<!DOCTYPE html><html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8" /></head><body><div data-html-editor-font-wrapper="true" style="font-family: irans, sans-serif; font-size: 13px;" dir="ltr"><div><div><div style="font-family: irans, sans-serif;font-size: 13px"><div><div><div style="font-family: irans, sans-serif;font-size: 13px"><div><div><div style="font-family: irans, sans-serif;font-size: 13px"><div><div><div style="font-family: irans, sans-serif;font-size: 13px"><p>Call for papers<br><strong>Brain Sciences</strong><br>Special Issue "Neuronal Mechanism of Object Recognition"<br><a data-saferedirecturl="https://www.google.com/url?q=https://www.mdpi.com/journal/brainsci/special_issues/neuronal_object_recognition&source=gmail&ust=1674397653800000&usg=AOvVaw3TTq-fByM4sLD9uWo9dwxF" rel="external nofollow noopener noreferrer" tabindex="-1" target="_blank" href="https://www.mdpi.com/journal/brainsci/special_issues/neuronal_object_recognition">https://www.mdpi.com/journal/<wbr>brainsci/special_issues/<wbr>neuronal_object_recognition</wbr></wbr></a><br><br>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<br>DANNs to biological vision.<br>The topics of interest include, but are not limited to:</p><ul><li>Object Recognition</li><li>Temporal Dynamics</li><li>Recurrent Processing</li><li>Decision Making</li><li>Deep Artificial Neural Networks</li></ul><p>Deadline for manuscript submissions: <b>25 April 2023</b><br><br>Dr. Reza Ebrahimpour (Sharif University of Technology, Iran)<br>Dr. Hamid Karimi Rouzbahani (University of Queensland, Australia)<br><em>Guest Editors</em></p><signature><br>--------<br>​Reza Ebrahimpour, Professor of Cognitive Neuroscience<br>Center for Cognitive Science<span style> </span><span style> </span><br>Institute for Convergence Science and Technology<br>Sharif University of Technology<br>Tehran, P.O.Box:<signature><signature>11155-1639</signature></signature>, Iran<br>Email: <a target="_blank" rel="external nofollow noopener noreferrer" tabindex="-1" href="mailto:ebrahimpour@sharif.edu">ebrahimpour@sharif.edu</a><br>Web: <a target="_blank" rel="external nofollow noopener noreferrer" tabindex="-1" href="https://www.sharif.edu/en/web/icst/cognitive/council">https://www.sharif.edu/en/web/icst/cognitive/council</a><br>And<br>School of Cognitive Sciences (SCS)<br>Institute for Research in Fundamental Sciences (IPM)<br>Niavaran, P.O.Box:19395-5746, Tehran, Iran<br>Email: <a target="_blank" rel="external nofollow noopener noreferrer" tabindex="-1" href="mailto:ebrahimpour@ipm.ir">ebrahimpour@ipm.ir</a><br>Web: <a target="_blank" rel="external nofollow noopener noreferrer" tabindex="-1" href="http://www.ipm.ac.ir/personalinfo.jsp?PeopleCode=IP0400031">http://www.ipm.ac.ir/personalinfo.jsp?PeopleCode=IP0400031</a><br><br>Lab Address: <a target="_blank" rel="external nofollow noopener noreferrer" tabindex="-1" href="http://ebrahimpourlab.ir/">http://ebrahimpourlab.ir</a></signature></div></div></div></div></div></div></div></div></div></div></div></div></div></body></html>