- Comp-neuro - lists.cnsorg.org

JOB: Neuroimaging Data Scientist. Multi-modal MRI study of voice perception.
by Sylvain Takerkart Sept. 15, 2016

Sept. 15, 2016

The 4-year project LIVES (Learning with Interacting Views), funded by the French National Research Agency (ANR), aims at developing innovative multi-view and multi-modal machine learning algorithms. As part of the LIVES consortium, our team at the Institut de Neurosciences de la Timone (INT, http://www.int.univ-amu.fr) is in charge of acquiring a large *multi-sequence MRI* dataset that will be used to benchmark these methods. This will include *anatomical*, *diffusion-weighte*d and *functional* (both resting state and task-based) MRI data. Our goal is to characterize the inter-subject variability observed in the cortical activation patterns measured with task-based functional MRI in the auditory cortex, in a task that involves the processing of vocal information, using the other acquired modalities. *Missions*. The data scientist will set-up processing pipelines dedicated to analyze the different MR scan types (univariate and multi-voxel pattern analysis methods for task-fMRI; advanced characterization of the cortical anatomy using high-level representations such as sulci and sulcal pits; estimation of structural and functional connectomes). He/she will organize the raw and processed data in a standardized fashion in order to make it accessible to the other members of the consortium in a first stage, and to the scientific community at large in a second stage. *Profile*. This position is for a candidate who is comfortable (or proficient) with *computer programming* (*python* will be our language of choice), has knowledge in *signal and image processing* and has a personal interest for *machine learning*. It can be of interest to different types of profiles. Either you are a young graduate (BSc or MSc) in data science, electrical engineering or neuroscience and you want to be at the heart of a project where medical imaging and machine learning meet, with the possibility to continue as a PhD student. Or you already have a PhD in a relevant domain and you want to take this opportunity to fulfill the aforementioned missions while getting further involved in the research aspects of the project, in machine learning or in neuroimaging. *Working environment*. INT is one of the top French neuroscience research institutes with 150 staff members in 10 inter-disciplinary teams examining different aspects of the cerebral organization. It is located on the medical campus of Aix-Marseille University. The successful candidate will join INT’s Neuro-Computing Center, who operates a high-end computing facility and pursues research at the intersection of machine learning and neuroscience. He/she will interact with other teams working on functional imaging of voice perception, computational anatomy, machine learning and MRI data acquisition. Marseille, the second largest city in France, is a vibrant inter-cultural hub located on the Mediterranean shore, and only 2h away from the Alps mountains. The position is initially open for one year, with a possible renewal for a second year. Starting date is around December 2016. If you are interested, please send your resume and cover letter before Septembre 30, 2016, to Sylvain_DOT_Takerkart_AT_univ-amu_DOT_fr and Pascal_DOT_Belin_AT_univ-amu_DOT_fr. -- Sylvain Takerkart Institut des Neurosciences de la Timone (INT) UMR 7289 CNRS-AMU Marseille, France tél: +33 (0)4 91 324 007 http://www.int.univ-amu.fr/_TAKERKART-Sylvain_?lang=en

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Tenure-Track Position in Computational Neuroscience, University of Oregon
by Yashar Ahmadian Sept. 15, 2016

Sept. 15, 2016

*Tenure track position in Computational Neuroscience* University of Oregon Departments of Mathematics and Biology (http://math.uoregon.edu/ http://biology.uoregon.edu/) Institute of Neuroscience (http://www.neuro.uoregon.edu/) The Departments of Biology and Mathematics and the Institute of Neuroscience at the University of Oregon announce a tenure track faculty position in computational neuroscience at the rank of assistant professor with ultimate department and institute affiliations flexible. Theorists seeking synergistic interactions with experimentalists in design, analysis and interpretation of research linking gene function, neuronal activity and behavior in model organisms and humans are especially encouraged to apply. This new position is part of an integrated effort to strengthen research at the nexus of mathematics and biology at the University of Oregon. It also advances a broader “Neurons to Minds” initiative that includes multiple new positions in systems and cognitive neuroscience to accelerate discovery in the neuronal basis of behavior and cognition. Minimum qualifications for candidates are a Ph.D. in an appropriate field (e.g. neuroscience, biology, mathematics, physics, electrical engineering, statistics, computer science, etc.), commitment to excellent teaching at the undergraduate and graduate levels, and an outstanding research record. Candidates should have the ability to work effectively within a diverse community. Applications will be received online at https://www.mathjobs.org/jobs/UO/IONMATHBIO. Candidates are asked to submit a cover letter, a curriculum vitae including a publication list, a statement of research accomplishments and future research plans, a description of teaching experience and philosophy, and three letters of recommendation (sent independently). Submission of 1-3 selected reprints is encouraged. To be assured of consideration, application materials should be uploaded by *November 15, 2016, but the position will remain open until filled*. Requests for information can be sent to Dr. Shawn Lockery, Chair, Computational Neuroscience Search Committee (shawn(a)uoregon.edu) *The University of Oregon is an equal opportunity, affirmative action institution committed to cultural diversity and compliance with the ADA. The University encourages all qualified individuals to apply, and does not discriminate **on the basis of any protected status, including veteran and disability status.* *---------------------------------------------* *Yashar Ahmadian* *Institute of Neuroscience* Departments of Biology and Mathematics *University of Oregon* *http://uoneuro.uoregon.edu/ahmadian/index.php <http://uoneuro.uoregon.edu/ahmadian/index.php>*

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Postdoc and Research Assistant openings in cognitive neuroscience at UCSF/Stanford
by Connolly, Colm Sept. 14, 2016

Sept. 14, 2016

Please respond to info(a)brainlens.org<mailto:info@brainlens.org> Research Assistant Position at UCSF in California, USA on Multilingualism The Hoeft Lab (http://brainLENS.org PI: Fumiko Hoeft MD PhD) is looking for an exceptional research assistant interested in the developmental cognitive neuroscience of multilingualism. This NIH-funded research is in collaboration with Cammie McBride PhD of Chinese Univ of Hong Kong, Ken Pugh PhD of Haskins/Yale/UConn, Linda Siegel of UBC, Manolo Carreiras PhD of BCBL, Ioulia Kovelman PhD of U Michigan, and Yuuko Uchikoshi EdD of UC Davis. The position is suited for a self-motivated, organized and independent thinker, with good interpersonal and multitasking skills, as well as proficiency in the English language and computer software. Neuroimaging, computational, statistical, programming, and neuropsych testing skills are highly desired but not necessary. Native-like proficiency in Spanish or Cantonese is a plus. A major strength of our lab is that there are plenty of opportunities to conduct independent research, to be first author on publications and to give conference presentations as a novice RA (e.g. Myers et al. Psychol Sci ’14, Myers et al. SCAN ’16, Haft et al. Curr Opin Behav Sci ’16). The position begins in January but start date can be sooner and is negotiable. Please email info(a)brainlens.org with a cover letter and your CV. Please add “[RA job]” and your full name in the Subject of the email. Qualified candidates will be asked to have 2-3 letters of reference forwarded. Joint Postdoctocal Position at UCSF and Stanford University in California, USA The Hoeft Lab (http://brainLENS.org PI: Fumiko Hoeft MD PhD) at the UCSF Dept of Psychiatry and Weill Institute for Neurosciences in collaboration with the Stanford Mood and Anxiety Disorders Lab in the Dept of Psychology (http://goo.gl/dc96Cx PI: Ian Gotlib PhD) is looking for an exceptional postdoc in the field of affective neuroscience, with advanced neuroimaging, psychophysiology, computational, programming and organizational skills. Training in genetics is a plus. The primary project that the postdoc will be responsible for is the examination of intergenerational neuroimaging using a ‘natural’ cross-fostering design that allows dissociation of genetic, prenatal and postnatal environment on brain networks that are transmitted across generations. Related articles from our lab can be found here - Yamagata et al. J Neurosci 2016 (http://goo.gl/vMK8iy) Ho et al. Trends in Neurosci 2016 (http://goo.gl/SyXLcK) and Scientific American (http://goo.gl/YTiH6D) There are other funded opportunities to be involved in research that examines the impact of anxiety and stereotype on psychophysiology and cognitive processes. The position can begin immediately. Please email info(a)brainlens.org with your CV, and with brief paragraphs of research interests, career goals and why you feel you are a good fit for the lab. Please add “[Postdoc job]” and your full name in the Subject of the email. Qualified candidates will be asked to have 3 letters of reference forwarded. Regards, -- Colm G. Connolly PhD Dept of Psychiatry & Langley Porter Psychiatric Institute UCSF Weill Institute for Neurosciences University of California, San Francisco 401 Parnassus Avenue, San Francisco, CA 94143

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Conference Aspects of Neuroscience [Warsaw, Poland, 25-27th November]
by Daniel Borek Sept. 13, 2016

Sept. 13, 2016

Dear Friends, On behalf of the Neurobiology Scientific Student Association at the Faculty of Biology of the University of Warsaw we would like to invite you to* the 6th annual International Conference “Aspects of Neuroscience” *to be held in Warsaw, Poland, from *November 25th to 27th, 2016*. As in previous years, the Conference is divided into 4 sessions presenting different approaches to Neuroscience:*Neurobiology, Cognitive Neuroscience, Clinical Neuroscience *and* Computational Neuroscience*. Each thematic session is opened by a lecture delivered by one of the guests who are specialists in a given domain. This year we will host such outstanding neuroscientists as *Prof. Eva Jablonka, Prof. Christian Lüscher, Prof. David Price, Prof. Kevin Warwick, Prof. Giovanna Mallucci *and* Dr. Daniel S. Margulies*. Participants are encouraged to give an oral presentation in one of the thematic sessions following the guest’s lecture or to present a poster during the poster session (either in the experimental or theoretical section). *Abstract submission deadline: 1st November 2016Passive registration deadline: 10th November 2016* The conference is intended for students, PhD students and research workers in neuroscience or related fields. The interdisciplinary character of the Conference is a base for integration of diverse scientific environments in order to create new quality in brain research. The language of the conference is English. *For more information about the Conference visit our new website:* http://neuroaspects.org <http://neuroaspects.us14.list-manage.com/track/click?u=c2e0466a746fafa55697…> Find us on Facebook: https://www.facebook.com/AspectsOfNeuroscience <http://neuroaspects.us14.list-manage.com/track/click?u=c2e0466a746fafa55697…> Yours sincerely, Organizing Committee of the International Conference Aspects of Neuroscience Neurobiology Scientific Student Association at the University of Warsaw

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Brains and Bits: Neuroscience Meets Machine Learning - NIPS Workshop: 9 & 10 December 2016
by Konrad Kording Sept. 13, 2016

Sept. 13, 2016

Join us (submit papers to) for the *Brains and Bits: Neuroscience Meets Machine Learning* *NIPS Workshop: 9 & 10 December 2016 Barcelona, Spain* http://www.stat.ucla.edu/~akfletcher/brainsbits.html The goal of this workshop is to bring together researchers in deep learning, machine learning, statistics, and computational neuroscience, and facilitate discussion about a) shared approaches for analyzing biological and artificial neural systems, b) how insights and challenges from neuroscience can inspire progress in machine learning, and c) machine learning methods for interpreting the revolutionary large scale datasets produced by new experimental neuroscience techniques. We invite high-quality submissions of abstracts for posters and contributed talks though CMT. Instructions are below. OverviewExperimental methods for measuring neural activity and structure have undergone recent revolutionary advances, including in high-density recording arrays, population calcium imaging, and large-scale reconstructions of anatomical circuitry. These developments promise unprecedented insights into the collective dynamics of neural populations and thereby the underpinnings of brain-like computation. However, these next-generation methods for measuring the brain’s architecture and function produce high-dimensional, large scale, and complex datasets, raising challenges for analysis. What are the machine learning and analysis approaches that will be indispensable for analyzing these next-generation datasets? What are the computational bottlenecks and challenges that must be overcome? In parallel to experimental progress in neuroscience, the rise of deep learning methods has shown that hard computational problems can be solved by machine learning algorithms that are inspired by biological neural networks, and built by cascading many nonlinear units. In contrast to the brain, artificial neural systems are fully observable, so that experimental data-collection constraints are not relevant. Nevertheless, it has proven challenging to develop a theoretical understanding of how neural networks solve tasks, and what features are critical to their performance. Thus, while deep networks differ from biological neural networks in many ways, they provide an interesting testing ground for evaluating strategies for understanding neural processing systems. Are there synergies between analysis methods for biological and artificial neural systems? Has the resurgence of deep learning resulted in new hypotheses or strategies for trying to understand biological neural networks? Conversely, can neuroscience provide inspiration for the next generation of machine-learning algorithms? We welcome participants from a range of disciplines in statistics, applied physics, machine learning, and both theoretical and experimental neuroscience, with the goal of fostering interdisciplinary insights. We hope that active discussions among these groups can set in motion new collaborations and facilitate future breakthroughs on fundamental research problems. ------------------------------ Organizers - Eva Dyer <http://kordinglab.com/people/eva_dyer/> Northwestern University - Allie Fletcher <http://www.stat.ucla.edu/~akfletcher/> Statistics, UCLA & UCB Redwood Center for Neuroscience - Konrad Kording <http://koerding.com//> Rehabilitation Institute of Chicago, Northwestern University - Jascha Sohl-Dickstein <http://www.sohldickstein.com/> Google Research - Joshua Vogelstein <http://jovo.me/> Biomedical Engineering, Johns Hopkins University - Jakob Macke <http://www.mackelab.org/> caesar Bonn, an Institute of the Max Planck Society Current Speakers - Christos Papadimitriou <https://people.eecs.berkeley.edu/~christos/> EECS, UC Berkeley - Adrienne Fairhall <https://fairhalllab.com/> Psychology and Biophysics, University of Washington - Yoshua Bengio <http://www.iro.umontreal.ca/~bengioy/yoshua_en/index.html> Computer Science and Operations Research, Université de Montréal - Sophie Denève <http://iec-lnc.ens.fr/group-for-neural-theory/members/faculty-in-alphabetic…> Group for Neural Theory, LNC, DEC, ENS - Demis Hassabis <http://demishassabis.com/> Google DeepMind - Terry Sejnowski <http://www.salk.edu/scientist/terrence-sejnowski/> Howard Hughes Medical Institute and Salk Institute, UCSD - Mitya Chklovskii <https://www.simonsfoundation.org/simons-center-for-data-analysis/scda-neuro…> Simons Foundation - Anima Anandkumar <http://newport.eecs.uci.edu/anandkumar/> EECS, UC Irvine - David Cox <http://www.coxlab.org/> Molecular and Cellular Biology and Computer Science, Harvard University - Surya Ganguli <https://web.stanford.edu/dept/app-physics/cgi-bin/person/surya-gangulijanua…> Applied Physics, Stanford University - Maneesh Sahani <http://www.gatsby.ucl.ac.uk/~maneesh/> Gatsby Institute, University College, London - Emily Fox <https://www.stat.washington.edu/~ebfox/> Statistics and Computer Science, University of Washington - Jonathan Pillow <http://pillowlab.princeton.edu/> Center for Statistics and Machine Learning, Princeton University - Fred Hamprecht <https://hciweb.iwr.uni-heidelberg.de/people/fhamprec> Heidelberg Collaboratory for Image Processing (HCI), Heidelberg - Max Welling, University of Amsterdam Submission Information & Important Dates Submissions will be considered both for poster and oral presentation. See instructions on the workshop website at http://www.stat.ucla.edu/~ akfletcher/brainsbits.html Submission deadline: 29 September 2016 11:59 PM PDT (UTC -7 hours) Acceptance notification: 12 October 2016

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2 open faculty positions at U. Colorado Medical School
by Zylberberg, Joel L Sept. 12, 2016

Sept. 12, 2016

Hello All, The Department of Physiology and Biophysics at the University of Colorado Medical School currently has two open tenure-track faculty positions. Specifically, we are searching for a new computational neuroscience / physiology faculty member, and for a new systems neuroscience / physiology faculty member. The positions at our medical school are quite nice for research-focused faculty: light teaching loads, extremely competitive start-up packages, etc. Details are available via this web link: http://www.ucdenver.edu/academics/colleges/medicalschool/departments/physio… If you have any questions, please don't hesitate to ask me. Also, please forward this advertisement to any colleagues of yours who might be interested. Thanks! __ Joel Zylberberg Assistant Professor Department of Physiology and Biophysics University of Colorado School of Medicine www.jzlab.org<http://www.jzlab.org> CIFAR Azrieli Global Scholar, Learning in Machines and Brains program ~the neuroscience is theoretical, but the fun is real

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Third International Conference on Mathematical Neuroscience (ICMNS 2017)
by Robert Rosenbaum Sept. 12, 2016

Sept. 12, 2016

We are delighted to announce the 3rd International Conference on Mathematical Neuroscience (ICMNS 2017), to be held at the Hotel Boulderado in Boulder, Colorado from May 30 to June 2 (Tutorials: May 30; Main Conference: May 31 - June 2). http://www.colorado.edu/amath/international-conference-mathematical-neurosc… *Abstract submission will open in October/November for both contributed talks and posters. *Student presenters can apply for travel awards funded by NSF, Burroughs Wellcome, and SIAM. The conference provides a forum for researchers to discuss current mathematical innovations emerging in neuroscience. We hope to attract and train young researchers on current methods in mathematical neuroscience. The meeting will feature leaders in applied mathematics and neuroscience that are developing new mathematical techniques for understanding high-dimensional data sets, building models to capture activity patterns and emergent computation, and working closely with experimentalists to address targeted questions about brain function. CONFIRMED SPEAKERS Danielle Bassett (University of Pennsylvania) Paul Bressloff (University of Utah) Nicolas Brunel (University of Chicago) Sophie Deneve (École Normale Supérieure) Brent Doiron (University of Pittsburgh) Ila Fiete (University of Texas) Stefano Fusi (Columbia University) Peter Thomas (Case Western Reserve University) Taro Toyoizumi (Riken Brain Science Institute) More TBA! We hope to see you in Boulder this coming May! All the best Zack Kilpatrick (University of Colorado) Julijana Gjorgjieva (Max Planck Institute for Brain Research) Robert Rosenbaum (University of Notre Dame) (conference chairs)

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PhD position in Computational/Systems Neuroscience @ the Institute of Neuroinformatics - ETH Zurich
by Benjamin Grewe Sept. 12, 2016

Sept. 12, 2016

PhD position in Computational/Systems Neuroscience @ the Institute of Neuroinformatics - ETH Zurich – in vivo imaging of neural ensemble learning in awake behaving animals – We are looking for a PhD candidate with a strong motivation to understand neuronal information processing (NIP) as well as learning induced changes in NIP at the level of large neuronal ensembles. To investigate NIP we utilize cutting edge large scale in vivo population calcium imaging techniques that allow recording ensemble activity of hundreds of neurons in freely moving and head-fixed rodents during diverse learning behaviors. Ideal candidates have some expertise in a neuroscience related field. The PhD candidate will acquire a unique skill set to utilize miniaturized and very large-scale calcium imaging technologies and work closely with the PI to perform behavior experiments and post-hoc data analysis. Applicants should hold a diploma or Masters degree in a quantitative discipline (e.g. physics, informatics, mathematics, biomedical-engineering) or neurobiology/neuroscience (e.g. systems neuroscience, neurophysiology, cell physiology). Experience in data analysis using MATLAB/Python/R or similar programming languages and basic knowledge in computational or experimental neuroscience is beneficial. Proficiency in English and a team-oriented working attitude are crucial for the applicant’s success. Candidates should be willing to perform biological in vivo experiments in rodents with an intrinsic motivation to unravel neural algorithms of information processing and learning. Please submit your complete application as one PDF via email to bgrewe(a)ethz.ch <mailto:bgrewe@ethz.ch>. The application should include the following documents: • A cover letter that includes the main motivation for doing a PhD at the INI. • Contact details of 2 references (email and phone number). • CV outlining prior education, research and publications (if available) • Academic transcripts (bachelor, master/diploma). • 1-2 page personal research statement. The research statement should cover the applicant’s future career goals and research project interests. The INI offers advanced, interdisciplinary PhD training and participation in cutting edge neuroscience and neuroinformatics research projects. The institute is home to a lively and interactive educational atmosphere with highly competitive salaries. Successful candidates will participate in the ZNZ doctoral program (http://www.neuroscience.uzh.ch/en.html <http://www.neuroscience.uzh.ch/en.html>) where they receive additional theoretical and practical training. Compensation/salary will be paid according to the guidelines of the Swiss National Science Foundation. Successful candidates will receive their PhD from ETH Zurich. Applications will be accepted until the position is filled and will be reviewed upon receipt. The Swiss Federal Institute of Technology (ETH) in Zurich is consistently ranked the top university in continental Europe and is a leading player in research and education in Switzerland and worldwide. The INI is a joint institute of ETH and the University of Zurich with a primary research focus at the intersection of systems neuroscience and neuroinformatics. The mission of the institute is to discover key principles by which neural networks operate and process information. Comprising a diverse research portfolio (www.ini.uzh.ch <http://www.ini.uzh.ch/>) the INI is uniquely positioned to bridge the gap between traditional systems neuroscience, neurobiology, neuroinformatics and neuroengineering. --------------------------------------------------------------------- Benjamin F. Grewe Institute of Neuroinformatics @ ETH Zurich Winterthurerstrasse 190 CH-8057 Zurich, Switzerland Email: Bgrewe(a)ethz.ch

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Final notice - 2016 EEGLAB workshop following SFN, Nov 17-21, San Diego
by Arnaud Delorme Sept. 10, 2016

Sept. 10, 2016

EEGLAB developers and advanced users will present a four day workshop on the data analysis concepts and functions of the EEGLAB electrophysiological data processing environment and associated toolboxes held at the University of California San Diego in La Jolla, November 17-21, 2016 following the Society for Neuroscience meeting. In particular, we will feature our new improved statistical framework and data management strategies based on the General Linear Model with support from the LIMO EEGLAB extension. The preliminary program, registration instructions, as well as list of accommodation are available at http://sccn.ucsd.edu/wiki/EEGLAB_2016_at_UCSD <http://sccn.ucsd.edu/wiki/EEGLAB_2016_at_UCSD> Registration costs are 350 US dollars for students and post-docs and 500 US dollars for faculty and professionals. The workshop is a non profit event and faculty are volunteering their time. Registration will be handled by Eventbrite below. https://www.eventbrite.com/e/the-ucsd-eeglab-2016-workshop-san-diego-ticket… <https://www.eventbrite.com/e/the-ucsd-eeglab-2016-workshop-san-diego-ticket…> For administrative questions about the location, hotel reservation, registration payments, and workshop overall organization please contact Risa at rshibata(a)ucsd.edu <mailto:antonio.cataldo2@unibo.it>. For questions about the scientific program, please contact EEGLAB organizers at eeglab(a)sccn.ucsd.edu <mailto:eeglab@sccn.ucsd.edu>. We are looking forward to see you at the workshop! A. Delorme S. Makeig

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Neural Processing Letters: CFP - Special Issue on "Off the mainstream: advances in neural networks and machine learning for pattern recognition"
by Friedhelm Schwenker Sept. 9, 2016

Sept. 9, 2016

Call for Papers Special issue on Off the mainstream: advances in neural networks and machine learning for pattern recognition to be published in Neural Processing Letters *** Submission deadline: January 31, 2017 *** Guest editors: Edmondo Trentin, University of Siena, Italy (trentin(a)dii.unisi.it) Friedhelm Schwenker, University of Ulm, Germany (friedhelm.schwenker(a)uni-ulm.de) Nemat El Gayar, Cairo University, Egypt (elgayar.neamat(a)gmail.com) Hazem M. Abbas, Ain Shams University, Egypt (as(a)eng.asu.edu.eg) Aims and scope of the special issue: "Mainstream science is about publishing what everyone else is publishing with very small changes. You'd better at least start off that way if you want to get tenure," the sociologist Rodney Stark said. But "big ideas don't come to those who avoid risk", as John Bohannon added. The area of artificial neural networks (ANN) and machine learning (ML) makes no exception to these ends: Mainstream topics, originally stemming from exciting breakthroughs (the "big ideas") that gradually become trends and end-up being mostly over-beaten publishing tracks, have characterized the scientific literature throughout the whole history of these research fields. A few, widely known instances of such (more or less recent) mainstream trends are: - Supervised support vector machine training (in both primal and dual) - Supervised multilayer perceptron training via regular backpropagation - Radial basis functions networks - Bayesian networks (either shallow or deep) - Deep feed-forward and convolutional neural networks - Countless applications of the aforementioned machineries - The “approximation capabilities” of such machineries Based on these premises, this special issue invites paper submissions on real novel research developments in the areas of neural networks and learning machines that (1) are rooted in (or, aimed at) pattern recognition (PR), and that, above all, (2) do not follow in the footsteps of nowadays established trends. Preference (over applications, theoretical analysis, and variants of established techniques) will thus be given to submissions that hand out fresh and innovative ideas/architectures/algorithms, even if they are in their infancy (e.g., possibly lacking of a complete investigation of their theoretical properties). A detailed list of topics of interest would contradict the very perspective of the present special issue. Nonetheless, some general, topical research directions are (to name a few): - New ANN or ML architectures - New ANN, ML, or PR algorithms - New estimation/optimization/assessment techniques for ANNs, ML, or PR - New and sound combination/hybridization of machines - Solutions to new, relevant PR-related problems - New and sound solutions to established PR-related problems If you are not sure on whether your manuscripts matches the aims and scope of this special issue or not, do not hesitate to get in touch with the guest editors at any time. The special issue will comprise (a) papers submitted in response to this call, and (b) extended versions of selected papers from the ANNPR 2016 Workshop (https://neuro.informatik.uni-ulm.de/ANNPR2016/) sponsored by the International Association for Pattern Recognition. Paper submission: Papers must be submitted online via the Neural Processing Letters website (https://www.editorialmanager.com/nepl/default.aspx) selecting the choice that indicates this special issue (identifier: S.I.:Off_mainstream). Prepare your paper following the Journal guidelines for Authors (http://www.springer.com/computer/ai/journal/11063?detailsPage=pltci_1060677) All submitted papers will undergo a regular peer-review process. Important dates: Opening of electronic submission: September 1, 2016 Submission deadline: January 31, 2017 Completion of 1st round of review process: March 31, 2017 Re-submission of revised manuscripts: May 15, 2017 Final decision: June 30, 2017 Tentative publication of the Special Issue: Fall 2017 -- PD Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email: friedhelm.schwenker(a)uni-ulm.de www: http://www.uni-ulm.de/in/neuroinformatik/mitarbeiter/f-schwenker.html

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