- Comp-neuro - lists.cnsorg.org

Research Assistant (Research Associate)

by Artur Luczak

Research Assistant (Research Associate) at the University of Lethbridge. Our lab seeks a highly motivated individual with a strong computational background to work at the interface of neuroscience and machine learning. We are especially interested in the application of deep neural networks to improve our understanding of neuronal data, and the application of recent neuroscience findings to improve the performance of deep neural networks. In our lab we also record the activity of hundreds of neurons in normal and epileptic animals, and the successful candidate is welcome to participate in those projects. Preferred candidate should have strong background in modeling neurons and networks, and should be very familiar with models of synaptic plasticity (e.g. BCM). Our lab at the Canadian Centre for Behavioural Neuroscience in the University of Lethbridge is located in the sunniest area of Canada and next to scenic Rocky Mountains. All qualified candidates are encouraged to apply; however, Canadians and permanent residents will be given priority. For more information please visit our website at: http://lethbridgebraindynamics.com/artur_luczak. Duties and responsibilities: - Conduct research and literature reviews, in related topics for use in scholarly publications. - Compile research results and assist professors in the analysis of results and the preparation of journal articles or papers. - Analyze data using statistics, programming techniques and artificial neural networks. - Develop analytical methods in biologically plausible artificial neural networks. - Participate in scientific discussion groups and seminars. All duties and responsibilities shall be carried out under the supervision of Prof. Artur Luczak. Skills & Qualifications: - Master degree’s in neuroscience, physics, computer science or related topics. - 3 years of related research work experience. - Having strong background in modeling neurons and networks, and should be very familiar with models of synaptic plasticity. - Familiarity with epilepsy is an asset - Highly experienced in programming with Python and Matlab. - Ability to work with data, applying machine learning methods and deep learning. - Working with computer clusters and cloud computing. - Excellent communication and team work skills. Working Conditions: Work in an office with a computer provided. Hours of Work: Full-time, 30-32 hours per week. Salary: $21.63 /hour Duration: one year with the opportunity for extension Language requirements: English Benefits: Standard benefits including Extended Health, Dental, EFAP, Life Insurance and Long Term Disability. Interested applicants are encouraged to contact Professor Artur Luczak (Luczak(a)uleth.ca) with a copy of their CV and a cover letter. Address: University of Lethbridge. 4401 University Drive West, Lethbridge, Alberta T1M 1R6, Canada

May 10, 2020

Call for papers MLCN at MICCAI 2020

by MLCN Workshop

*Please find below the call for papers for the International Workshop of Machine Learning in Clinical Neuroimaging (MLCN) on 4 October 2020 at MICCAI 2020 in Lima, Peru. We welcome contributions on novel machine learning methods and their applications to clinical neuroimaging data.* The submission deadline is *30 June 2020*, and all MLCN accepted papers will be eligible for the best paper award of 500 USD. For more information, please visit https://mlcnws.com/. Best wishes, The MLCN 2020 committee Christos Davatzikos Andre Marquand Jonas Richiardi Emma Robinson Ahmed Abdulkadir Cher Bass Mohamad Habes Seyed Mostafa Kia Jane Maryam Rondina Chantal Tax Hongzhi Wang Thomas Wolfers International Workshop on Machine Learning in Clinical Neuroimaging 4 October 2020 in Lima, Peru The International Workshop of Machine Learning in Clinical Neuroimaging ( https://mlcnws.com/) a satellite event of MICCAI (https://miccai2020.org) calls for original papers in the field of clinical neuroimaging data analysis with machine learning. The two tracks of the workshop include methodological innovations as well as clinical applications. This highly interdisciplinary topic provides an excellent platform to connect researchers of varying disciplines and to collectively advance the field in multiple directions. For the machine learning track, we seek contributions with substantial methodological novelty in analyzing high-dimensional, longitudinal, and heterogeneous neuroimaging data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to: - Spatio-temporal brain data analysis - Structural data analysis - Graph theory and complex network analysis - Longitudinal data analysis - Model stability and interpretability - Model scalability in large neuroimaging datasets - Multi-source data integration and multi-view learning - Multi-site data analysis, from preprocessing to modeling - Domain adaptation, data harmonization, and transfer learning in neuroimaging - Unsupervised methods for stratifying brain disorders - Deep learning in clinical neuroimaging - Model uncertainty in clinical predictions - ... In the clinical neuroimaging track, we seek contributions that explore how the application of advanced machine learning methods help us to move towards precision medicine for complex brain disorders. Topics of interest include but are not limited to: - Biomarker discovery - Refinement of nosology and diagnostics - Biological validation of clinical syndromes - Treatment outcome prediction - Course prediction - Analysis of wearable sensors - Neurogenetics and brain imaging genetics - Mechanistic modeling - Brain aging - ... Submission Process: The workshop seeks high quality, original, and unpublished work that addresses one or more challenges described above. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style (see https://www.miccai2020.org/en/PAPER-SUBMISSION-GUIDELINE.html#manuscript-fo… for detailed author guidelines) using the CMT system at https://cmt3.research.microsoft.com/MLCN2020. The page limit is 8-pages (text, figures, and tables) plus up to 2-pages of references. We review the submissions in a double-blind process. Please make sure that your submission is anonymous. Accepted papers will be published in a joint proceeding with the MICCAI 2020 conference. Best Paper Award: This year, all MLCN accepted papers will be eligible for the best paper award. The recipient of the award will be chosen by the MLCN scientific committee based on the scientific quality and novelty of contributions. The winner will be announced at the end of the workshop and will receive 500 USD honorarium. Important Dates: - Paper submission deadline: June 30th, 2020 - Notification of Acceptance: July 24th, 2020 - Camera-ready Submission: July 31st, 2020 - Workshop Date: 4 October 2020

May 8, 2020

PhD position at EPFL (Lausanne, Switzerland)

by Milekovic Tomislav

PhD position in the lab of Prof. Gregoire Courtine at EPFL (Lausanne, Switzerland) A computational approach to personalize and optimize clinical treatments for restoring locomotion to people paralyzed by spinal cord injury Location: The laboratory of Prof. Gregoire Courtine at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland, is looking to fill a fully funded PhD position. The qualified candidate will benefit from joining a very dynamic and multidisciplinary group working at the interface of computational neuroscience, neuroengineering, prosthetics and biology. EPFL provides state-of-the-art facilities and is one of the leading technical universities worldwide. In addition, PhD salaries at EPFL rank the highest in the world. Description: A therapy based on epidural electrical stimulation (EES) of the spinal cord can restore the ability to walk to people paralyzed by spinal cord injury. EES does this by recruiting sensory axons within dorsal spinal roots that enter the spinal cord between the vertebrae. Yet, clinically available electrode arrays used to deliver the EES were not designed to target individual spinal roots. Data driven design of the electrode arrays has the potential to substantially improve the specificity of spinal EES and, therefore, dramatically improve the recovery of people with spinal cord injury. The efficacy of EES could be further enhanced through computational algorithms capable of designing EES protocols that fully utilize the interaction between the electrode array and patient's anatomy. These two developments are critical for deployment of the EES-based therapy to clinics around the world to help millions of people suffering from spinal cord injury. We have created a computational pipeline capable of creating detailed computational models of individual persons' spinal columns from MRI scans and fMRI recordings. These hybrid models are composed of 3D finite element models (FEM) to characterize the electric current and potential in the spinal cord of individuals, and various abstractions of compartmental cable models and network models of spinal cord neuronal populations and their connections to calculate the effects of EES on the spinal networks and, in turn, the activation of muscles. This computational approach has the potential to optimize the efficacy of EES on a personalized basis, lead to novel superior electrode array designs, and further our understanding of the mechanisms by which spinal cord controls movement. The successful candidate will work to automatize our computational pipeline in order to make the described approaches useful in applied clinical practice. They will work on the development of efficient and robust computer vision algorithms to automatically segment medical imaging datasets. They will also further develop our computational pipeline to enable automatic definition of personalized EES stimulation protocols. Furthermore, they will implement a computational framework around our pipeline that can perform a large-scale and diverse sensitivity and uncertainty analysis. This framework will be critical to enhance the efficacy and explore possible novel applications of spinal cord EES. Prerequisites: - Master's Degree in Physics, Computer Science, Mathematics, Microengineering, Electrical Engineering or related - Proficiency in Python, Matlab and C++ - Experience with Finite Element Models, compartmental cable models, neurobiomechanical models and Machine Learning Contact: Applications including a CV and a cover letter should be sent to andreas.rowald(a)epfl.ch

May 8, 2020

Fully-funded PhD Positions in Biomedical Imaging Computing, Lehigh University, USA

by Yu Zhang

*Fully-funded PhD Positions in Biomedical Imaging Computing, Lehigh University, USA* The Biomedical Imaging and Computation Laboratory (BIC Lab) at Lehigh University (Bethlehem, USA) is currently accepting applications for PhD positions in biomedical imaging computing. *Position description: *The research project will focus on developing and applying a variety of machine learning/deep learning methods for biomedical imaging analysis and prediction. The successful applicants will contribute to research and development in artificial intelligence (AI)-driven neural biomarker discovery using advanced neuroimaging techniques (e.g., MRI and EEG) for precision medicine of brain diseases. *Qualifications: *Successful applicants will have a Bachelor’s or Master’s degree in Electrical Engineering, Biomedical Engineering, Computer Science, or other related fields, and strong motivation to do high-quality research in novel biomedical imaging computing with machine learning. Additionally, strong quantitative skills and a programming background (Matlab and/or Python) are important. Previous research experiences in related fields and scientific publications in journals/conferences are preferred. *Lehigh University: *Lehigh University is a premier research university in the United States, consistently ranked in the top tier of national research universities each year. It is a coeducational, nondenominational, private university that offers a distinctive academic environment for undergraduate and graduate students from across the globe. Located in Pennsylvania’s scenic Lehigh Valley, the campus is situated on 1,600 acres in close proximity to both New York City and Philadelphia. The application materials will be reviewed immediately once received. The expected starting time is Fall 2020 or Spring 2021. Interested candidates should send a cover letter describing your research interests, CV (including undergraduate GPA and ranking, transcripts if applicable), and contact information of three references to Dr. Yu Zhang at yzhangsu(a)stanford.edu and zhangyubrain(a)gmail.com. Check here for detailed admission requirements (e.g., GRE and TOEFL scores) https://engineering.lehigh.edu/bioe/graduate/bioengineering-admissions-guid… -- Yu Zhang, PhD Assistant Professor Department of Bioengineering, Lehigh University Research Fellow Wu Tsai Neurosciences Institute, Stanford University Webpage: http://yuzhangresearch.weebly.com/ Email: yzhangsu(a)stanford.edu

May 7, 2020

Neuromatch 2.0 – an online unconference in neuroscience, May 25-27, 2020

by Grace Lindsay

Announcing Neuromatch 2.0 – an online unconference in neuroscience, May 25-27, 2020. After a successful two-day conference in March, Neuromatch is back for three days this May! This fully online computational neuroscience conference will include invited and submitted talks, a virtual poster session, panels, debates, group discussion, and “mind-matching” events. In partnership with the IEEE Brain Initiative, one day of the conference will focus on neural data analysis. In partnership with eLife, Neuromatch will also feature talks and discussion with early career researchers. Registration and talk/poster submissions are due May 20th. For instructions on how to submit, please see: https://neuromatch.io/instructions/ For more information and to register for the conference, visit our site: https://neuromatch.io/ If you want to participate in the “mind-matching” or group discussion events (where you’ll be algorithmically paired with other scientists based on mutual research interests), don’t forget to include example abstracts so we know your interests! Confirmed Speakers: - Eve Marder (Brandeis University) - Jenny Bizley (UCL) - Richard Naud (U of Ottawa) - Yael Niv (Princeton) - Jakob Macke (TU of Munich) - Asya Rolls (Technion) - Joseph Raimondo (U of Cape Town) - Dan Yamins (Stanford) - Demba Ba (Harvard) - Ilana Witten (Princeton) - Eugenia Chiappe (Champalimaud) - Byron Yu (CMU) - Adrienne Fairhall (U of Washington) - Polina Anikeeva (MIT) Sincerely, Neuromatch 2.0 organizers Dan Goodman (Imperial) Konrad Kording (UPenn) Brad Wyble (Penn State) Titipat Achakulvisut (UPenn) Tim Vogels (Oxford) Chris Rozell (Georgia Tech) Yiota Poirazi (IMBB/FORTH) Grace Lindsay (UCL)

May 7, 2020

Two Post-doc opportunities in machine learning for neurological conditions

by Andre Altmann

Dear Comp-Neuro Community, If you like the challenge of developing novel computational and mathematical solutions to real-world medical challenges, then you will love working with us in the UCL Centre for Medical Image Computing (CMIC) [1] in London, UK. We are looking for two excellent postdoctoral researchers to join the UCL POND[2] team's well-established, supportive, expanding team of computational scientists in developing state-of-the-art data-driven solutions for neurological conditions like Alzheimer’s disease. In this field we lead international initiatives such as EuroPOND[3], TADPOLE Challenge[4], and the new E-DADS consortium[5]. Importantly, we ensure impact by collaborating closely with clinical scientists on our doorstep in London and our network of collaborators across the globe. *Deadline for applications is 25th May*. For details on the roles and to apply: http://pond.cs.ucl.ac.uk/vacancies Best wishes, Andre Altmann Links: [1] https://www.ucl.ac.uk/medical-image-computing/ [2] http://pond.cs.ucl.ac.uk/ [3] http://europond.eu/ [4] https://tadpole.grand-challenge.org/ [5] https://e-dads.github.io/

May 7, 2020

Postdoctoral Associate in Computational Neuroscience of Learning and Memory

by Yuri Dabaghian

*Postdoctoral Associate in Computational Neuroscience of Learning and Memory* A NSF-funded postdoctoral position is immediately available in the group of Dr. Yuri Dabaghian at the University of Texas in Houston. The goal of the projects is to understand the hippocampal mechanisms of learning and memory using computational topological and geometric approaches, neuronal network theory. The projects involve both computational modeling and analyses of in-vivo electrophysiological data provided by experimental collaborators. The ideal candidate should have a strong in background Computational Neuroscience, Applied Mathematics, Theoretical Physics or similar. To apply, please send a CV, letter of motivation and the names of two-three references to Yuri Dabaghian (Yuri.A.Dabaghian(a)uth.tmc.edu) Applications will be accepted until the position is filled.

May 4, 2020

Fully funded 4-year PhD Position at IIT - Computational approaches to study the neural code

by Stefano Panzeri

Fully funded 4-year PhD Position at IIT - Computational approaches to study the neural code We are seeking candidates for a fully funded 4-year PhD position at the Italian Institute of Technology’s in the lab directed by Stefano Panzeri ( https://www.iit.it/people/stefano-panzeri) We are particularly interested in candidates that wish to work in a project in collaboration with the experimental neurophysiology laboratory of Giuliano Iurilli (https://www.iit.it/people/giuliano-iurilli) We are interested in using mathematical models of neural networks and information theoretic and machine learning analytical tools to understand the principles of neural coding by using as a model the olfactory system. For recent representative publications of Panzeri and Iurilli on relevant topics please see: Chong et al, in press, preprint available at https://www.biorxiv.org/content/10.1101/841916v1 van Vugt B, et al, (2018) Science: DOI: 10.1126/science.aar7186 Runyan C. A., et al (2017) Nature: 548: 92-96 Panzeri, S, et al (2017) Neuron 93: 491-507 Iurilli, G. and Datta, R.S. (2017) Neuron 93 , 1180-1197 Safaai, H. et al (2015) Proc. Natl. Acad. Sci. USA 112(41): 12834–12839 The ideal candidate should have a strong background in numerate sciences, and have a strong propensity for interdisciplinary research. No extensive previous experience in neuroscience is needed. However, a keen interest in understanding the brain is essential. The deadline for the formal application is set to May 27th 2019, but interested candidates are invited me to contact us informally by email ( stefano.panzeri(a)iit.it and Giuliano.urilli(a)iit.it ) as soon as possible, and *no later than May 11th 2020,* by attaching their CV and briefly explaining their interest in this position, to initiate discussion about potential PhD projects of interest. Yours truly, Stefano Panzeri

May 4, 2020

PhD position in theoretical and experimental neuroscience

by Nicolas P. Rougier

PhD position in theoretical and experimental neuroscience Application deadline: 30/05/2020 What: PhD position in theoretical and experimental neuroscience Where: Bordeaux, France When: September 2020 (3 years duration) Who: Nicolas Rougier (Inria) & Arthur Leblois (CNRS) A PhD position is available at the Inria Bordeaux Sud-Ouest center and the Institute of Neurodegenerative Disease in Bordeaux, France. Candidate profile ================= The future PhD candidate must have a strong experience at both the theoretical and experimental level. In particular, the following qualities and skills will be highly valued, if not required: * Solid bases in maths and/or physics; * Previous experience in experimental neuroscience; * Programming experience (Python or similar coding language); * Experience in machine learning or data mining is a plus; * Independence and ability to manage a project; * Good English reading/speaking skills. How to apply ============ Please send your application (CV, letter of motivation, and the names and email addresses of one or two persons who can provide recommendation letters) to Dr. Nicolas Rougier (nicolas.rougier(a)inria.fr) and Arthur Leblois (arthur.leblois(a)univ-bordeaux.fr) before the 30th of May 2020. Proposed research ================= Temporally precise movement patterns underlie many motor skills and innate actions, yet the origin of temporal control in motor behaviors remains unclear. While cortical motor regions traditionally have been viewed as encoding features of motor gestures, these models do not address how sequences of motor gestures are produced or appropriately timed. Recently, a number of studies have suggested that, beyond just representing features of motor output, motor regions may have intrinsic oscillatory dynamics or sequential dynamics to act as their own pattern generators. However, in the order of milliseconds, the neural and anatomical correlates of time processing are still largely unknown because the dominant model, which is based on internal clock fails at giving account on experimental data. Understanding the neural dynamics underlying the acquisition and performance of complex sensorimotor tasks has been hindered by the complexity of the underlying brain networks in common animal models used in neuroscience (e.g. rodents or primates). In particular, the complex interactions between cortical areas (primary and secondary motor cortex in rodents, motor, premotor and supplementary motor areas in primates) related to many different sensorimotor associations and motor skills is daunting. Conversely, in birds, the sensorimotor skill of song production and its learning has a dedicated set of interconnected brain nuclei known as the “song system”, making them an outstanding model to study the neural mechanisms of vocal learning and more generally, of sensorimotor learning. Songbirds indeed rely on learned vocalizations to communicate during courtship or aggressive behaviours. Just as speech learning in humans, vocal learning in young birds requires the coordination of vocal muscles to reproduce previously experienced adult vocalizations. Song learning is also limited to a critical period during development and heavily relies on auditory feedback. The proposed research lies at the interface of neurophysiology, cognitive science, applied mathematics, and theoretical physics. The concepts and methods that will be used will draw on single neuron physiology, electrophysiological studies in behaving animals, statistical mechanics, dynamical system theory, and stochastic differential equations. To this end, we will combine a theoretical approach (development of a mathematical model of the timing system) and an experimental one (chronic neural recording in awake birds, behavioural manipulations). Therefore, a collaboration with a team of physiologists at the Institute of Neurodegenerative Diseases, and in particular with Arthur Leblois who has a long-established expertise in the neural mechanisms underlying vocal learning in songbirds. The model will be designed under the supervision of Nicolas Rougier who has extensive experience in computational modelling. Predictions of the model will then be tested experimentally by measuring neuronal activity in the brain areas controlling song timing in songbirds under the supervision of Arthur Leblois. Chronic electrophysiological recordings in singing birds are routinely performed in the lab of Arthur Leblois, and will be implemented in a protocol allowing the induction of plasticity in the duration of song syllables in young adult birds over short periods of time.

May 4, 2020

NEST Conference 2020 goes virtual — Call for Contributions

by Hans Ekkehard Plesser

Dear all, The NEST Initiative is excited to invite everyone interested in Neural Simulation Technology and the NEST Simulator to the NEST Conference 2020. 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. This year's conference will take place as a virtual conference on Monday/Tuesday 29/30 June 2020. We are now inviting contributions to the conference, including plenary talks, "posters" and breakout sessions on specific topics. Important dates 01 June 2020 — Deadline for submission of contributions 08 June 2020 — Notification of acceptance 10 June 2020 — Deadline for NEST Initiative Membership applications (registration is free for members in 2020) 22 June 2020 — Registration deadline 29 June 2020 — NEST Conference 2020 starts For more information on how to submit your contribution, register and participate, please visit the conference website https://nest-simulator.org/conference We are looking forward to seeing you all in June! Hans Ekkehard Plesser, Susanne Kunkel, Dennis Terhorst, Anne Elfgen & many more När du skickar e-post till Karolinska Institutet (KI) innebär detta att KI kommer att behandla dina personuppgifter. Här finns information om hur KI behandlar personuppgifter<https://ki.se/medarbetare/integritetsskyddspolicy>. Sending email to Karolinska Institutet (KI) will result in KI processing your personal data. You can read more about KI’s processing of personal data here<https://ki.se/en/staff/data-protection-policy>. _______________________________________________ Neuroinfo mailing list Neuroinfo(a)incf.org<mailto:Neuroinfo@incf.org> https://lists.incf.org/cgi-bin/mailman/listinfo/neuroinfo

May 4, 2020