ArticlePLoS computational biology2024
Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Article
- A TALE OF TWO TAILS: PREFERRED AND ANTI-PREFERRED NATURAL STIMULI IN VISUAL CORTEX.... International Conference on Learning Representations · 2026Article
- Dual-feature selectivity enables bidirectional coding in visual cortical neurons.bioRxiv : the preprint server for biology · 2025Article
- A wireless subdural-contained brain-computer interface with 65,536 electrodes and 1,024 channels.Nature electronics · 2025Article
- Deep learning-driven characterization of single cell tuning in primate visual area V4 supports topological organization.bioRxiv : the preprint server for biology · 2025Article
- Stable, chronic in-vivo recordings from a fully wireless subdural-contained 65,536-electrode brain-computer interface device.bioRxiv : the preprint server for biology · 2025Article
- A large-scale examination of inductive biases shaping high-level visual representation in brains and machines.Nature communications · 2024Article
- Heterogeneous orientation tuning in the primary visual cortex of mice diverges from Gabor-like receptive fields in primates.Cell reports · 2024Article
- How well do models of visual cortex generalize to out of distribution samples?PLoS computational biology · 2024Article
- High-performing neural network models of visual cortex benefit from high latent dimensionality.PLoS computational biology · 2024Article
- Energy Guided Diffusion for Generating Neurally Exciting Images.bioRxiv : the preprint server for biology · 2023Article
- A Unifying Principle for the Functional Organization of Visual Cortex.bioRxiv : the preprint server for biology · 2023Article
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8 authors.
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Abstract
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 that are not 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 V4's main functional role in semantic processing and suggesting that V4's selectivity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.
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