@article {5033, title = {Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions}, journal = {Cognitive Neuropsychology}, year = {2022}, month = {02/2022}, pages = {1 - 3}, keywords = {computer vision models, intermediate representations, Ventral visual cortex, visual deficits}, issn = {0264-3294}, doi = {10.1080/02643294.2022.2041588}, url = {https://www.tandfonline.com/doi/full/10.1080/02643294.2022.2041588}, author = {Armendariz, Marcelo and Will Xiao and Vinken, Kasper and Gabriel Kreiman} } @article {5096, title = {Face neurons encode nonsemantic features}, journal = {Proceedings of the National Academy of Sciences}, volume = {119}, year = {2022}, month = {02/2022}, abstract = {

The primate inferior temporal cortex contains neurons that respond more strongly to faces than to other objects. Termed {\textquotedblleft}face neurons,{\textquotedblright} these neurons are thought to be selective for faces as a semantic category. However, face neurons also partly respond to clocks, fruits, and single eyes, raising the question of whether face neurons are better described as selective for visual features related to faces but dissociable from them. We used a recently described algorithm, XDream, to evolve stimuli that strongly activated face neurons. XDream leverages a generative neural network that is not limited to realistic objects. Human participants assessed images evolved for face neurons and for nonface neurons and natural images depicting faces, cars, fruits, etc. Evolved images were consistently judged to be distinct from real faces. Images evolved for face neurons were rated as slightly more similar to faces than images evolved for nonface neurons. There was a correlation among natural images between face neuron activity and subjective {\textquotedblleft}faceness{\textquotedblright} ratings, but this relationship did not hold for face neuron{\textendash}evolved images, which triggered high activity but were rated low in faceness. Our results suggest that so-called face neurons are better described as tuned to visual features rather than semantic categories.

}, issn = {0027-8424}, doi = {10.1073/pnas.2118705119}, url = {https://pnas.org/doi/full/10.1073/pnas.2118705119}, author = {Bardon, Alexandra and Will Xiao and Carlos R Ponce and Margaret S Livingstone and Gabriel Kreiman} } @article {4657, title = {XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization}, journal = {PLOS Computational Biology}, volume = {16}, year = {2020}, month = {06/2020}, pages = {e1007973}, abstract = {

A longstanding question in sensory neuroscience is what types of stimuli drive neurons to fire. The characterization of effective stimuli has traditionally been based on a combination of intuition, insights from previous studies, and luck. A new method termed XDream (EXtending DeepDream with real-time evolution for activation maximization) combined a generative neural network and a genetic algorithm in a closed loop to create strong stimuli for neurons in the macaque visual cortex. Here we extensively and systematically evaluate the performance of XDream. We use ConvNet units as in silico models of neurons, enabling experiments that would be prohibitive with biological neurons. We evaluated how the method compares to brute-force search, and how well the method generalizes to different neurons and processing stages. We also explored design and parameter choices. XDream can efficiently find preferred features for visual units without any prior knowledge about them. XDream extrapolates to different layers, architectures, and developmental regimes, performing better than brute-force search, and often better than exhaustive sampling of \>1 million images. Furthermore, XDream is robust to choices of multiple image generators, optimization algorithms, and hyperparameters, suggesting that its performance is locally near-optimal. Lastly, we found no significant advantage to problem-specific parameter tuning. These results establish expectations and provide practical recommendations for using XDream to investigate neural coding in biological preparations. Overall, XDream is an efficient, general, and robust algorithm for uncovering neuronal tuning preferences using a vast and diverse stimulus space. XDream is implemented in Python, released under the MIT License, and works on Linux, Windows, and MacOS.

}, doi = {10.1371/journal.pcbi.1007973}, url = {https://dx.plos.org/10.1371/journal.pcbi.1007973}, author = {Will Xiao and Gabriel Kreiman}, editor = {Fyshe, Alona} } @conference {4460, title = {Biologically-plausible learning algorithms can scale to large datasets.}, booktitle = { International Conference on Learning Representations, (ICLR 2019)}, year = {2019}, abstract = {

The backpropagation (BP) algorithm is often thought to be biologically implau- sible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this {\textquotedblleft}weight transport problem{\textquotedblright} (Grossberg, 1987), two biologically-plausible algorithms, pro- posed by Liao et al. (2016) and Lillicrap et al. (2016), relax BP{\textquoteright}s weight sym- metry requirements and demonstrate comparable learning capabilities to that of BP on small datasets. However, a recent study by Bartunov et al. (2018) finds that although feedback alignment (FA) and some variants of target-propagation (TP) perform well on MNIST and CIFAR, they perform significantly worse than BP on ImageNet. Here, we additionally evaluate the sign-symmetry (SS) algo- rithm (Liao et al., 2016), which differs from both BP and FA in that the feedback and feedforward weights do not share magnitudes but share signs. We examined the performance of sign-symmetry and feedback alignment on ImageNet and MS COCO datasets using different network architectures (ResNet-18 and AlexNet for ImageNet; RetinaNet for MS COCO). Surprisingly, networks trained with sign- symmetry can attain classification performance approaching that of BP-trained networks. These results complement the study by Bartunov et al. (2018) and es- tablish a new benchmark for future biologically-plausible learning algorithms on more difficult datasets and more complex architectures.

}, author = {Will Xiao and Chen, Honglin and Qianli Liao and Tomaso Poggio} }