Dear Colleagues,

Humans are astonishingly good at recognising visual objects despite drastic variations in their appearances. To understand how the brain handles these variations, several promising approaches have been developed in recent years. First, high-resolution brain imaging techniques, including unimodal and multimodal (e.g., fusion-based E/MEG-fMRI) and high-dimensional brain imaging analyses, such as representational similarity and connectivity analyses, have paved the way for obtaining high temporal and spatial insights into the brain. These allow the evaluation of temporal evolution and spatial distribution of representations, which is critical for characterising the role of high-speed feed-forward and recurrent mechanisms across the visual cortex, as well as temporal decision-making mechanisms. This is especially important for uncertain and degraded sensory inputs. Despite recent progress, the role of recurrent and feedback processes in object recognition remains underinvestigated. Second, the development of ground-breaking deep artificial neural networks (DANNs) has provided new tools to evaluate plausible mathematical operations which might contribute to robust object recognition under variations such as occlusion, lighting and background. These networks have not only revolutionised artificial intelligence in many applications, outperforming humans in several of them, but have also revealed unknown characteristics of the visual system. For example, they have shown the possible selectivity of individual neurons and neuron populations to object category and category–orthogonal variations, and have shown how such selectivity can lead to incorrect categorisation of objects, as in the case of adversarial images. This Special Issue is dedicated to original research on neural mechanisms of object recognition, particularly novel methods in brain imaging analysis and comparing DANNs to biological vision.

Dr. Reza Ebrahimpour
Dr. Hamid Karimi-Rouzbahani
Guest Editors

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• object recognition
• temporal dynamics
• recurrent processing
• decision making
• deep artificial neural networks