Biology subjects

Groen, I. I. A.

Publications and source records attributed to Groen, I. I. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Brain alignment in deep neural networks emerges early and independently of object classification

Deep convolutional neural networks are leading models of biological vision, largely because of their strong brain alignment: their features predict neural responses better than earlier models. Yet they are believed to recognize objects differently, relying on texture where humans rely on shape and failing on perturbations humans handle effortlessly. What then does alignment reflect? Tracking three architectures densely during training, we find that alignment with human fMRI, EEG, and macaque electrophysiology is already largely present at initialization, when networks classify at chance, and reaches a plateau within one to five epochs; thereafter it changes only modestly while classification accuracy continues to climb to 75%. Network lesioning shows that a kernel's contribution to alignment is essentially uncorrelated with its contribution to classification throughout training. Brain-network alignment therefore appears to reflect the structure of the visual environment both systems encode, rather than a shared solution for classification.

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bioRxiv2026

Normalization accounts for temporal dynamics in human somatosensory cortex

Sensory processing is fundamentally shaped by stimulation history. For example, in visual cortex, neural responses are reduced for repeated or sustained stimuli (adaptation). These phenomena are well characterized and effectively modeled by divisive normalization. We asked whether these same computational principles govern somatosensory processing. We used fMRI (6 participants) and intracranial electroencephalography (iEEG, 2 participants) to measure responses to time-varying vibrotactile stimuli in human somatosensory cortex. Stimuli consisted of single- and paired-pulses with durations and interstimulus intervals ranging from 0.05 to 1.2 s. We extracted BOLD time courses to capture neural response amplitudes, and high-frequency iEEG broadband envelopes to capture fast neural dynamics. In both experiments, we observed pronounced sub-additive temporal summation. Responses to longer or repeated stimuli were consistently lower than predicted by linear integration. Computational modeling revealed that divisive normalization models outperformed linear models in cross-validated accuracy across both datasets. These results demonstrate that somatosensory temporal dynamics closely mirror those in the visual system. Our findings suggest that the nervous system employs similar computational principles across modalities to encode sensory information across time. Significance statementHow the brain integrates sensory information over time is a fundamental question in neuroscience. While nonlinear temporal integration is well documented in visual cortex, it has not been extensively mapped in the human somatosensory system. By combining fMRI with intracranial EEG in humans, we demonstrate that somatosensory responses to tactile stimulation exhibit subadditive temporal summation. This nonlinearity is accurately captured by a divisive normalization model, matching observations in the visual system. Our results suggest that normalization is a canonical computation shared across different modalities to manage temporal dynamics, providing a unified framework for understanding how the brain encodes dynamic sensory stimuli.

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bioRxiv2026

Image-grounded encoding models reveal distinct temporal profiles of naturalistic object and scene processing in the human brain

In real-world vision, the human brain needs to process large amounts of information to effectively interact with its environment. It is well established that our visual system has specialized regions to process information efficiently, such as scene-, face-, and object-selective areas, which can be uncovered using functional magnetic resonance imaging (fMRI). However, functional localizers typically use artificially separated stimuli (e.g. scene backgrounds vs. object cutouts), and fMRI signals are insensitive to potential temporal processing differences. Here, we identify temporal signatures of object and scene processing in natural visual environments by building image-grounded brain-predictive encoding models of human electroencephalography (EEG) responses. In a large set of high-resolution natural images, we first separate object from scene information on a per-image basis, and then feed this information to train separate deep neural network-based encoding models to predict EEG responses to the full, intact images. We find that encoding models that receive only object information consistently exhibit a delayed temporal encoding profile compared to models that only receive scene information. Control analyses confirm the robustness of this delayed object encoding, showing that consistent selection of object or scene information is needed to achieve high encoding performance. Using these distinct encoding profiles as processing templates, we reveal which image parts are seen by the brain as an object, versus a background scene element. These findings demonstrate that object and scene processing in the human brain unfold differentially over time and indicate that image-grounded encoding models are powerful tools for isolating components of naturalistic perception. SignificanceThe neural mechanisms underlying visual perception are typically studied by experimentally separating different kinds of information, for example presenting either isolated objects or scene backgrounds. Since information naturally co-occurs in the world -- objects are typically embedded within scenes -- this artificial separation reduces ecological validity. We leverage image-computable neural encoding models to identify distinct visual processing profiles within ecologically valid, natural images. Models that receive stimuli in which only objects are visible and scene information is masked predict EEG responses at later time points than models receiving the inverse, "scene-only" stimuli. By quantifying how items such as buildings, trees or the ground are encoded in EEG signals, we reveal which elements of visual environments drive neural responses to intact, real-world images.

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bioRxiv2025

Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing

Deep neural networks (DNNs) excel at predicting neural responses across the visual hierarchy 1-5, a success widely interpreted as evidence of shared object recognition computations 6,7. Yet improving DNN object recognition accuracy does not reliably increase neural predictivity 8,9, and even untrained networks predict brain responses above chance 9-11. This disconnect suggests that object recognition may not drive DNN-brain alignment. Texture-like statistics are represented in both DNNs and mid-level visual cortex 12-17. In natural images, these statistics are carried by objects and backgrounds, shaping representations and recognition in both systems 18-21. Does DNN-brain alignment reflect shared sensitivity to object-related information or texture-like statistics? To dissociate these factors, we recorded EEG from 57 participants viewing natural scenes, texture-synthesized images preserving local statistics while disrupting global form and object-only images with backgrounds removed. If alignment reflects texture-like statistics, it should peak for texture-synthesized images. If it reflects object-related processing, alignment should be strongest for natural and object-only conditions, which preserve object information. We compared EEG responses with DNN activations via weighted representational similarity analysis 22,23. Texture-synthesized images yielded the strongest DNN-EEG alignment, peaking in early responses (<200 ms) and explaining up to [~]85% of noise-ceiling-normalized explainable variance versus [~]44% for natural and [~]55% for isolated objects. Crucially, object categories were more decodable for natural and object-only images than texture-synthesized images, yet these object-rich conditions showed weaker alignment. This dissociation reveals that DNNs capture the texture-statistical component of early visual responses while failing to explain later, object-related variance. HighlightsO_LITexture-synthesized images yield the strongest DNN-brain alignment across DNNs. C_LIO_LIObject categories decode better from object-rich than texture-synthesized EEG. C_LIO_LIDNN-brain alignment reflects shared texture statistics, not object-related processing. C_LI

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bioRxiv2025

Neuronal or vascular receptive fields? On the relation between BOLD-fMRI population receptive field (pRF) estimates and cortical vascularization.

This study investigates the contribution of different vascular compartments on population receptive field (pRF) size estimates within the early visual cortex (V1, V2, and V3) using BOLD-fMRI. We employed T2*-weighted gradient-echo (GE) and T2-weighted spin-echo (SE) sequences at 7 Tesla (7T) and a multi-band GE sequence at 3T to explore how different vessel sensitivities across these sequences influence pRF modeling. Our results confirm the expected pRF size increase across eccentricity and visual areas but found no significant differences in pRF size estimates across MR sequences, voxel sizes, or field strengths. BOLD signal amplitudes were influenced by MR sequence, with largest signal changes observed for 7TGE, and amplitude increases were noted across cortical depth for GE sequences but not for SE sequences. Contrary to our hypotheses, pRF size estimates were not noticeably affected by the local vascularization, suggesting that pRFs primarily reflect neuronal activity rather than vascular compartment characteristics. Our study highlights the robust nature of pRF size estimates across various fMRI conditions and points toward the decoupling of pRF properties from vascular factors.

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bioRxiv2025

Temporal adaptation aids object recognition in deep convolutional neural networks in suboptimal viewing scenario's

The primate visual system excels in recognizing objects under challenging viewing scenarios. A neural mechanism that is thought to play a key role in this ability is rapid temporal adaptation, or the adjustment of neurons activity based on recent history. To understand how temporal adaptation may support object recognition, previous work has incorporated a variety of temporal feedback mechanisms in deep convolutional neural networks (DCNN) and explored how these mechanisms affect object recognition performance. While multiple adaptation mechanisms have been shown to impact model behavior, it remains unclear how the origin (intrinsic or recurrent) and the way the temporal feedback is integrated (additive or multiplicative) affects object recognition. Here, we compare the impact of four different temporal adaptation mechanisms on object recognition using three different task designs, including object recognition under either noise or occlusion, and in the context of novelty detection. Our results show that the effectiveness of temporal adaptation mechanisms for robust object recognition depends on the task and dataset. For objects embedded in noise, intrinsic adaptation excels with simple, high-contrast inputs, while recurrent mechanisms perform better with complex, low-contrast inputs, highlighting their focus on different visual features. Under dynamic occlusion, recurrent adaptation mechanisms exhibit a more progressive increase in performance over time, suggesting they better maintain object coherence when parts are obscured. For novelty detection, recurrent mechanisms show higher performance compared to intrinsic adaptation mechanisms, suggesting that recurrence aids in detecting global changes caused by the presentation of new objects. All together, these findings suggest that robust object recognition likely requires multiple temporal adaptation strategies in parallel to handle the diverse challenges of naturalistic visual settings.

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bioRxiv2025

Temporal misalignment in scene perception: Divergent representations of locomotive action affordances in human brain responses and DNNs

The human visual system processes scenes with remarkable speed, enabling the extraction of essential information to navigate our surroundings in a single glance. To elucidate how the brain transforms visual inputs into neural representations of navigationally relevant information, we collected electroencephalography (EEG) responses to diverse indoor and outdoor scenes along with behavioral annotations of locomotive action affordances (e.g., walking, cycling), object annotations, and low-level image features to model distinct types of scene information. Using representational similarity analysis, we examined the neural representation of locomotive action affordances over time, their co-localization within scene-selective cortex, and their computational alignment with deep neural networks (DNNs). Our results show that locomotive action affordance representations emerge within 200 ms of visual processing, showing unique contributions to EEG responses at temporally distinct time-points from objects and low-level properties. Spatiotemporal fusion with functional magnetic resonance imaging (fMRI) recordings in scene-selective brain regions reveals that both the parahippocampal (PPA) and occipital place region (OPA), but not the medial place region (MPA), contribute to locomotive action affordance representations, with a distinct temporal hierarchy between them. While DNNs exhibit good predictivity of early EEG responses, they primarily capture low-level features and show limited alignment with affordance processing. These findings reveal a temporally distinct neural representation of action affordances and highlight a limitation of current DNNs in modeling affordance perception.

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bioRxiv2024

Deep predictive coding networks partly capture neural signatures of short-term temporal adaptation in human visual cortex

Predictive coding is a leading theory of cortical function which posits that the brain continually makes predictions of incoming sensory stimuli using a hierarchical network of top-down and bottom-up connections. This theory is supported by prior work showing that PredNet, a deep learning network designed according to predictive coding principles, exhibits several characteristics of neural responses commonly observed in primate visual cortex. However, one ubiquitous neural phenomenon that has not yet been investigated is short-term visual adaptation: the adjustment of neural responses over time when exposed to static visual inputs that are either prolonged or directly repeated. Here, we examine whether PredNet exhibits two neural signatures of temporal adaptation previously observed in intracranial recordings of human participants viewing prolonged and repeated stimuli (Brands et al., 2024). We find that, like human visual cortex, PredNet adapts to static images, evidenced by subadditive temporal response summation: a non-linear accumulation of response magnitudes when prolonging stimulus durations, which results from neurally plausible transient-sustained dynamics in the unit activation time courses. However, PredNet activations also show a systematic response to stimulus offsets, which is absent in the human neural data. For repeated stimuli, PredNet shows slight response suppression for any two images presented in quick succession, but no repetition suppression, a comparatively stronger response reduction for identical than for non-identical image pairs that is robustly observed throughout human visual cortex. We show that these results are stable across multiple training datasets and two different types of loss computation. Lastly, in both PredNet and the neural data, we find a relationship between temporal adaptation and visual input properties, showing that temporally sustained activity is enhanced for more complex scenes containing clutter. All together, these results suggest that the emergent temporal dynamics in the PredNet only partly align with neural data and are linked to low-level properties of the visual input rather than high-level predictions arising from top-down processes.

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bioRxiv2024

One Hundred Neural Networks and Brains Watching Videos: Lessons from Alignment

What can we learn from comparing video models to human brains, arguably the most efficient and effective video processing systems in existence? Our work takes a step towards answering this question by performing the first large-scale benchmarking of deep video models on representational alignment to the human brain, using publicly available models and a recently released video brain imaging (fMRI) dataset. We disentangle four factors of variation in the models (temporal modeling, classification task, architecture, and training dataset) that affect alignment to the brain, which we measure by conducting Representational Similarity Analysis across multiple brain regions and model layers. We show that temporal modeling is key for alignment to brain regions involved in early visual processing, while a relevant classification task is key for alignment to higher-level regions. Moreover, we identify clear differences between the brain scoring patterns across layers of CNNs and Transformers, and reveal how training dataset biases transfer to alignment with functionally selective brain areas. Additionally, we uncover a negative correlation of computational complexity to brain alignment. Measuring a total of 99 neural networks and 10 human brains watching videos, we aim to forge a path that widens our understanding of temporal and semantic video representations in brains and machines, ideally leading towards more efficient video models and more mechanistic explanations of processing in the human brain.

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bioRxiv2024

Systematic effects of retinotopic biases and category selectivity across human occipitotemporal cortex

The organization of human visual cortex has traditionally been studied using two different methods: retinotopic mapping and category-selectivity mapping. Retinotopic mapping has identified a large number of systematic maps of the visual field, while category-selectivity mapping has identified clusters of neural populations that reliably respond more strongly to specific image categories such as objects, faces, scenes and body parts compared to other categories. While early investigations seemed to suggest that these two organizing principles were largely separated in the brain, with retinotopic maps in posterior visual cortex and category-selective regions in anterior visual cortex, recent work shows that category-selective regions overlap with retinotopic maps, giving rise to spatial visual field biases within these regions. Here, we collected fMRI responses whilst performing both retinotopic and category mapping within the same participants, allowing detailed comparison of neural tuning for space and category at the single voxel level. We use these data to evaluate two previous proposals of how retinotopic biases relate to category-selectivity: 1) complementary quadrant biases (upper vs. lower contralateral visual field) inherited from early visual cortex explain the presence of paired regions selective for the same category across lateral and ventral occipitotemporal cortex (lOTC, vOTC); and 2) eccentricity biases (center vs. periphery of the visual field) explain the presence of selectivity for different categories, specifically differentiating face-versus scene-selectivity within the ventral surface. Confirming and extending previous findings for a comprehensive set of face-, scene-, object, and place-selective regions of interest, we provide robust evidence that category-selective regions do not sample visual space uniformly, exhibiting systematic biases towards either the upper or lower field (all category regions) and center vs. periphery (face vs. place regions). Consistent with 1), we find that quadrant biases differ systematically between lateral and ventral OTC, with lateral regions showing systematic lower field biases and ventral regions showing upper field biases, differentiating regions selective for the same category in terms of their spatial bias. However, contrary to 2), we find that eccentricity tuning does not strongly predict the strength of face-or scene category-selectivity in a given voxel. Specifically, highly face-selective voxels are not solely confined to the fovea, and while most scene-selective voxels show peripheral tuning, highly scene-selective voxels actually show strong foveal tuning, particularly in anterior medial-ventral cortex. Collectively, these results demonstrate that spatial biases in category-selective cortex are widespread and robust, whilst also suggesting there is no simple relation between spatial tuning and category-selectivity.

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bioRxiv2024

BrainACTIV: Identifying visuo-semantic properties driving cortical selectivity using diffusion-based image manipulation

The human brain efficiently represents visual inputs through specialized neural populations that selectively respond to specific categories. Advancements in generative modeling have enabled data-driven discovery of neural selectivity using brain-optimized image synthesis. However, current methods independently generate one sample at a time, without enforcing structural constraints on the generations; thus, these individual images have no explicit point of comparison, making it hard to discern which image features drive neural response selectivity. To address this issue, we introduce Brain Activation Control Through Image Variation (BrainACTIV), a method for manipulating a reference image to enhance or decrease activity in a target cortical region using pretrained diffusion models. Starting from a reference image allows for fine-grained and reliable offline identification of optimal visuo-semantic properties, as well as producing controlled stimuli for novel neuroimaging studies. We show that our manipulations effectively modulate predicted fMRI responses and agree with hypothesized preferred categories in established regions of interest, while remaining structurally close to the reference image. Moreover, we demonstrate how our method accentuates differences between brain regions that are selective to the same category, and how it could be used to explore neural representation of brain regions with unknown selectivities. Hence, BrainACTIV holds the potential to formulate robust hypotheses about brain representation and to facilitate the production of naturalistic stimuli for neuroscientific experiments.

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bioRxiv2024

Spatial sampling of deep neural network features improves encoding models of foveal and peripheral visual processing in humans

Deep Neural Networks (DNNs) are increasingly being used to build encoding models to predict neural data recorded during visual stimulation. Their ability to process natural images makes them a prime candidate for studying the neural profile underlying real-world visual perception. However, there are still prominent discrepancies in how DNNs and humans process visual information. One discrepancy lies in the spatial sampling of visual input: while DNNs uniformly sample from their input at every spatial location, the human visual system samples differentially from central and peripheral regions. Here, we implement multiple spatial sampling strategies on feature maps of DNNs into encoding models that predict human EEG responses to a novel stimulus set consisting of large, high-quality natural scene images. By applying differential spatial sampling of DNN feature maps, we reveal distinct temporal profiles for encoding of peripheral vs. central information in EEG signals. Moreover, we show that a differential spatial sampling derived from the density of retinal ganglion cells yields the best performing encoding model when using DNN feature maps. We experimentally confirm this pattern by separately stimulating peripheral and central visual field regions, and demonstrate that the distinct temporal profiles for central and peripheral information are only revealed when using large-field stimuli. Together, these results show that aligning the spatial sampling of humans and DNN encoding models can improve predictions of neural data. The distinct temporal profiles for encoding of peripheral vs. central information support a global-to-local processing hierarchy of real-world vision.

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bioRxiv2024

Contrast-dependent response modulation in convolutional neural networks captures behavioral and neural signatures of visual adaptation

Human perception is robust under challenging conditions, for example when sensory inputs change over time. Temporal adaptation in the form of reduced responses to repeated external stimuli is ubiquitously observed in the brain, yet it remains unclear how repetition suppression aids recognition of novel inputs. To clarify this, we collected behavioural and electrocorticography (EEG) measurements while human participants categorized objects embedded in visual noise patterns after first viewing these patterns in isolation, inducing adaptation to the noise stimulus. We furthermore manipulated the availability of object information in the visual input by varying the contrast of the noise-embedded objects. Our results provide convergent behavioral, neural and computational evidence of a benefit of temporal adaptation on sensory representations. Adapting to a noise pattern resulted in overall faster object recognition and better recognition of objects as object contrast increased. These adaptation-induced behavioral improvements were accompanied by more pronounced contrast-dependent modulation of object-evoked EEG responses, and better decoding of object information from EEG activity. To identify potential neural computations mediating the benefits of temporal adaptation on object recognition, we equipped task-optimized deep convolutional neural networks (DCNNs) with different candidate mechanisms to adjust network activations over time. DCNNs with intrinsic adaptation mechanisms, such as additive suppression, best captured contrast-dependent human performance benefits, whilst also showing improved object decoding as a result of adaptation. Finally, adaptation effects in networks that use temporal divisive normalization, a biologically-plausible canonical neural computation, were most robust to spatial shifts, suggesting that temporal adaptation via divisive normalization aids stable representations of time-varying visual inputs. Overall, our results demonstrate how temporal adaptation improves sensory representations and identify candidate neural computations mediating these effects. Author summaryRobust perception is essential for the human brain to detect, process, and act upon new sensory inputs. Temporal adaptation is believed to play a key role in robust sensory processing by allowing neurons to continuously adjust their responses to previous inputs in order to optimize the processing of future inputs. Here, we show that temporal adaptation aids visual object recognition by improving neural representations of object contrast and object category. By emulating temporal adaptation in deep convolutional neural network models with different computational mechanisms, we identify candidate neural computations mediating benefits of temporal adaptation on sensory processing.

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bioRxiv2024

Shape-Biased Learning by Thinking Inside the Box

Convolutional Neural Networks (CNNs) surpass human-level performance on visual object recognition and detection, but their behavior still differs from human behavior in important ways. One prominent example is that CNNs trained on ImageNet exhibit an image texture bias, while humans exhibit a strong bias toward object shape. Although CNN shape bias can be increased in various ways, e.g., using data augmentation or additional training techniques, it remains unclear what causes the strong discrepancy between human and CNN object recognition strategies. Developmental research suggests that one factor driving human shape bias is that during early childhood, toddlers tend to fill their field-of-view with close-up objects. Here, we operationalize this close-up as a zoom-in on objects during CNN training which we show increases shape bias without any additional training or data augmentation. We provide further evidence for the advantage of closeup object vision by systematically manipulating the background-object ratio during CNN training, and demonstrate a strong (inverse) correlation with shape bias. Moreover, zooming-in on objects, thereby more closely emulating child vision, not only increases shape bias but also concurrently aligns classification accuracy and shape bias between humans and CNNs. Finally, we achieve a near human-like shape bias when using a developmentally-inspired background-object ratio for training and shape bias assessment. In sum, from a simple adjustment to common image datasets - zooming-in on objects - human-like shape bias can emerge. These results suggest that taking inspiration from human learning strategies is a promising avenue for building human-aligned, efficient, and more robust vision CNNs.

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bioRxiv2024

Distinct representation of navigational action affordances in human behavior, brains and deep neural networks

To decide how to move around the world, we must determine which locomotive actions (e.g., walking, swimming, or climbing) are afforded by the immediate visual environment. The neural basis of our ability to recognize locomotive affordances is unknown. Here, we compare human behavioral annotations, functional magnetic resonance imaging (fMRI) measurements, and deep neural network (DNN) activations to both indoor and outdoor real-world images to demonstrate that human visual cortex represents locomotive action affordances in complex visual scenes. Hierarchical clustering of behavioral annotations of six possible locomotive actions show that humans group environments into distinct affordance clusters using at least three separate dimensions. Representational similarity analysis of multi-voxel fMRI responses in scene-selective visual cortex shows that perceived locomotive affordances are represented independently from other scene properties such as objects, surface materials, scene category or global properties, and independent of the task performed in the scanner. Visual feature activations from DNNs trained on object or scene classification as well as a range of other visual understanding tasks correlate comparatively lower with behavioral and neural representations of locomotive affordances than with object representations. Training DNNs directly on affordance labels or using affordance-centered language embeddings increases alignment with human behavior, but none of the tested models fully captures locomotive action affordance perception. These results uncover a new type of representation in the human brain that reflects locomotive action affordances. SignificanceTo navigate the world around us, we can use different actions, such as walking, swimming or climbing. How does our brain compute and represent such locomotive action affordances? Here, we show that activation patterns in high-level visual regions in the human brain represent information about affordances independent of other visual elements such as surface materials and objects, and do so in an automatic manner. We also demonstrate that commonly used models of visual processing in human brains, namely object- and scene- classification trained deep neural networks, do not strongly represent this information. Our results suggest that locomotive action affordance perception in scenes relies on specialized neural representations different from those used for other visual understanding tasks.

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bioRxiv2023

Temporal dynamics of neural adaptation across human visual cortex

Neural responses in visual cortex adapt to prolonged and repeated stimuli. While adaptation occurs across the visual cortex, it is unclear how adaptation patterns and computational mechanisms differ across the visual hierarchy. Here we characterize two signatures of short-term neural adaptation in time-varying intracranial electroencephalography (iEEG) data collected while participants viewed naturalistic image categories varying in duration and repetition interval. Ventraland lateral-occipitotemporal cortex exhibit slower and prolonged adaptation to single stimuli and slower recovery from adaptation to repeated stimuli compared to V1-V3. For category-selective electrodes, recovery from adaptation is slower for preferred than non-preferred stimuli. To model neural adaptation we augment our delayed divisive normalization (DN) model by scaling the input strength as a function of stimulus category, enabling the model to accurately predict neural responses across multiple image categories. The model fits suggest that differences in adaptation patterns arise from slower normalization dynamics in higher visual areas interacting with differences in input strength resulting from category selectivity. Our results reveal systematic differences in temporal adaptation of neural population responses across the human visual hierarchy and show that a single computational model of history-dependent normalization dynamics, fit with area-specific parameters, accounts for these differences. Author summaryNeural responses in visual cortex adapt over time, with reduced responses to prolonged and repeated stimuli. Here, we examine how adaptation patterns differ across the visual hierarchy in neural responses recorded from human visual cortex with high temporal and spatial precision. To identify possible neural computations underlying short-term adaptation, we fit the response time courses using a temporal divisive normalization model. The model accurately predicts prolonged and repeated responses in lower and higher visual areas, and reveals differences in temporal adaptation across the visual hierarchy and stimulus categories. Our model suggests that differences in adaptation patterns result from differences in divisive normalization dynamics. Our findings shed light on how information is integrated in the brain on a millisecond-time scale and offer an intuitive framework to study the emergence of neural dynamics across brain areas and stimuli.

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bioRxiv2023

Spatial Tuning of Alpha Oscillations in Human Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWNeuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz) but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance StatementThe alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention. Grant support: NIH R01 MH111417 (Petridou, Winawer, Ramsey, Devinsky); JSPS Overseas Research Fellowship (Yuasa) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

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bioRxiv2022

Emergence of perceptual reorganisation from prior knowledge in human development and Convolutional Neural Networks

Perception in the mature human visual system relies heavily on prior knowledge. Here we show for the first time that prior-knowledge-induced reshaping of visual perception emerges gradually, and late in childhood. To isolate the effects of prior knowledge on vision, we presented 4-to-12-year-olds and adults with two-tone images, which are degraded photos that are hard to recognise on first viewing. In adults, seeing the original photo causes a perceptual reorganisation leading to sudden, mandatory recognition of the two-tone version - a well-documented process relying on top-down signalling from higher-order brain areas to early visual cortex. We find that children younger than 7 to 9 years, however, do not experience this knowledge-guided shift, despite viewing the original photo immediately before each two-tone. To assess potential computations underlying this development we compared human performance to three state-of-the-art neural networks with varying architectures. We found that the best-performing architecture behaved much like 4- to 5-year-old humans, who display a feature-based rather than holistic processing strategy akin to neural networks. Our results reveal a striking age-related shift in the reconciliation of prior knowledge with sensory input, which may underpin the development of many perceptual abilities.

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