Biology subjects

Vinken, K.

Publications and source records attributed to Vinken, K..

8 recordsLinked to original sources

bioRxiv2025

Response dynamics in macaque ventral stream recapitulate the visual hierarchy

Chronic recording in macaque inferotemporal cortex (IT) revealed a progression of feature selectivity over the duration of the response, with different images evoking strongest responses in different time bins. Neurons responded briskly and transiently to early best images and gave slower and more sustained responses to later best images. Correlations between IT selectivity and deep neural network (DNN) activations in early layers were faster and more transient than correlations with deeper DNN layers, suggesting a hierarchical representation unfolding. Analysis of best images in different time bins showed that early best images were simpler, or made up of fewer parts, than later best images. This simple-to-complex selectivity dynamic is consistent with, but more universal than, previously reported dynamics of global-to-local or category-to-exemplar selectivity. In parallel with the simple-to-complex selectivity progression, the image selectivity and the population activity became sparser during the response, consistent with the hypothesis of a canonical cortical microcircuit that pools inputs and incorporates neighboring-neuron inhibition. We propose that this temporal cascade in IT recapitulates the multiple antecedent stages in the visual hierarchy, which also show a simple-to-complex progression of feature selectivity.

neuroscience↗

bioRxiv2025

Mapping Macaque to Human Cortex with Natural Scene Responses

Neuroscience has long relied on macaque studies to infer human brain function, yet identifying functionally corresponding brain regions across species and measurement modalities remains a fundamental challenge. This is especially true for higher-order cortex, where functional interpretations are constrained by narrow hypotheses and anatomical landmarks are often non-homologous. We present a data-driven approach for mapping functional correspondence across species using rich, naturalistic stimuli. By directly comparing macaque electrophysiology with human fMRI responses to 700 natural scenes, we identify fine-grained alignment based on response pattern similarity, without relying on predefined tuning concepts or hand-picked stimuli. As a test case, we examine the ventral face patch system, a well-studied but contested domain in cross-species alignment. Our approach resolves a longstanding ambiguity, yielding a correspondence consistent with full-brain anatomical warping but inconsistent with prior studies limited by narrow functional hypotheses. These findings show that natural image-evoked response patterns provide a robust foundation for cross-species functional alignment, supporting scalable comparisons as large-scale primate recordings become more widespread.

neuroscience↗

bioRxiv2025

Monkey See, Model Knew: Large Language Models Accurately Predict Visual Brain Responses in Humans and Non-Human Primates

AO_SCPLOWBSTRACTC_SCPLOWRecent progress in multimodal AI and language-aligned visual representation learning has rekindled debates about the role of language in shaping the human visual system. In particular, the emergent ability of language-aligned vision models (e.g. CLIP) - and even pure language models (e.g. BERT) - to predict image-evoked brain activity has led some to suggest that human visual cortex itself may be language-aligned in comparable ways. But what would we make of this claim if the same procedures could model visual activity in a species without language? Here, we conducted controlled comparisons of pure-vision, pure-language, and multimodal vision-language models in their prediction of human (N=4) and rhesus macaque (N=6, 5:IT, 1:V1) ventral visual activity to the same set of 1000 captioned natural images (the NSD1000). The results revealed markedly similar patterns in model predictivity of early and late ventral visual cortex across both species. This suggests that language model predictivity of the human visual system is not necessarily due to the evolution or learning of language perse, but rather to the statistical structure of the visual world that is reflected in natural language.

neuroscience↗

bioRxiv2023

When the whole is only the parts: non-holistic object parts predominate face-cell responses to illusory faces.

Humans are inclined to perceive faces in everyday objects with a face-like configuration. This illusion, known as face pareidolia, is often attributed to a specialized network of face cells in primates. We found that face cells in macaque inferotemporal cortex responded selectively to pareidolia images, but this selectivity did not require a holistic, face-like configuration, nor did it encode human faceness ratings. Instead, it was driven mostly by isolated object parts that are perceived as eyes only within a face-like context. These object parts lack usual characteristics of primate eyes, pointing to the role of lower-level features. Our results suggest that face-cell responses are dominated by local, generic features, unlike primate visual perception, which requires holistic information. These findings caution against interpreting neural activity through the lens of human perception. Doing so could impose human perceptual biases, like seeing faces where none exist, onto our understanding of neural activity.

neuroscience↗

bioRxiv2023

A computationally informed comparison between the strategies of humans and rodents in visual object recognition

Many species are able to recognize objects, but it has been proven difficult to pinpoint and compare how different species solve this task. Recent research suggested to combine computational and animal modelling in order to obtain a more systematic understanding of task complexity and compare strategies between species. In the present study, we created a large multidimensional stimulus set and designed a visual discrimination task partially based upon modelling with a convolutional deep neural network (CNN). Experiments included rats (N = 11; 1115 daily sessions in total for all rats together) and humans (N = 45). Each species was able to master the task and generalize to a variety of new images. Nevertheless, rats and humans showed very little convergence in terms of which object pairs were associated with high and low performance, suggesting the use of different strategies. There was an interaction between species and whether stimulus pairs favoured early or late processing in a CNN. A direct comparison with CNN representations and visual feature analyses revealed that rat performance was best captured by late convolutional layers and partially by visual features such as brightness and pixel-level similarity, while human performance related more to the higher-up fully connected layers. These findings highlight the additional value of using a computational approach for the design of object recognition tasks. Overall, this computationally informed investigation of object recognition behaviour reveals a strong discrepancy in strategies between rodent and human vision.

neuroscience↗

bioRxiv2022

The neural code for 'face cells' is not face specific

1 Face cells are visual neurons that respond more to faces than other objects. Clustered together in inferotemporal cortex, they are thought to carry out face processing specifically and are thus studied using faces almost exclusively. Analyzing neural responses in and around macaque face patches to hundreds of objects, we found graded response profiles for non-faces that were predictive of the degree of face selectivity and provided information on face-cell tuning that could not be characterized with actual faces. This relationship between non-face and face responses was not predicted by color and simple shape properties, but by information encoded in deep neural networks trained on general object classification rather than face identification. These findings contradict the long-standing assumption that face cells owe their category selectivity to face-specific features, instead providing evidence for the notion that category-selective neurons are best understood as tuning directions in an integrated, domain-general object space.

neuroscience↗

bioRxiv2020

Deep Neural Networks Point to Mid-level Complexity ofRodent Object Vision

In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but high-profile papers have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture the richness of information processing in primates through a succession of convolutional and fully connected layers. We find that rodent object vision can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most complex representations - which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large.

neuroscience↗

bioRxiv2019

Incorporating adaptation in object recognition models captures temporal dynamics in neurophysiology and perception

Adaptation is a fundamental property of the visual system that molds how an object is processed and perceived in its temporal context. It is unknown whether adaptation requires a circuit level implementation or whether it emerges from neuronally intrinsic biophysical processes. Here we combined neurophysiological recordings, psychophysics, and deep convolutional neural network computational models to test the hypothesis that a neuronally intrinsic, biophysically plausible, fatigue mechanism is sufficient to account for the hallmark properties of adaptation. The proposed model captured neural signatures of adaptation including repetition suppression and novelty detection. At the behavioral level, the proposed model was consistent with perceptual aftereffects. Furthermore, adapting to prevailing but irrelevant inputs improves object recognition and the adaptation computations can be trained in a network trained to maximize recognition performance. These results show that an intrinsic fatigue mechanism can account for key neurophysiological and perceptual properties and enhance visual processing by incorporating temporal context.

neuroscience↗