@inproceedings{
lappe2024parallel,
title={Parallel Backpropagation for Shared-Feature Visualization},
author={Alexander Lappe and Anna Bogn{\'a}r and Ghazaleh Ghamkhari Nejad and Albert Mukovskiy and Lucas Martini and Martin A. Giese and Rufin Vogels},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=OQzCSb6fbl}
}
High-level visual brain regions contain subareas in which neurons appear to respond more strongly to examples of a particular semantic category, like faces or bodies, rather than objects. However, recent work has shown that while this finding holds on average, some out-of-category stimuli also activate neurons in these regions. This may be due to visual features common among the preferred class also being present in other images. Here, we propose a deep-learning-based approach for visualizing these features. For each neuron, we identify relevant visual features driving its selectivity by modelling responses to images based on latent activations of a deep neural network. Given an out-of-category image which strongly activates the neuron, our method first identifies a reference image from the preferred category yielding a similar feature activation pattern. We then backpropagate latent activations of both images to the pixel level, while enhancing the identified shared dimensions and attenuating non-shared features. The procedure highlights image regions containing shared features driving responses of the model neuron. We apply the algorithm to novel recordings from body-selective regions in macaque IT cortex in order to understand why some images of objects excite these neurons. Visualizations reveal object parts which resemble parts of a macaque body, shedding light on neural preference of these objects.
Parallel Backpropagation for Shared-Feature Visualization
Alexander Lappe Anna Bognár Ghazaleh Ghamkhari Nejad Albert Mukovskiy Lucas Martini Martin A. Giese Rufin Vogels