<div dir="ltr">Hi all,<div><br></div><div>I hope that you are well and that you are enjoying the last days of the summer!</div><div>Below you will find an invitation for review, that due to time constraints, I will not be able to accept.</div><div>In case anyone is interested in reviewing this article && has time by Sept 19 </div><div>(the abstract is at the end of the forwarded email), please let me know to add you as suggested alternative reviewer.</div><div><br></div><div>Thanks and happy fall semester :)</div><div><br><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">Neurocomputing</strong> <span dir="auto"><<a href="mailto:em@editorialmanager.com">em@editorialmanager.com</a>></span><br>Date: Sat, Aug 22, 2020 at 9:32 PM<br>Subject: Neurocomputing Review Request NEUCOM-D-20-03222<br>To: Ifigeneia Apostolopoulou <<a href="mailto:iapostol@andrew.cmu.edu">iapostol@andrew.cmu.edu</a>><br></div><br><br>Dear Ms. Apostolopoulou,<br>
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As editor of Neurocomputing, I would hereby like to ask you the big favor of reviewing the manuscript<br>
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"Deep Hebbian predictive coding accounts for emergence of complex neural response properties along the visual cortical hierarchy"<br>
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The abstract is attached at the bottom of this message. If possible, I would welcome receiving your review by Sep 19, 2020 (mm/dd/yyyy).<br>
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Please click on one of the following links to indicate whether you accept or decline the role of reviewing this paper. If you are not able to review this manuscript, We would appreciate receiving suggestions for alternative reviewers.<br><br>******<br><br>
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Your help as an expert on neural networks is highly appreciated!<br><br>
Kind regards,<br>
<br>
Professor Yang Tang <br>
Associate Editor<br>
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Reviewer Guidelines are now available to help you with your review: <a href="http://www.elsevier.com/wps/find/reviewershome.reviewers/reviewersguidelines" rel="noreferrer" target="_blank">http://www.elsevier.com/wps/find/reviewershome.reviewers/reviewersguidelines</a><br>
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Predictive coding provides a computational paradigm for modelling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we developed a scalable, deep network architecture for predictive coding that is trained using a Hebbian learning rule and mimics the feedforward and feedback connectivity of the cortex. After training on image datasets, the models formed latent representations in higher area that allowed reconstruction of the original images. We analyzed low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increased systematically across ascending areas in the model hierarchy. Depending on the strength of regularization factors, sparseness also increased from lower to higher areas. These results suggest a rationale as to why experimental results on sparseness across the cortical hierarchy have<br>
been inconsistent. Finally, representations for different object classes became more distinguishable from lower to higher areas. Thus, deep neural networks trained using a Hebbian formulation of predictive coding can reproduce several properties associated with neuronal responses along the visual cortical hierarchy.<br>
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