transient Suppressed-by-Contrast RGCs (tSbC), a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by heador eye-movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

neuroscience↗

bioRxiv2022

Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks

Responses to natural stimuli in area V4 - a mid-level area of the visual ventral stream - are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional role of V4 in object classification. However, we currently do not know if and to what extent V4 plays a role in solving other computational objectives. Here, we investigated normative accounts of V4 (and V1 for comparison) by predicting macaque single-neuron responses to natural images from the representations extracted by 23 CNNs trained on different computer vision tasks including semantic, geometric, 2D, and 3D types of tasks. We found that V4 was best predicted by semantic classification features and exhibited high task selectivity, while the choice of task was less consequential to V1 performance. Consistent with traditional characterizations of V4 function that show its high-dimensional tuning to various 2D and 3D stimulus directions, we found that diverse non-semantic tasks explained aspects of V4 function beyond those captured by individual semantic tasks. Nevertheless, jointly considering the features of a pair of semantic classification tasks was sufficient to yield one of our top V4 models, solidifying V4s main functional role in semantic processing and suggesting that V4s affinity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.

neuroscience↗

bioRxiv2022

Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex

More than a dozen excitatory cell types have been identified in the mouse primary visual cortex (V1) based on transcriptomic, morphological and in vitro electrophysiological features. However, the functional landscape of excitatory neurons with respect to their responses to visual stimuli is currently unknown. Here, we combined large-scale two-photon imaging and deep learning neural predictive models to study the functional organization of mouse V1 using digital twins. Digital twins enable exhaustive in silico functional characterization providing a bar code summarizing the input-output function of each neuron. Clustering the bar codes revealed a continuum of function with around 30 modes. Each mode represented a group of neurons that exhibited a specific combination of stimulus selectivity and nonlinear response properties such as cross-orientation inhibition, size-contrast tuning and surround suppression. These non-linear properties were expressed independently spanning all possible combinations across the population. This combinatorial code provides the first large-scale, data-driven characterization of the functional organization of V1. This powerful approach based on digital twins is applicable to other brain areas and to complex non-linear systems beyond the brain.

neuroscience↗

bioRxiv2019

Learning Divisive Normalization in Primary Visual Cortex

Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed. Author summaryDivisive normalization (DN) is a computational building block throughout sensory processing in the brain. We currently lack an understanding of what role this normalization mechanism plays when processing complex stimuli like natural images. Here, we use modern machine learning methods to build a general DN model that is directly informed by data from primary visual cortex (V1). Contrary to high-predictive deep learning models, our DN-based models parameters offer a straightforward interpretation of the nature of normalization. Within the receptive field, we found that neurons responding strongly to a specific orientation are preferentially normalized by other neurons that are highly active for similar orientations, rather than being normalized by all neurons as it is currently assumed by textbook models.

neuroscience↗

bioRxiv2019

The temporal structure of the inner retina at a single glance

The retina decomposes visual stimuli into parallel channels that encode different features of the visual environment. Central to this computation is the synaptic processing in a dense and thick layer of neuropil, the so-called inner plexiform layer (IPL). Here, different types of bipolar cells stratifying at distinct depths relay the excitatory feedforward drive from photoreceptors to amacrine and ganglion cells. Current experimental techniques for studying processing in the IPL do not allow imaging the entire IPL simultaneously in the intact tissue. Here, we extend a two-photon microscope with an electrically tunable lens allowing us to obtain optical vertical slices of the IPL, which provide a complete picture of the response diversity of bipolar cells at a \"single glance\". The nature of these axial recordings additionally allowed us to isolate and investigate batch effects, i.e. inter-experimental variations resulting in systematic differences in response speed. As a proof of principle, we developed a simple model that disentangles biological from experimental causes of variability, and allowed us to recover the characteristic gradient of response speeds across the IPL with higher precision than before. Our new framework will make it possible to study the computations performed in the central synaptic layer of the retina more efficiently.

neuroscience↗

bioRxiv2018

Inception in visual cortex: in vivo-silico loops reveal most exciting images

Much of our knowledge about sensory processing in the brain is based on quasi-linear models and the stimuli that optimally drive them. However, sensory information processing is nonlinear, even in primary sensory areas, and optimizing sensory input is difficult due to the high-dimensional input space. We developed inception loops, a closed-loop experimental paradigm that combines in vivo recordings with in silico nonlinear response modeling to identify the Most Exciting Images (MEIs) for neurons in mouse V1. When presented back to the brain, MEIs indeed drove their target cells significantly better than the best stimuli identified by linear models. The MEIs exhibited complex spatial features that deviated from the textbook ideal of V1 as a bank of Gabor filters. Inception loops represent a widely applicable new approach to dissect the neural mechanisms of sensation.

neuroscience↗

bioRxiv2018

Stimulus domain transfer in recurrent models for large scale cortical population prediction on video

To better understand the representations in visual cortex, we need to generate better predictions of neural activity in awake animals presented with their ecological input: natural video. Despite recent advances in models for static images, models for predicting responses to natural video are scarce and standard linear-nonlinear models perform poorly. We developed a new deep recurrent network architecture that predicts inferred spiking activity of thousands of mouse V1 neurons simulta-neously recorded with two-photon microscopy, while accounting for confounding factors such as the animals gaze position and brain state changes related to running state and pupil dilation. Powerful system identification models provide an opportunity to gain insight into cortical functions through in silico experiments that can subsequently be tested in the brain. However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli. We investigated these domain transfer properties in our model and find that our model trained on natural images is able to correctly predict the orientation tuning of neurons in responses to artificial noise stimuli. Finally, we show that we can fully generalize from movies to noise and maintain high predictive performance on both stimulus domains by fine-tuning only the final layers weights on a network otherwise trained on natural movies. The converse, however, is not true.

neuroscience↗

bioRxiv2018

Image content is more important than Bouma’s Law for scene metamers

We subjectively perceive our visual field with high fidelity, yet large peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (crowding). A recent paper proposed a model of the mid-level ventral visual stream in which neural responses were averaged over an area of space that increased as a function of eccentricity (scaling). Human participants could not discriminate synthesised model images from each other (they were metamers) when scaling was about half the retinal eccentricity. This result implicated ventral visual area V2 and approximated \"Boumas Law\" of crowding. It has subsequently been interpreted as a link between crowding zones, receptive field scaling, and our rich perceptual experience. However, participants in this experiment never saw the original images. We find that participants can easily discriminate real and model-generated images at V2 scaling. Lower scale factors than even V1 receptive fields may be required to generate metamers. Efficiently explaining why scenes look as they do may require incorporating segmentation processes and global organisational constraints in addition to local pooling.

neuroscience↗