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KogWis2022 | 15th Biannual Conference of the German Cognitive Science Society +++ The call for symposia is open
by Constantin Rothkopf Feb. 18, 2022

Feb. 18, 2022

Event: The 15th Biannual Conference of the German Cognitive Science Society, KogWis2022 Theme: Understanding Minds Date: 5.-7. September 2022 Location: Freiburg im Breisgau, Germany Host: Albert-Ludwigs University Freiburg Web: https://www.kogwis2022.uni-freiburg.de/ Abstract: The 15. biannual Conference of the German Cognitive Science Society (KogWis2022) will take place from 5.-7. September 2022 in Freiburg i.Br., Germany, organized by the Center for Cognitive Science. The theme “Understanding Minds” reflects two perspectives: The conference provides a forum for all topics in the study of how minds – both human and artificial – operate. The theme also puts a specific spotlight on how cognitive systems make sense of the world, in particular in language comprehension and communication. The Call for Symposia for the 15th Biannual Conference of the German Cognitive Science Society, KogWis2022, is now open! https://www.kogwis2022.uni-freiburg.de/ Confirmed keynote speakers are: Dedre Gentner, Northwestern University, Illinois Seana Coulson, University of California, San Diego Marcel Brass, Humboldt University, Berlin Matthew Crocker, Saarland University, Saarbrücken For any inquiries please use the conference e-mail-address kogwis2022(a)cognition.uni-freiburg.de. Further information is available on the conference website: www.kogwis2022.uni-freiburg.de. Organising Committee: Evelyn Ferstl, Lars Konieczny, Rul von Stülpnagel, Lisa Zacharski, Judith Beck

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MBL Methods in Computational Neuroscience Course
by Stephen A. Baccus Feb. 18, 2022

Feb. 18, 2022

Applications are open for the Methods in Computational Neuroscience course at the Marine Biology Laboratory in Woods Hole, MA. The course will run from July 28 to August 24, 2022, and the online application form can be found at: https://www.mbl.edu/education/courses/methods-in-computational-neuroscience/<https://ws2.mbl.edu/studentapp/studentapp.asp?courseid=MCN> The course application deadline is *March 30*. The course covers a range of topics in computational neuroscience including neuronal biophysics, neural coding & information processing, circuit dynamics, learning & memory, motor control, and cognitive processing & disease. In addition, numerous tutorials and problem sets will cover a broad range of computational and mathematical modeling methods. The course strongly emphasizes the collaboration between theory and experiment in solving neuroscience problems, and lectures will be given by a mixture of theorists and experimentalists. The final weeks of the course are primarily reserved for work on projects that students design in collaboration with the resident faculty. Further information can be found on the MCN website: http://www.mbl.edu/mcn/ 2022 Course Directors: Stephen Baccus, Stanford University Xiao Jing Wang, New York University 2022 Faculty: Larry Abbott, Columbia University Tim Behrens, Univ. College London Emery Brown, MIT Randy Buckner, Harvard University Anne Churchland, UCLA Claudia Clopath, Imp. College London Albert Compte, IDIBAPS Shaul Druckmann, Stanford University Uri Eden, Boston University Bard Ermentrout, U. of Pittsburgh Adrienne Fairhall, U. of Washington Michale Fee, MIT James Fitzgerald, Janelia Res., HHMI Loren Frank, UCSF Stefano Fusi, Columbia University Surya Ganguli, Stanford University Paul Glimcher, New York University Mark Goldman, UC Davis Nancy Kopell, Boston University Eve Marder, Brandeis University Yiota Poirazi, IMBB-FORTH David Redish, U. Minnesota Terry Sejnowski, Salk Institute Reza Shadmehr, Johns Hopkins Univ. Sara Solla, Northwestern University Haim Sompolinsky, Hebrew University Nelson Spruston, Janelia Res., HHMI Josh Tenenbaum, MIT Nao Uchida, Harvard University Greg Wayne, DeepMind

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[jobs] Phd opportunity in Lille, France - Spiking Neural Networks for Video Analysis
by Ioan Marius BILASCO Feb. 17, 2022

Feb. 17, 2022

The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is looking to recruit a PhD student starting as soon as possible on the following subject : Spiking Neural Networks for Video Analysis The FOX research group is part of the CRIStAL laboratory (University of Lille, CNRS), located in Lille, France. We focus on video analysis for human behavior understanding. Specifically, we develop spatio-temporal models of motions for tasks such as abnormal event detection, emotion recognition, and face alignment. We are also involved in IRCICA (CNRS), a research institute promoting multidisciplanary research. At IRCICA, we collaborate with computer scientists and experts in electronics engineering to create new models of neural networks that can be implemented on low-power hardware architectures. Recently, we designed state-of-the-art models for image recognition with single and multi-layer unsupervised spiking neural networks. We were among the first to succesfully apply unsupervised SNNs on modern datasets of computer vision. We also developed our own SNN simulator to support experiments with SNN on computer vision problems. Our work is published in major journals (Pattern Recognition, IEEE Trans. on Affective Computing) and conferences (WACV, IJCNN) in the field. Abstract: Spiking Neural Network have recently been evaluated on classical image recognition tasks [1]. This work has highlighted their promising performances in this domain and have identified ways to improve them to be competitive with comparable deep learning approaches. In particular, it demonstrated the ability of SNN architectures to learn relevant patterns for static pattern recognition in an unsupervised manner. However, dealing with static images is not enough, and the computer vision community is increasingly interested in video analysis, for two reasons. First, video data is more and more common and corresponds to a wide range of applications (video surveillance, audio-visual productions, autonomous vehicles...). Second, this data is richer than isolated static images, and thus offers the possibility to develop more effective systems, e.g. using motion information. Thus, it is recognized in the community that modeling motion in videos is more relevant than studying visual appearance alone for tasks such as action or emotion recognition. The next step for SNNs is therefore to study their ability to model motion rather than, or in addition to, image appearance. The goal of the Ph.D. candidate will be to explore the use of SNNs for space-time modeling in videos. This work will be targeted towards applications in human behavior understanding and especially action recognition. More specifically, the Ph.D. candidate is expected to: * identify what issues may prevent space-time modeling with SNNs and how they can be circumvented; * propose new supervised and unsupervised SNN models for motion modeling, which are compatible with hardware implementations on ultra-low power devices; * evaluate the proposed models on standard datasets for video analysis. Detailed subject: https://bit.ly/stssnnfox <https://bit.ly/stssnnfox> Candidates must hold a Master degree (or an equivalent degree) in Computer Science, Statistics, Applied Mathematics or a related field. Experience in one or more of the following is a plus: • image processing, computer vision; • machine learning; • bio-inspired computing; • research methodology (literature review, experimentation…). Candidates should have the following skills: • good proficiency in English, both spoken and written; • scientific writing; • programming (experience in C++ is a plus, but not mandatory). This PHD thesis will be funded in the framework of the ANVI-Luxant industrial chair. The general objective of the Chair is to make a scientific and technological progress in the mastery of emerging information processing architectures such as neuromorphic architectures as an embedded artificial intelligence technique. The use-case studies will come from video protection in the context of retail and transportation. The candidate will be funded for 3 years; he/she is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits (health insurance, retirement fund, and paid vacations). The position is located in Lille, France. With over 110 000 students, the metropolitan area of Lille is one France's top education student cities. The European Doctoral College Lille Nord-Pas de Calais is headquartered in Lille Metropole and includes 3,000 PhD Doctorate students supported by university research laboratories. Lille has a convenient location in the European high-speed rail network. It lies on the Eurostar line to London (1:20 hour journey). The French TGV network also puts it only 1 hour from Paris, 35 mn from Brussels, and a short trips to other major centres in France such as Paris, Marseille and Lyon. For application, please send the following information in a single PDF file to Dr. Marius Bilasco (marius.bilasco(a)univ-lille.fr <mailto:marius.bilasco@univ-lille.fr>) with subject [PhD_Luxant-ANVI]: * A cover letter. * A curriculum vitae, including a list of publications, if any. * Transcripts of grades of Master's degree. * The contact information of two references (and any letters if available). We look forward to receiving your application as soon as possible, but no later than 26.3.2022

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[jobs] PhD opportunity - Spatio-temporal augmentation models for motion pattern learning - AI_PhD@Lille, France
by Ioan Marius BILASCO Feb. 17, 2022

Feb. 17, 2022

The FOX team from the CRIStAL laboratory (UMR CNRS), Lille France is looking to recruit a PhD student starting on October 1st 2022 on the following subject : Spatio-temporal data augmentation models for motion pattern learning using deep learning: applications to facial analysis in the wild The FOX research group is part of the CRIStAL laboratory (University of Lille, CNRS), located in Lille, France. We focus on video analysis for human behavior understanding. Specifically, we develop spatio-temporal models of motions for tasks such as abnormal event detection, emotion recognition, and face alignment. Our work is published in major journals (Pattern Recognition, IEEE Trans. on Affective Computing) and conferences (WACV, IJCNN). Abstract: Facial expression analysis is a well-studied field when dealing with segmented and constrained data captured in lab conditions. However, many challenges must still be addressed for building in-the-wild solutions that account for various motion intensities, strong head movements during expressions, the spotting of the subsequence containing the expression, partially occluded faces, etc. In recent years, learned features based on deep learning architectures were proposed in order to deal with these challenges. Deep learning is characterized by neural architectures that depend on a huge number of parameters. The convergence of these neural networks and the estimation of optimal parameters require large amounts of training data, especially when dealing with spatio-temporal data, particulary adequate for facial expression recognition. The quantity, but also the quality, of the data and its capacity to reflect the addressed challenges are key elements for training properly the networks. Augmenting the data artificially in an intelligent and controlled way is an interesting solution. The augmentation techniques identified in the literature are mainly focused on image augmentation and consist of scaling, rotation, and flipping operations, or they make use of more complex adversarial training. These techniques can be applied at the frame level, but there is a need for sequence level augmentation in order to better control the augmentation process and ensure the absence of temporal artifacts that might bias the learning process. The generation of dynamic frontal facial expressions has already been addressed in the literature. The goal of this Ph.D. is to conceive new space-time augmentation methods for unconstrained facial analysis (involving head movements, occultations, etc.). Attention should be paid in assessing the quality standards related to facial expression requirements: stability over time, absence of facial artifacts, etc. More specifically, the Ph.D. can didate is expected to conceive augmentation architectures that address various challenges (motion intensities, head movements) while maintaining temporal stability and eliminating facial artifacts. More details are available here : https://bit.ly/staugm_motion <http://bit.ly/st_augm_motion> Candidates must hold a Master degree in Computer Science, Statistics, Applied Mathematics or a related field. Experience in one or more of the following is a plus: • image processing, computer vision; • machine learning; • research methodology (literature review, experimentation…). Candidates should have the following skills: • good proficiency in English, both spoken and written; • scientific writing; • programming (experience in C++ is a plus, but not mandatory). This PHD thesis will be funded in the framework of the AI_PhD@Lilleprogram. http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/ <http://www.isite-ulne.fr/index.php/en/phd-in-artificial-intelligence/> The candidate will be funded for 3 years; he/she is expected to defend his/her thesis and graduate by the end of the contract. The monthly gross salary is around 2000€, including benefits (health insurance, retirement fund, and paid vacations). Additional financial support is expected in the framework of the AI_PhD@Lille program. The position is located in Lille, France. With over 110 000 students, the metropolitan area of Lille is one France's top education student cities. The European Doctoral College Lille Nord-Pas de Calais is headquartered in Lille Metropole and includes 3,000 PhD Doctorate students supported by university research laboratories. Lille has a convenient location in the European high-speed rail network. It lies on the Eurostar line to London (1:20 hour journey). The French TGV network also puts it only 1 hour from Paris, 35 mn from Brussels, and a short trips to other major centres in France such as Paris, Marseille and Lyon. We look forward to receiving your application as soon as possible, but no later than 26.03.2021.

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job: staff scientist/team leader at NIMH
by Francisco Pereira Feb. 17, 2022

Feb. 17, 2022

The National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), is recruiting for a Staff Scientist/Team Leader position to direct the Scientific and Statistical Computing Core/AFNI Team (https://afni.nimh.nih.gov).The primary function of this group is to support multimodal neuroimaging research in the intramural research program (IRP) at NIH. This includes (i) applying, extending, and developing new and existing software tools and techniques to conduct pioneering analyses on a diverse array of neuroimaging modalities (e.g. MRI, DTI, fMRI, MEG, PET, and EEG), (ii) advising and training IRP researchers on neuroimaging analysis methods, and (iii) leading the future growth and development of the AFNI code base ( https://github.com/afni/afni) Primary responsibilities of the Team Leader will include serving as a partner in and facilitator of neuroimaging research within the NIMH IRP; providing guidance on study design; and ensuring that appropriate standards of quality, rigor, and transparency are maintained. They will also act in coordination with the leaders of the Data Science and Sharing ( https://cmn.nimh.nih.gov/dsst) and Machine Learning Teams ( https://cmn.nimh.nih.gov/mlt) Finally, they will be responsible for management and supervision of approximately 9 full time staff members including software engineers, statisticians, and other biomedical science professionals. The successful candidate must be committed to scientific excellence and highly collaborative research, ensuring an immense impact on the fields of brain imaging and mental health research. Candidates must have earned a Ph.D. or M.D. and demonstrate experience as an outstanding scientist. Applicants must possess the ability to analyze, prioritize, and delegate resources while managing multiple projects. Expertise and experience should include demonstration of positive and collegial interactions with staff at all levels and the ability to communicate and promote their research, software, and scholarly contributions. Applicants should also have knowledge and experience in software engineering, including writing, distributing, maintaining, and supporting scientific software for a large user base while adhering to community recognized standards and practices. Please see the official posting https://cmn.nimh.nih.gov/compcore-job for more details, including instructions on how to apply.

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new Open Access Perspective
by Daniel Gardner Feb. 17, 2022

Feb. 17, 2022

What do brains compute, and how do they do it? Has evolution conserved a small canonical set of computations performed by neural circuitry? In a just-published invited Perspective for the Journal of Computational Neuroscience, I present arguments for (and against) these fundamental questions. The title, Where are the cores in this thing? …and what are they computing? (with apologies to Larry Abbott) is of course an homage to Larry's 'Where are the switches on this thing?' It's Open Access and freely downloadable (tell your students and friends) at: https://link.springer.com/content/pdf/10.1007/s10827-021-00809-1.pdf The very first sentence is: "Evolution conserves neural components and connectivity—does this extend to computations and circuitry?" I go on to note that independent of specific microcircuit connectivity, some key neuronal and network properties, conserved across all nervous systems, may be essential for computation. Candidates include irregular firing, stochastic plastic synapses, alternate A/D PSPs/spikes, nonlinear f-I, cellular longevity, dynamic but stable networks with both fixed and adaptive features, and local processing linked by distant connectivity for coherence and modulation. Collegially, I invite discussion. I also urge neuroscientists to add these questions to those that guide their own research, and I offer community efforts to widen the scope. ...Daniel Gardner, Ph.D. dan(a)med.cornell.edu dg458(a)columbia.edu Head, Laboratory of Neuroinformatics Professor Physiology & Biophysics/Neurology/Neuroscience Weill Medical College of Cornell University lab: (212) 746-6373 cell: (917) 902-0654

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NEST Conference 2022 - registration is open
by Hans Ekkehard Plesser Feb. 17, 2022

Feb. 17, 2022

Dear Colleagues, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2022. The NEST Conference provides an opportunity for the NEST Community to meet, exchange success stories, swap advice, learn about current developments in and around NEST spiking network simulation and its application. We particularly encourage young scientists to participate in the conference! This year's conference will again take place as a virtual event on Thursday/Friday 23/24 June 2022. Register now! For more information please visit the conference website https://nest-simulator.org/conference We are looking forward to seeing you all in June! Hans Ekkehard Plesser and colleagues -- Prof. Dr. Hans Ekkehard Plesser Head, Department of Data Science Faculty of Science and Technology Norwegian University of Life Sciences PO Box 5003, 1432 Aas, Norway Phone +47 6723 1560 Email hans.ekkehard.plesser(a)nmbu.no<mailto:hans.ekkehard.plesser@nmbu.no> Home http://arken.nmbu.no/~plesser

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Postdoctoral Researcher in Medical Interactive Technologies
by Mastaneh Torkamani Feb. 17, 2022

Feb. 17, 2022

The University of Eastern Finland is inviting applications for a Postdoctoral Researcher position in Medical Interactive Technologies on the Joensuu Campus. The position will be filled for a 2-year term from 1.3.2022 (or as agreed). Please find more information below and submit your application no later than 25.2.2022. We are looking for a highly motivated and self-driven Postdoctoral Researcher to work full-time on an interdisciplinary research project funded by the Academy of Finland: "Next generation sensory monitoring for improving microsurgical skills and performance" led by Associate Professor Roman Bednarik (https://www.uef.fi/en/web/int) As our new Postdoctoral Researcher in Medical Interactive Technologies, you will be working in the School of Computing at the Faculty of Science and Forestry, Joensuu. The project is in collaboration with Kuopio University Hospital, Department of Neurosurgery and Microsurgery Training Center, https://www.microsurgerycenter.com/. The research will focus on deep understanding of sensorimotor perception and action in image-guided surgeries. A large part of the research evolves around the visual and tactile perception that underlies surgeon’s high-level skills, i.e. eye-hand coordination, action planning, and decision making. Situated in bimanual surgical tasks, our research combines human factors, human-robot interaction, computational modeling, machine learning, and computer vision for intelligent skill monitoring and interaction with robotic assistance in surgical procedures. For more information, please see the job advert at https://rekry.saima.fi/certiahome/open_job_view.html?did=5600&jc=16&id=0000… Please submit your application by using our electronic application form no later than 25.2.2022 (by 24.00 hours Finnish time). For further information on the position, please contact associate Professor Roman Bednarik, e-mail roman.bednarik(a)uef.fi, tel. +358 29 4453621 For further information on the application procedure, please contact HR-secretary Noora Hämäläinen, e-mail noora.hamalainen(a)uef.fi, tel. +358 29 4458 302 Best regards, Mastaneh Torkamani Azar, Ph.D. Postdoctoral Researcher Interactive Technologies Research Group School of Computing University of Eastern Finland Email: mastaneh.torkamani(a)uef.fi https://sites.uef.fi/int/

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Postdoc position at UTSA
by Fidel Santamaria Feb. 16, 2022

Feb. 16, 2022

As part of a collaboration with the AFRL, the laboratory of Dr Santamaria at UTSA is looking for a talented postdoc to computationally study how fast thermal gradients impact synaptic mechanisms in dendritic spines. This will be part of a collaborative project between UTSA, AFRL, and other universities where experiments and simulations will combine to understand fundamental plasticity processes in neuronal synapses. The results will have important practical implications in neuroscience and biomedical engineering. The selected person will work on developing deterministic and stochastic models of how biochemical signals, involved in synaptic plasticity, are affected by thermal gradients. Interested individuals should send a CV and cover letter to Dr. Santamaria (fidel.santamaria(a)utsa.edu) The position is for one year, with the possibility of renewal. This position is only open to American citizens. Applicants must have experience with Python or Matlab and a background in physics or engineering is preferred. _______________________________________________________________________________ Fidel Santamaria, PhD Professor Department of Neuroscience, Development, and Regenerative Biology University of Texas at San Antonio www.utsa.edu/SantamariaLab<http://www.utsa.edu/SantamariaLab> Office: BSB 1.03.30 Tel +1(210) 458 6910

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Special issue of SIAP on the life sciences
by Kresimir josic Feb. 14, 2022

Feb. 14, 2022

SIAM Journal on Applied Mathematics (SIAP) invites contributions for a special section on the life sciences to highlight the use and development of mathematical models and analysis tools to address life sciences issues that are of true biological, medical, or industrial relevance. Papers submitted to this special section will be peer-reviewed, subject to the review criteria of SIAP. Papers will initially be handled by guest editors. The window for submissions runs from February 15 to June 30, 2022. For information about this issue please see: https://bit.ly/3LsPxcw <https://bit.ly/3LsPxcw>

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Third session of the Brains Through Time Reading Club
by manuel molano Feb. 14, 2022

Feb. 14, 2022

Dear all, I would like to bring to your attention the third session of the Brains Through Time Reading Club<https://sites.google.com/view/bbtreadingclub/home>! It will be on February 23rd (4pm CEST / 10 EST) and will include the participation of Leonard Maler<https://www.uottawa.ca/brain/people/maler-leonard>, Idoia Quintana-Urzainqui<https://twitter.com/idoiaeu>, Luis Puelles<https://scholar.google.com/citations?user=nIoLKVAAAAAJ&hl=en> and Paul Cisek<https://cisek.org/pavel/>. We will discuss the main ideas of the third chapter of the book: The origin of jaws and paired fins. You can register HERE<https://www.crowdcast.io/e/brains-through-time-3>. And in case you missed the previous sessions, you can watch them HERE<https://www.youtube.com/channel/UCDWFS3MBL9OhmpmL6mFmxsg/featured>.

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Computational Neuroscience Summer school in South Africa, DEADLINE TOMORROW
by Tim Vogels Feb. 14, 2022

Feb. 14, 2022

Dear all, This is a reminder that the application DEADLINE for the next IMBIZO is TOMORROW. If you, or anyone you know who would benefit from this opportunity please consider applying: IBRO-SIMONS COMPUTATIONAL NEUROSCIENCE IMBIZO #isiCNI2022 12 August - 4 September 2022, Noordhoek Beach, Cape Town, South Africa http://imbizo.africa/ <http://imbizo.africa/> Application deadline: 15th February 2022 The #isiCNI2022 is a southern hemisphere summer school aiming to promote computational neuroscience in Africa. It will bring together international and local students under the tutelage of the world's leading experts in the field. Like its international sister courses, this four-week summer school aims to teach central ideas, methods, and practices of modern computational neuroscience through a combination of lectures and hands-on project work. Mornings will be devoted to lectures on topics across the breadth of computational neuroscience, including experimental underpinnings and machine learning analogues. The rest of the day will be spent working on research projects under the close supervision of expert tutors and faculty. Individual research projects will focus on the modelling of neurons, neural systems, behaviour, the analysis of state-of-the-art neural data, and the development of theories to explain experimental observations. New this year is a week focused on neuroscience-inspired machine learning! Who should apply? This course is aimed at Masters and early-PhD level students though Honours or advanced undergraduates may also apply. Postdoctoral students who can motivate why the course would benefit them are also encouraged to apply, Students should have sufficient quantitative skills, (e.g. a background in mathematics, physics, computer science, statistics, engineering or related field). Some knowledge of neural biology will be useful but not essential. Experimental neuroscience students are encouraged to apply, but should ensure that they have a reasonable level of quantitative proficiency (i.e. at least second-year level mathematics or statistics and have done at least one course in computer programming). Please distribute this information as widely as you can. Essential details * Fee (which covers tuition, lodging, and meals): 1100 EUR Thanks to our generous sponsors, significant financial assistance is available to reduce and waiver fees for students, particularly for African applicants. We also hope to provide some travel bursaries for international students. If you are in need of financial assistance to attend the Imbizo, please state so clearly in the relevant section of your application. The Imbizo is planned to be hosted in person, with everyone following our COVID policy <https://imbizo.africa/covid-policy/>. If it is unsafe to hold the summer school, we will follow up with further information at the appropriate time. * Application deadline: 15th February 2022 * Notification of results: late April 2022 Information and application https://imbizo.africa/ <https://imbizo.africa/> Questions? isicn.imbizo(a)gmail.com <mailto:isicn.imbizo@gmail.com> What is an Imbizo? \ɪmˈbiːzɒ\ | Xhosa - Zulu A gathering of the people to share knowledge. FACULTY Demba Ba - Harvard University Adrienne Fairhall - University of Washington Peter Latham - University College London Daphne Bavelier - University of Geneva Timothy Lillicrap - DeepMind Jonathan Pillow - Princeton University Joseph Raimondo - University of Cape Town Mackenzie Mathis - EPFL Lausanne Athanassia Papoutsi - IMBB-FORTH Evan Schaffer - Columbia University Henning Sprekeler - Technical University of Berlin Thomas Tagoe - University of Ghana Tim Vogels - Institute of Science and Technology Austria Blake Richards - McGill University Alex Pouget - University of Geneva TUTORS Mohamed Abdelhack - Krembil Centre for Neuroinformatics Annik Carson - McGill University Spiros Chavlis - IMBB-FORTH Christopher Currin - Institute of Science and Technology Austria Sanjukta Krishnaopal - University College London Marjorie Xie - Columbia University ORGANISERS Demba Ba (Harvard University) Christopher Currin (Institute of Science and Technology Austria) Peter Latham (Gatsby Unit for Computational Neuroscience) Joseph Raimondo (University of Cape Town) Emma Vaughan (Imbizo Logistics) Tim Vogels (Institute of Science and Technology Austria) Sponsors The isiCNI is made possible by the generous support from the Simons Foundation <https://www.simonsfoundation.org/> and the International Brain Research Organisation (IBRO) <https://ibro.org/>, as well as the Wellcome Trust <https://wellcome.org/>, Deep Mind <https://deepmind.com/> and Wits University <https://www.wits.ac.za/> Organizational Affiliates University of Cape Town, University College London, Institute of Science and Technology Austria, TReND in Africa <http://trendinafrica.org/>, Neuroscience Institute <http://www.neuroscience.uct.ac.za/>, Gatsby Foundation <http://www.gatsby.org.uk/>, IBRO African Center for Advanced Training in Neurosciences at UCT

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[Jobs] Available Position: Professor in Artificial Intelligence (AI) at the University of Plymouth, UK
by Amir Aly Feb. 12, 2022

Feb. 12, 2022

Dear All, *Apologies for cross-posting* *The School of Engineering, Computing, and Mathematics (SECaM)*, at the University of Plymouth has a unique opportunity for an outstanding academic and their team to join the School and strengthen our research, leadership, and collaboration in *Artificial Intelligence*. This is an opportunity to establish senior academic direction and growth in this core area of Artificial Intelligence and to build on multi-disciplinary collaboration, supporting the School’s strategic objectives to create a critical mass of expertise in the area of Artificial Intelligence aligning directly with Industry 4.0 and future national and international research opportunities and skills development needs. You will work together with colleagues to create a vision for our existing Centre for Robotics and Neural Systems (CRNS), which builds on the world-leading and international excellence performance in the field of computer science, social and cognitive robotics, neural computation, and others. You will join a community of over 100 staff and 2000 students who work alongside one another in world-leading facilities. This synergistic mix of academic disciplines has research strength aligned with challenges of the fourth Industrial Revolution, clean growth, digitalization, autonomy, and health technology, helping to develop pioneering, innovative solutions to real-world problems that have a genuinely positive impact upon society. The deadline is 25th February. For more information and to apply, please visit: https://hrservices.plymouth.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.o… <https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fhrservice…> Regards ---------------- *Dr. Amir Aly* Lecturer in Artificial Intelligence and Robotics Center for Robotics and Neural Systems (CRNS) School of Engineering, Computing, and Mathematics Room B332, Portland Square, Drake Circus, PL4 8AA University of Plymouth, UK

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Running your new lab: tips and tricks - Panel discussion - 10th March 2022, 12h GMT / 13h CET
by Sidarus, Nura Feb. 11, 2022

Feb. 11, 2022

Running your new lab: tips and tricks 10th March 2022 12h GMT / 13h CET Online To commemorate International Women's Day, we are delighted to host this practical panel discussion to provide support and inspiration for starting and running a lab. Save the date and do not miss the opportunity to share experiences with leading women scientists in cognitive sciences across Europe. Details and registration: https://www.eventbrite.co.uk/e/running-your-new-lab-tips-and-tricks-tickets… Our panel: * Prof Nivedita Mani, Professor of Psychology of Language, Georg-August-Universität Göttingen, Germany * Dr Jessie Ricketts, Reader in Psychology, Department of Psychology, Royal Holloway, University of London, UK * Prof Agnieszka Wykowska, Professor in Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia (IIT), Genova, Italy Organised and facilitated by Nura Sidarus (Royal Holloway University of London) & co-facilitated by María Jesús Maraver (Universidade de Lisboa). Supported by Women in Cognitive Science - Europe (WiCS-E) & Royal Holloway University of London. __________________________________________________________ Dr Nura Sidarus (she/her) Lecturer | Department of Psychology Royal Holloway University of London | Egham | Surrey | TW20 0EX Office: Wolfson 248 | Tel: +44 (0) 1784 44 3717 Affiliate Member | Institute of Cognitive Neuroscience, UCL nura.sidarus(a)rhul.ac.uk<mailto:%20nura.sidarus@rhul.ac.uk> | www.nurasidarus.com<http://www.nurasidarus.com/> __________________________________________________________ This email, its contents and any attachments are intended solely for the addressee and may contain confidential information. In certain circumstances, it may also be subject to legal privilege. Any unauthorised use, disclosure, or copying is not permitted. If you have received this email in error, please notify us and immediately and permanently delete it. Any views or opinions expressed in personal emails are solely those of the author and do not necessarily represent those of Royal Holloway, University of London. It is your responsibility to ensure that this email and any attachments are virus free.

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crowd-sourcing COSYNE post-review score sheet
by PERRINET Laurent Feb. 11, 2022

Feb. 11, 2022

Dear community COSYNE is a great conference which plays a pivotal role in our field. Raw numbers we were given are * 881 submitted abstracts * 215 independent reviewers * 2639 reviews If you have submitted an abstract (or several) you have recently received your scores. I am not affiliated to COSYNE - yet I would like to contribute in some way and would like to ask one minute of your time to report the raw scores from your reviewers: https://forms.gle/p7eG1p6dJAkr4Cyg7 (Do one form per abstract.) For this crowd-sourcing effort to have a most positive impact, I will share the results and summarize in a few lines them in one week time (11/02). The more numerous your feedbacks, the higher the precision of results! Thanks in advance for your action, Laurent PS: if any similar initiative already exists, I'll be more than willing to receive feedback -- Laurent Perrinet - INT (UMR 7289) AMU/CNRS https://laurentperrinet.github.io/

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12 Postdoctoral positions - Ruhr University Bochum
by Vinita Samarasinghe Feb. 11, 2022

Feb. 11, 2022

*12 postdoctoral positions (min. 3 years) in Neuroscience, Psychology, and Philosophy of Mind*** The Ruhr University Bochum and the University of Duisburg-Essen are among Germany’s leading research universities. They draw their strength from both the diversity and the proximity of natural sciences, humanities, and engineering disciplines on coherent campuses. These highly dynamic settings enable researchers to work across traditional boundaries of academic subjects and faculties. *We are searching for advanced postdocs who ideally already have ca. 2 years of research experience*and want to continue their careers in an inspiring environment that fosters interdisciplinary approaches. We aim to install an outstanding group of postdocs, starting on 1^st of April 2022 at the earliest, who are tightly integrated with the research focus “THINK@Ruhr”. Details of this research program as well as all further procedural details are outlined on the website: _*www.thinkatruhr.de* <http://www.ThinkAtRuhr.de/>_. Deadline of application is February 27, 2022. *Applicants should have *an excellent PhD in a research topic related to neuroscience, psychology, or philosophy of mind and should already have published in high-ranking peer-reviewed journals. The postdoc positions offer a focus on research only (no teaching or administration). It is expected that the successful candidate will submit a grant proposal to establish her/his own research group (e.g. ERC Starting Grant, Emmy Noether Research Grant, Sofja Kovalevskaja Award, etc.) during the postdoctoral period. Candidate selection will be based on whether submission of such a proposal is a realistic goal in addition to usual measures of scientific excellence. The candidates will be associated with faculty members who are named on the website thinkatruhr.de. THINK@Ruhr will foster and support any research activities of the postdocs. Our universities offer attractive academic career track options and family friendly conditions. For more information including application procedure and contact information please visit https://thinkatruhr.de/what-we-offer/

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PostDoc position in Göttingen: Retina research and computational neuroscience
by Gollisch, Tim Feb. 11, 2022

Feb. 11, 2022

An exciting full-time postdoc position is available in the lab of Tim Gollisch at the University Medical Center Göttingen, Germany. The project centers on investigating information processing and neural coding in the neural network of the vertebrate retina, using a combination of experimental recordings with computational modeling and data analysis. The postdoc will contribute to our ongoing work under the ERC Consolidator Grant project CODE4Vision (https://www.retina.uni-goettingen.de/code4vision/) The work includes participation in recordings from the isolated retina (mostly mouse) with multielectrodes, using both wild-type retinas and optogenetic retina models of vision restoration therapy. Patch-clamp recordings are also a possibility. A strong focus will then be to combine these experiments with novel tools for data analysis and mathematical modeling, using cascade-type models (linear-nonlinear models and beyond), artificial neural networks, or machine-learning techniques to analyze the retinal network. Our research group is integrated into the strong and lively neuroscience research community of Göttingen, including the university, several Max Planck Institutes, the German Primate Center, and the European Neuroscience Institute. We are also part of the Bernstein Center for Computational Neuroscience Göttingen and of the Collaborative Research Centers "Cellular Mechanisms of Sensory Processing" (http://sfb889.uni-goettingen.de/) and "Mathematics of Experiment" (https://www.uni-goettingen.de/en/628179.html) For more information about the research group, please visit the group's website: https://www.retina.uni-goettingen.de/. We are looking for a highly motivated scientist with a good background in electrophysiology or computational/theoretical neuroscience. Experience in both of these areas is a bonus, but not required. Experience in computer programming or in scripting of data analysis routines is also a plus. The position is for 2 years initially with the possibility for renewal. Please send your application to Tim Gollisch (tim.gollisch(a)med.uni-goettingen.de<mailto:tim.gollisch@med.uni-goettingen.de>), including a CV, a statement of what interests you about the group's work, and contact details for two references. The University Medical Center Göttingen is an equal opportunities employer, and women are especially encouraged to apply. Applicants with disabilities and equal qualifications will be given preferential treatment.

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Groningen Spring School on Cognitive Modelling (4 to 8 April 2022)
by Thomas Feb. 11, 2022

Feb. 11, 2022

Date: 4 to 8 April 2022 Location: Groningen, the Netherlands Fee: € 300 (late fee after March 7 will be € 350) www.cognitive-modeling.com/springschool After an enforced two-year covid break, we are excited to announce the fifth Groningen Spring School on Cognitive Modeling (4 to 8 April 2022), with a great lineup of speakers. The Spring School will cover four different modeling paradigms: ACT-R, Nengo, PRIMs, and discriminative learning. It thereby offers a unique opportunity to learn the relative strengths and weaknesses of these approaches. In addition, this year we are offering a lecture series on dynamical systems, which should be interesting for anyone looking into modeling cognitive dynamics at some or other level of abstraction. We recommend this lecture series as an excellent combination with Nengo, for those interested in neuromorphic computing. The first day will provide an introduction to all five topics. From day two, spring school students will be asked to commit to one topic, for which they will attend lectures as well as hands-on tutorials to get practical experience at working with the paradigm. In addition, students can sign up for a second topic, for which they will attend lectures only. All students are invited to join a series of plenary research talks on the different paradigms. Please feel free to forward the information to anyone who might be interested in the Spring School. The Spring School team PS: Please note that, due to its interactive character, the spring school will be held as an offline-only event. We will not offer the option for hybrid or online teaching. In case the spring school needs to be cancelled at the last minute after all, participants will receive a full reimbursement. ______________ ACT-R Teachers: Jelmer Borst, Stephen Jones, & Katja Mehlhorn (University of Groningen) Website: http://act-r.psy.cmu.edu. ACT-R is a high-level cognitive theory and simulation system for developing cognitive models for tasks that vary from simple reaction time experiments to driving a car, learning algebra, and air traffic control. ACT-R can be used to develop process models of a task at a symbolic level. Participants will follow a compressed five-day version of the traditional summer school curriculum. We will also cover the connection between ACT-R and fMRI. Nengo Teacher: Terry Stewart and Andreas Stöckel (University of Waterloo) Website: http://www.nengo.ca Nengo is a toolkit for converting high-level cognitive theories into low-level spiking neuron implementations. In this way, aspects of model performance such as response accuracy and reaction times emerge as a consequence of neural parameters such as the neurotransmitter time constants. It has been used to model adaptive motor control, visual attention, serial list memory, reinforcement learning, Tower of Hanoi, and fluid intelligence. Participants will learn to construct these kinds of models, starting with generic tasks like representing values and positions, and ending with full production-like systems. There will also be special emphasis on extracting various forms of data out of a model, such that it can be compared to experimental data. PRIMs Teacher: Niels Taatgen (University of Groningen) Website: https://www.ai.rug.nl/~niels/prims/index.html How do people handle and prioritize multiple tasks? How can we learn something in the context of one task, and partially benefit from it in another task? The goal of PRIMs is to cross the artificial boundary that most cognitive architectures have imposed on themselves by studying single tasks. It has mechanisms to model transfer of cognitive skills, and the competition between multiple goals. In the tutorial we will look at how PRIMs can model phenomena of cognitive transfer and cognitive training, and how multiple goals compete for priority in models of distraction. Discriminative learning and the lexicon: NDL and LDL Harald Baayen, Yu-Ying Chuang, and Maria Heitmeier University of Tuebingen NDL and LDL are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error which calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the endstate of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing. In this course, we will show how comprehension and production of morphologically complex words can be modeled successfully with the "Discriminative Lexicon" model for a range of languages (Hebrew, Maltese, English, German, Dutch, Mandarin Chinese, Korean, Kinyarwanda, Estonian, and Finnish). We will discuss the kinds of form and meaning representations that can be set up, including form features derived from the speech signal for auditory comprehension and semantic features grounded in distributional semantics. Furthermore, we will provide a survey of the measures that can be derived from the model mappings to predict empirical response variables such as reaction times in primed and unprimed lexical decision, spoken word duration, and tongue movements during speaking. Finally, participants will receive some training in using the JudiLing package for Julia. This package provides optimized code for implementing and evaluating components of a "discriminative lexicon" for a given language. Dynamical Systems: a Navigation Guide Teacher: Herbert Jaeger (University of Groningen) This lecture series gives a broad overview over the zillions of formal models and methods invented by mathematicians and physicists for describing “dynamical systems”. Here is a list of covered items: Finite-state automata (with and without input, deterministic and non-deterministic, probabilistic), hidden Markov models and partially observable Markov decision processes, cellular automata, dynamical Bayesian networks, iterated function systems, ordinary differential equations, stochastic differential equations, delay differential equations, partial differential equations, (neural) field equations, Takens’ theorem, the engineering view on “signals”, describing sequential data by grammars, Chomsky hierarchy, exponential and power-law long-range interactions, attractors, structural stability, bifurcations, phase transitions, topological dynamics, nonautonomous attractor concepts. In the lectures I work out the connecting lines between these different models and methods, aiming at drawing the "big picture". — Thomas Tiotto, PhD Candidate, CogniGron - Groningen Cognitive Systems and Materials University of Groningen Nijenborgh 9, 9747 AG Groningen, The Netherlands Email: t.f.tiotto(a)rug.nl Profile: https://www.rug.nl/staff/t.f.tiotto/ Office: 5161 0318

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Postdoctoral Fellowship in Machine Learning for Brain Connectivity in Clinical Neuroscience
by Paolo Avesani Feb. 11, 2022

Feb. 11, 2022

A postdoctoral fellowship in Machine Learning for Brain Connectivity in Clinical Neuroscience We are pleased to announce the opening of one Postdoctoral Fellowship at the Neuroinformatics Lab, an interdisciplinary initiative between the Center for Mind/Brain Sciences (CIMeC) of the University of Trento, and the Center for Digital Health of Fondazione Bruno Kessler. The position is part of the “Neusurplan” project, an integrated approach to neurosurgery planning based on multimodal and longitudinal data. The goal is to pursue an integrated approach to pre-operative neurosurgical planning, combining structural and functional characterization of brain connectivity. The data driven strategy will take advantage of a unique dataset of intra-operative points of directed electrical stimulation and the related functional responses. In this project, the candidate will pursue research on machine learning methods for neuroimaging data analysis to study and characterize brain connectivity, with applications to longitudinal studies and clinical practice. The ideal candidate should have a mixed background in neuroimaging techniques and numerate disciplines, like computer science, engineering, physics, or mathematics. This project is in collaboration with the Division of Neurosurgery, S. Chiara Hospital, Trento (IT). The position is for a 2 year Postdoc Fellowship (May, 2022 - April, 2024). We welcome expressions of interest for this position, please contact Paolo Avesani (paolo.avesani@ <avesani(a)fbk.eu>unitn.it) and/or Emanuele Olivetti ( olivetti(a)fbk.eu) with your CV and a statement of intent. The University of Trento ranks among top Italian Universities ( https://www.unitn.it/en/ateneo/1636/rankings) Fondazione Bruno Kessler ranks first among the Italian research centers in Engineering and Computer Science ( https://magazine.fbk.eu/en/news/fbk-ranks-1st-in-italy-for-scientific-excel… ). To also consider a work/life balance, there is more than our passion for translational research. You can check these pointers for a flavor of life quality in Trentino: https://www.visittrentino.info/en; https://www.discovertrento.it/en A few papers related to the project are below: Bertò G,et al., (2021) Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation, Neuroimage, 224 https://doi.org/10.1016/j.neuroimage.2020.117402 Sarubbo S, et al., (2020) Mapping critical cortical hubs and white matter pathways by direct electrical stimulation: an original functional atlas of the human brain, Neuroimage, 205 https://doi.org/10.1016/j.neuroimage.2019.116237 Astolfi P, et al., (2020) Tractogram filtering of anatomically non-plausible fibers with geometric deep learning, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) LNCS, vol 12267. Springer https://doi.org/10.1007/978-3-030-59728-3_29 Sarubbo S, et al., (2021) Planning brain tumor resection using a probabilistic atlas of cortical and subcortical structures critical for functional processing: a proof of concept, Operative Neurosurgery, 20(3), 175-183 https://doi.org/10.1093/ons/opaa396 -- -- Le informazioni contenute nella presente comunicazione sono di natura privata e come tali sono da considerarsi riservate ed indirizzate esclusivamente ai destinatari indicati e per le finalità strettamente legate al relativo contenuto. Se avete ricevuto questo messaggio per errore, vi preghiamo di eliminarlo e di inviare una comunicazione all’indirizzo e-mail del mittente. -- The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. If you received this in error, please contact the sender and delete the material.

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Postdoc position - Computational Neuroscience - Ruhr University Bochum
by Vinita Samarasinghe Feb. 10, 2022

Feb. 10, 2022

Deadline Extended! Prof. Sen Cheng, Institute for Neural Computation at the Ruhr University Bochum, invites applications for a full time *Postdoctoral position* (TV-L E13) in Computational Neuroscience. The position starts on July 1, 2022 and is funded for three years. The successful applicant will work on a collaborative project (within the Collaborative Research Center “Extinction Learning” (SFB 1280 <https://sfb1280.ruhr-uni-bochum.de/en/home/>)), together with experimentalists to: * analyze learning dynamics in behavioral, neural, and psychophysiological data, which will be collected by other projects within the SFB 1280, * compare the learning dynamics between individuals, species, learning phases and learning paradigms, * develop algorithms to analyze the learning dynamics, * develop and study computational models of learning dynamics, * coordinate research with other participating projects. Candidates must have: * a doctorate degree in neuroscience, physics, mathematics, electrical/biomedical engineering or a closely related field, * relevant experience in mathematical modeling, * excellent programming skills (e.g., Python, C/C++, Matlab), * excellent communication skills in English, * the ability to work well in a team. Research experience in neuroscience would be a further asset. The position is third party funded and does not have any formal teaching duties attached. The research group is highly dynamic and uses diverse computational modeling approaches including biological neural networks, cognitive modeling, and machine learning to investigate learning and memory in humans and animals. For further information see www.rub.de/cns. The Ruhr University Bochum is home to a vibrant research community in neuroscience and cognitive science. The Institute for Neural Computation is an independent research unit and combines different areas of expertise ranging from experimental and theoretical neuroscience to machine learning and robotics. The Institute for Neural Computation focuses on the dynamics and learning of perception and behavior on a functional level but is otherwise very diverse, ranging from neurophysiology and psychophysics over computational neuroscience to machine learning and technical applications. Please send your application, including CV, transcripts and research statement electronically, as a *single PDF file*, to *samarasinghe(a)ini.rub.de*. In addition, at least two academic references must be sent independently to the above email address. The deadline for applications is *February 20, 2022*. Travel costs for interviews will not be reimbursed. The Ruhr University Bochum is committed to equal opportunity. We strongly encourage applications from qualified women and persons with disabilities. We are committed to providing a supportive work environment for female researchers, in particular those with young children. Our university provides mentoring and coaching opportunities specifically aimed at women in research. We have a strong research network with female role models and will provide opportunities to network with them. Wherever possible, events will be scheduled during regular childcare hours. Special childcare will be arranged if events have to be scheduled outside of regular hours, in case of sickness and during school or daycare closures. Where childcare is not an option parents will be offered a home office solution. If you have any questions please feel free to get in touch with Vinita Samarasinghe (contact below) -- Vinita Samarasinghe M.Sc., M.A. Science Manager Arbeitsgruppe Computational Neuroscience Institut für Neuroinformatik Ruhr-Universität Bochum, NB 3/73 Postfachnummer 110 Universitätstr. 150 D-44801 Bochum Tel: +49 (0)234 32 27996 Email:samarasinghe@ini.rub.de

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4 PhD/postdoc positions in ML for neuroscience, cognitive science and physics
by Ecker, Alexander Feb. 9, 2022

Feb. 9, 2022

We have four open positions for PhD students or postdocs in the Neural Data Science Lab at the Campus Institute Data Science (University of Göttingen) and the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany. If you want to work with us on exciting projects developing machine learning methods for neuroscience, cognitive science and physics, please check out the three short project descriptions below and apply now. Details can also be found on our website: https://eckerlab.org/applications/ Application documents including a short motivation letter, CV, transcript of records (PhD students) and the contact details of two references should be sent to Alexander Ecker: ecker(a)cs.uni-goettingen.de<mailto:ecker@cs.uni-goettingen.de> We value diversity and equality. We therefore particularly encourage international and female applicants as well as applications from underrepresented groups or candidates with disabilities. ============ *(1) Data-driven multi-modal discovery of cell types in the neocortex* Understanding the relationship between structure and function of cortical neurons and circuits is one of the key challenges in neuroscience. In this project, we develop deep learning methods for data-driven identification of excitatory cell types in the visual cortex and to understand how a neuron’s morphology relates to its function. We will harness a unique large-scale functional anatomy dataset: a combination of electron-microscopy reconstructions at sub-micrometer resolution with two-photon functional imaging of nearly all excitatory neurons in one cubic millimeter of the mouse visual cortex. Role: Postdoc (preferred) or PhD student Your profile: Background in systems / computational neuroscience or machine learning (deep learning in particular). Experience with both is a strong plus, but not a requirement. This project is funded by the European Research Council through an ERC Starting Grant and in tight collaboration with Andreas Tolias' lab at Baylor College of Medicine, Houston, TX, USA. Regular visits at our collaborator’s lab in Houston are possible/encouraged. ============ *(2) Action Capture Platform: Deep learning for movement analysis and action classification* Studying social interactions and the underlying cognitive processes in humans and nonhuman primates requires observing and quantifying their behaviour, including body and head orientation, both in interactive lab settings and in the wild. The long-term goal of this project is to develop methods for automated, video-based recognition, motion tracking, and pose estimation of individual nonhuman primates as well as recognition of action types and inter-agent interactions in groups of primates freely moving primaates. We strive for generalisation across a range of primate species and varying real-world environmental conditions without the need to acquire detailed keypoint annotations in every context. The automated behavioural analysis tools developed in this project constitute a key enabling technology for a Collaborative Research Center focused on studying the Cognition of Interaction. Role: Postdoc (preferred) or PhD student Your profile: Strong background in machine learning (deep learning in particular) and/or computer vision. Experience with congitive science is a strong plus, but not a requirement. This project is funded by the German Research Foundation (DFG) as part of Collaborative Research Center SFB 1528 – Cognition of Interaction and in collaboration with several groups at the German Primate Center: Julia Ostner, Claudia Fichtel, Julia Fischer ============ *(3) Holography reconstruction using deep learning* Digital in-line holography is a very powerful method to investigate particles in turbulent fluid flows such as atmospheric clouds, aerosols, etc. using the diffraction patterns of the particles illuminated by coherent light. Currently, wider use of the technique is held back both by a data processing bottleneck and the degradation in hologram quality in field measurements compared to laboratory experiments due to the physical environment. In this project, we will train machine learning algorithms to reconstruct holograms using a combination of real holograms from the lab and the field and simulated datasets. We will start with simple idealized Lorenz-Mie theory, but later extend to full wave-equation calculations including non-flat and non-Gaussian beams, realistic defects in the optical components, dirty optics, camera micro-lens arrays, pixel cross-talk and digitization errors, non-spherical particles including ice crystals, aggregate particles with different indices of refraction, non-perfect collimation, non-perfect alignment, spatial variation in the index of refraction of the air in the view volume due to temperature gradients, multi-scattering from multiple particles, etc. Role: Postdoc (preferred) or PhD student Your profile: Strong background in physics. Experience with machine learning (deep learning in particular) and computer vision is a strong plus, but not a requirement. This project is in collaboration with the lab of Eberhard Bodenschatz and funded by the Max Planck Institute for Dynamics and Self-Organization. ============ -- Alexander Ecker Professor of Data Science Department of Computer Science • Campus Institute Data Science University of Göttingen • Max Planck Inst. for Dynamics and Self-Organization Goldschmidtstr. 1 • room 2.137 • 37077 Göttingen https://eckerlab.org • @alxecker • +49 551 39-21272

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On behalf of Michele Ferrante: New Funding Opportunity: Neuro-Glia Computational Mechanisms Governing Complex Behaviors
by Reinoud Maex Feb. 9, 2022

Feb. 9, 2022

Begin forwarded message: From: Michele Ferrante <mferran1(a)gmu.edu<mailto:mferran1@gmu.edu>> Subject: New Funding Opportunity: Neuro-Glia Computational Mechanisms Governing Complex Behaviors Date: 4 February 2022 at 11:04:45 GMT To: <comp-neuro-owner(a)neuroinformatics.be<mailto:comp-neuro-owner@neuroinformatics.be>> New Funding Opportunity: Neuro-Glia Computational Mechanisms Governing Complex Behaviors https://grants.nih.gov/grants/guide/notice-files/NOT-MH-22-090.html Purpose Background and Rationale This Notice of Special Interest (NOSI) encourages projects to experimentally test mechanistic hypotheses on the role of neuro-glia activity coupling in modulating complex behaviors. The human brain regulates complex behavior by processing information across ~170 billion cells, including ~86 billion neurons and ~84 billion glial cells. The influence of glial cell types (i.e., astrocytes, oligodendrocytes, and microglia) on neural activity may explain behavioral processes across broad spatio-temporal scales and hierarchies. For example, astrocytes may regulate cognitive functions by releasing gliotransmitters that activate hundreds of neuronal synapses at once, regulating system-level short-/long-term plasticity. Activity changes in neuro-oligodendrocytes networks may dynamically regulate myelin axon-sheathing, which in turn may affect action potential conduction, neuronal spike timing, and oscillations linked to cognitive/social/affective processes. Finally, microglia activity-dependent synaptic pruning may alter behaviorally activated neural networks over long time scales. Discovering how mechanistic dysfunctions in neuro-glia interactions may alter behavioral phenotypes relevant to mental health is a challenge with potentially high translational impact. Studying how neuro-glia activity coupling affects complex behavior has been challenging in part due to technical barriers. For example, until recently, the field lacked reliable and selective tools to manipulate glial cells. Biotechnology is now expanding the range of possible investigations into glial function by providing new methods necessary to record and manipulate glial cells with mouse lines and viral methods expressing designer reporters, sensors, and actuators of glial activity. Advances in metabolic imaging and genetically encoded activity measurements allow for simultaneous observation of interactions in neurons and glial activity. Tools are also available for selectively stimulating and silencing glia cells’ calcium signaling in-vivo through designer receptors exclusively activated by designer drugs (DREADDs) and optogenetics. In parallel, basic behavioral neuroscience has been rapidly advanced by integrating system-level neurotechnology with computational modeling. Computational models (e.g., reinforcement learning, drift-diffusion, Bayesian, biophysically realistic, dynamical systems, and deep neural networks) have directly linked behavioral parameters and the neural substrates that compute them, but the role of non-neuronal cells in these computations have been largely ignored. Thus, the integration of computational modeling approaches to investigate neuro-glia interactions could provide new perspectives on how they enable complex behavior and how they become altered in mental illnesses. Combining newly developed experimental methods for recording and controlling neuro-glia activity with rigorous computational approaches may inform mechanistic models of how neuro-glia interactions may compute or fail to compute cognitive and socio-affective functions relevant to neuropsychiatric disorders. Responsive Areas of Research Examples of research areas that may be included in applications submitted under this NOSI include, but are not limited to, the application of existing or novel: * Experimental methods to measure or control glial activity in behaving animals to elucidate the role of neuro-glia activity in cognitive or socio-affective behaviors. * Biologically inspired computational approaches to provide a mechanistic explanation of the consequences of neuro-glia interactions during cognitive or socio-affective behaviors. * Computational models able to map fine-grained cognitive or socio-affective behavioral parameters onto neuro-glia computations. * Neuro-glia computational principles or mechanistic knowledge derived from animal experiments in Basic Experimental Studies Involving Humans (BESH). Applications must include both: * A well-conceived scientific rationale, question, and/or hypothesis grounded in cognitive or socio-affective science, where neuro-glia activity may mechanistically explain a complex behavior. * Measurement or control neuro-glia activity during complex behavior. Applications proposing to exclusively investigate the effects of glial cells on behavior will be considered responsive to this NOSI, but applications including measurement and/or manipulation of both glia and neurons will be given higher priority. Applications Not Responsive to this NOSI * Applications that do not include all the items contained in the “applications must include” section above are not responsive to this NOSI and will not be reviewed. * Applications exclusively focused on in-vitro preparations - without an in-vivo behavioral component - are not responsive to this NOSI and will not be reviewed. * Applications proposing to apply animal models of mental disorders or use broad batteries of behavioral tests in animals to address constructs that are accessible only in humans by self-report, such as “depression” or “anxiety,” are non-responsive to this NOSI. For additional information on NIMH’s guidelines and priorities for animal neurobehavioral approaches applicants are strongly encouraged to review NOT-MH-19-053<https://grants.nih.gov/grants/guide/notice-files/NOT-MH-19-053.html>. Note: NIMH only accepts mechanistic studies that meet NIH's definition of a clinical trial through PA-20-183<https://grants.nih.gov/grants/guide/pa-files/PA-20-183.html> and PA-20-184<https://grants.nih.gov/grants/guide/pa-files/PA-20-184.html>. Applications directed to NIMH for intervention development must be submitted through PAR-21-130<https://grants.nih.gov/grants/guide/pa-files/PAR-21-130.html>. For further information on NIMH clinical trial policies, see NOT-MH-20-105<https://grants.nih.gov/grants/guide/notice-files/NOT-MH-20-105.html> and NOT-MH-19-006<https://grants.nih.gov/grants/guide/notice-files/NOT-MH-19-006.html>. Application and Submission Information This notice applies to due dates on or after June 5, 2022 and subsequent receipt dates through May 8, 2025. Submit applications for this initiative using one of the following funding opportunity announcements (FOAs) or any reissues of these announcement through the expiration date of this notice. * PA-20-184<https://grants.nih.gov/grants/guide/pa-files/PA-20-184.html> - Research Project Grant (Parent R01 Basic Experimental Studies with Humans Required) * PA-20-185<https://grants.nih.gov/grants/guide/pa-files/PA-20-185.html> - NIH Research Project Grant (Parent R01 Clinical Trial Not Allowed) * PA-20-196<https://grants.nih.gov/grants/guide/pa-files/pa-20-196.html> - NIH Exploratory/Developmental Research Grant Program (Parent R21 Basic Experimental Studies with Humans Required) * PA-21-219<https://grants.nih.gov/grants/guide/pa-files/pa-21-219.html> - Joint NINDS/NIMH Exploratory Neuroscience Research Grant (R21 Clinical Trial Optional) * PA-21-235<https://grants.nih.gov/grants/guide/pa-files/pa-21-235.html> - NIMH Exploratory/Developmental Research Grant (R21 Clinical Trial Not Allowed) * PAR-21-175<https://grants.nih.gov/grants/guide/pa-files/PAR-21-175.html> - Understanding and Modifying Temporal Dynamics of Coordinated Neural Activity (R01 Clinical Trial Optional * PAR-21-176<https://grants.nih.gov/grants/guide/pa-files/PAR-21-176.html> - Understanding and Modifying Temporal Dynamics of Coordinated Neural Activity (R21 Clinical Trial Optional) All instructions in the SF424 (R&R) Application Guide<https://grants.nih.gov/grants/how-to-apply-application-guide.html> and the funding opportunity announcement used for submission must be followed, with the following additions: * For funding consideration, applicants must include “NOT-MH-22-090” (without quotation marks) in the Agency Routing Identifier field (box 4B) of the SF424 R&R form. Applications without this information in box 4B will not be considered for this initiative. Applications nonresponsive to terms of this NOSI will not be considered for the NOSI initiative. Inquiries Please direct all inquiries to the contacts in Section VII of the listed funding opportunity announcements with the following additions/substitutions: Scientific/Research Contact(s) Michele Ferrante, Ph.D. National Institute of Mental Health (NIMH) Telephone: 301-435-6782 Email: ferrantem(a)nih.gov<mailto:michele.ferrante@nih.gov> Andrew Breeden, Ph.D. National Institute of Mental Health (NIMH) Telephone: 301-443-1576 Email: andrew.breeden(a)nih.gov<mailto:andrew.breeden@nih.gov>

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PostDoc position at UCL: endogenous brain fluctuations and decision making
by Tobias U. Hauser Feb. 8, 2022

Feb. 8, 2022

PostDoc position in real-time fMRI at Max Planck UCL Centre for Computational Psychiatry We are looking for a PostDoc to work on an exciting project investigating the role of endogenous brain fluctuations on decision making, funded by my ERC Starting Grant. In this project, we will investigate how endogenous, spontaneous fluctuations (aka resting-state fluctuations) influence behaviour. In our previous work (https://pnas.org/content/116/37/18732.short) we showed that moment-to-moment fluctuations affect risk taking behaviour. Here, we want to build on this and see how wide-reaching these effects are, and what the pharmacological bases of these effects are. We are looking for someone with expertise in advanced functional neuroimaging and preferably experience in real-time fMRI. We think that a postdoc with advanced understanding of time-series analyses and in-depth fMRI experience would be best suited for this position. Even though the general topic is set, own ideas and project ideas will very much appreciated & encouraged! The position is for 2 years (at the first instance) and will be based at the Max Planck UCL Centre for Computational Psychiatry and Ageing Research and the Wellcome Centre for Human Neuroimaging, located in Central London. If you have further questions, please contact Tobias Hauser (t.hauser(a)ucl.ac.uk) To read more about the research group, please see https://devcompsy.org/ Job advert here: https://atsv7.wcn.co.uk/search_engine/jobs.cgi?SID=amNvZGU9MTg4MjA4NSZ2dF90… -- Play smartphone games and help us understand the brain: www.brainexplorer.net Dr Tobias U. Hauser Sir Henry Dale Fellow, Principal Research Fellow Developmental Computational Psychiatry lab Max Planck UCL Centre for Computational Psychiatry & Ageing Research Wellcome Centre for Human Neuroimaging University College London 10-12 Russell Square London WC1B 5EH +44 207 679 5264 (internal: 45264) t.hauser(a)ucl.ac.uk www.tobiasuhauser.com www.devcompsy.org

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14 Open positions in neurotechnology R&D
by Eduardo López-Larraz Feb. 7, 2022

Feb. 7, 2022

Bitbrain is a neurotechnology company that combines neuroscience, artificial intelligence, and hardware to develop innovative products. We are currently looking for 14 highly talented and enthusiastic individuals to join our R&D team. The profiles vary across the entire R&D pipeline of neurotechnology applications in healthcare: neuro/biomedical engineering, electronic engineering, software engineering, computer science, data science, project management, clinical neuropsychology, neuroscience lab assistants. More information and application procedure: https://www.bitbrain.com/careers -- Eduardo López-Larraz, PhD Research Scientist R&D Department eduardo.lopez(a)bitbrain.com Twitter <https://twitter.com/Edu_Lo_L> Scholar <https://scholar.google.com/citations?user=wLM67tAAAAAJ> LinkedIn <https://www.linkedin.com/in/edulop> www.bitbrain.com +34 931 444 823 info(a)bitbrain.com Facebook <https://www.facebook.com/BitbrainEN> Twitter <https://twitter.com/Bitbrain_EN> LinkedIn <https://www.linkedin.com/company/2070341>

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Fully funded PhD position (4 years)
by Michael Biehl Feb. 7, 2022

Feb. 7, 2022

Apologies for multiple postings *Three weeks left to apply! * A *fully funded PhD position* (4 years) in the *Statistical **Physics of Neural Networks* is available at the University of Groningen, The Netherlands, see https://www.rug.nl/about-ug/work-with-us/job-opportunities/?details=00347-0… for details and application details. Applications (before March 1) are only possible through this webpage. The title of the project is "The role of the activation function for feedforward learning systems (RAFFLES)". For further information please contact Michael Biehl. ------------------------------------------------------------ Michael Biehl Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence P.O. Box 407, 9700 AK Groningen The Netherlands Tel. +31 50 363 3997 www.cs.rug.nl/~biehl m.biehl(a)rug.nl

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