Fwd: Neurocomputing Review Request NEUCOM-D-20-03222

[Ifigeneia Apostolopoulou]

Hi all,

I hope that you are well and that you are enjoying the last days of the
summer!
Below you will find an invitation for review, that due to time constraints,
I will not be able to accept.
In case anyone is interested in reviewing this article && has time by Sept
19
(the abstract is at the end of the forwarded email), please let me know to
add you as suggested alternative reviewer.

Thanks and happy fall semester :)


---------- Forwarded message ---------
From: Neurocomputing <em at editorialmanager.com>
Date: Sat, Aug 22, 2020 at 9:32 PM
Subject: Neurocomputing Review Request NEUCOM-D-20-03222
To: Ifigeneia Apostolopoulou <iapostol at andrew.cmu.edu>


Dear Ms. Apostolopoulou,

As editor of Neurocomputing, I would hereby like to ask you the big favor
of reviewing the manuscript

"Deep Hebbian predictive coding accounts for emergence of complex neural
response properties along the visual cortical hierarchy"

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).

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.

******

In addition to accessing our subscriber content, you can also use our Open
Access content.  Read more about Open Access here:
http://www.elsevier.com/openaccess

Your help as an expert on neural networks is highly appreciated!

Kind regards,

Professor Yang Tang
Associate Editor

Reviewer Guidelines are now available to help you with your review:
http://www.elsevier.com/wps/find/reviewershome.reviewers/reviewersguidelines

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
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.

For further assistance, please visit our customer support site at
http://help.elsevier.com/app/answers/list/p/7923 Here you can search for
solutions on a range of topics, find answers to frequently asked questions
and learn more about EM via interactive tutorials. You will also find our
24/7 support contact details should you need any further assistance from
one of our customer support representatives.

Please note: Reviews are subject to a confidentiality policy,
http://service.elsevier.com/app/answers/detail/a_id/14156/supporthub/publishing/

__________________________________________________
In compliance with data protection regulations, you may request that we
remove your personal registration details at any time.  (Use the following
URL: https://www.editorialmanager.com/neucom/login.asp?a=r). Please contact
the publication office if you have any questions.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/autonlab-users/attachments/20200823/7e8d0d9a/attachment.html>