Vision is a highly demanding computational problem. Biological and artificial visual systems face the same challenges – e.g., building transformation-invariant representations of visual objects or pattern-invariant representations of motion direction. In our work, we use computational modeling and machine learning tools, often in collaboration with leading theoretical neuroscientists and data scientists, to compare visual processing in brains in machines. Specifically:

• we have developed conceptual (e.g., ideal observer) models to explain rat visual perception of shapes (Djurdjevic et al., 2018), motion direction (Matteucci et al., 2021) and textures (Caramellino et al., 2021)
• we have applied cutting edge machine learning approaches (e.g., clustering algorithms) to investigate object representations in monkey (Baldassi et al., 2013) and rat (Vascon et al., 2019) visual cortex
• we have investigated how data representations are reformatted across deep convolutional neural networks for image classification (Ansuini et al., 2019) and how consistent such representations are with those found along the rat visual cortical hierarchy (Matteucci et al., 2019, 2023; Muratore et al., 2022) and those supporting rat object vision (Muratore et al, 2024).

See below for a complete list of our computational studies.