# A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes

arXiv: https://arxiv.org/abs/2307.00932 · HTML: https://mlsanity.com/paper/2307.00932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 72 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [A Benchmark for Interpretability Methods in Deep Neural Networks](https://mlsanity.com/paper/1806.10758/) (2018)
- [Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks](https://mlsanity.com/paper/2004.05937/) (2020)

## Built on by (most cited first, 1 of 1)

- [NeuroBind: Towards Unified Multimodal Representations for Neural Signals](https://mlsanity.com/paper/2407.14020/) (2024)

## Abstract (arXiv)

The visual system evolved to process natural scenes, yet most of our understanding of the topology and function of visual cortex derives from studies using artificial stimuli. To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses. We used this dataset to build a digital twin of V4 via deep learning, generating a detailed topographical map of natural image preferences at each cortical position. The map revealed clustered functional domains for specific classes of natural image features. These ranged from surface-related attributes like color and texture to shape-related features such as edges, curvature, and facial features. We validated the model-predicted domains with additional widefield calcium-imaging and single-cell resolution two-photon imaging. Our study illuminates the detailed topological organization and neural codes in V4 that represent natural scenes.