@article {5014, title = {Deep neural network models of sound localization reveal how perception is adapted to real-world environments}, journal = {Nature Human Behavior}, volume = {6}, year = {2022}, month = {01/2022}, pages = {111{\textendash}133 }, chapter = {111}, abstract = {

Mammals localize sounds using information from their two ears. Localization in real-world conditions is challenging, as echoes provide erroneous information and noises mask parts of target sounds. To better understand real-world localization, we equipped a deep neural network with human ears and trained it to localize sounds in a virtual environment. The resulting model localized accurately in realistic conditions with noise and reverberation. In simulated experiments, the model exhibited many features of human spatial hearing: sensitivity to monaural spectral cues and interaural time and level differences, integration across frequency, biases for sound onsets and limits on localization of concurrent sources. But when trained in unnatural environments without reverberation, noise or natural sounds, these performance characteristics deviated from those of humans. The results show how biological hearing is adapted to the challenges of real-world environments and illustrate how artificial neural networks can reveal the real-world constraints that shape perception.

}, doi = {https://doi.org/10.1038/s41562-021-01244-z}, url = {https://www.nature.com/articles/s41562-021-01244-z}, author = {Andrew Francl and Josh H. McDermott} } @article {4478, title = {Do Neural Networks for Segmentation Understand Insideness?}, year = {2020}, month = {04/2020}, abstract = {

The insideness problem is an image segmentation modality that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentation benchmarks, but it is unclear that they have the ability to solve the insideness problem as it requires evaluating long-range spatial dependencies. In this paper, the insideness problem is analyzed in isolation, without texture or semantic cues, such that other aspects of segmentation do not interfere in the analysis. We demonstrate that DNNs for segmentation with few units have sufficient complexity to solve insideness for any curve. Yet, such DNNs have severe problems to learn general solutions. Only recurrent networks trained with small images learn solutions that generalize well to almost any curve. Recurrent networks can decompose the evaluation of long-range dependencies into a sequence of local operations, and learning with small images alleviates the common difficulties of training recurrent networks with a large number of unrolling steps.

}, author = {Kimberly M. Villalobos and Vilim Štih and Amineh Ahmadinejad and Shobhita Sundaram and Jamell Dozier and Andrew Francl and Frederico Azevedo and Tomotake Sasaki and Xavier Boix} } @article {3962, title = {Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?}, year = {2018}, month = {12/2018}, abstract = {

THIS MEMO IS REPLACED BY CBMM MEMO 105

A key component of visual cognition is the understanding of spatial relationships among objects. Albeit effortless to our visual system, state-of-the-art artificial neural networks struggle to distinguish basic spatial relationships among elements in an image. As shown here, deep neural networks (DNNs) trained with hundreds of thousands of labeled examples cannot accurately distinguish whether pixels lie inside or outside 2D shapes, a problem that seems much simpler than image segmentation. In this paper, we sought to analyze the capability of ANN to solve such inside/outside problems using an analytical approach. We demonstrate that it is a mathematically tractable problem and that two previously proposed algorithms, namely the Ray-Intersection Method and the Coloring Method, achieve perfect accuracy when implemented in the form of DNNs.

}, author = {Kimberly M. Villalobos and Jamel Dozier and Vilim Stih and Andrew Francl and Frederico Azevedo and Tomaso Poggio and Tomotake Sasaki and Xavier Boix} }