December 2023 - Comp-neuro - lists.cnsorg.org

APPLY TO THE THOM YOUNG RESARCHER AWARD
by hocine Cherifi 05 Dec '23

05 Dec '23

*YOUNG RESARCHER AWARD* <https://iutdijon.u-bourgogne.fr/ccs-france/young-resarcher-award/> *@ **FRCCS 2024* <https://iutdijon.u-bourgogne.fr/ccs-france/> The Young Researcher Award is part of CSS FRANCE's <https://www.cssfrance.org/> global initiative to support early career researchers in their quest to advance the frontiers of science across a broad range of disciplines. It is in place to recognize early career contributions and leadership in research in Complex Systems related fields. It is awarded to young researchers up to *five years* after the Ph.D. completion (date of Ph.D. defense) and the deadline of the call for nomination. *Eligibility* - Applications for this award are welcomed by academia, business, and government scientists. - Eligible candidates should not be older than 35 years. - There should be no more than five years between the date of the Ph.D. defense and the deadline of the call for nomination. - Self-nominations are accepted and encouraged. - Any young researcher can be nominated in whatever discipline Complex Systems covers. - Candidates must commit to presenting their work at FRCCS if awarded. *Important dates* · Application deadline: *March 04, 2024* · Notification to applicants: *April 02, 2024* *Application Instruction* *For self-nomination* The application package must contain: - A CV of the candidate (maximum two pages) - Research achievement: a letter describing the context and relevance of the achievement, its scientific impact, and why it deserves an award (maximum two pages). - A long-term research project (maximum two pages) *For supporter nomination* - A CV of the candidate (maximum two pages) - Research achievement: a letter describing the context and relevance of the achievement, its scientific impact, and why it deserves an award (maximum two pages). - A statement from the candidate (maximum two pages) reflects their understanding of how the original scientific research moved science forward. *Application Process* Applications are made via a google form: Fill out the self-nomination form <https://docs.google.com/forms/d/e/1FAIpQLSc7JjwOEPF0YYBRHWomS3o-fZ3YXu0a5QU…> or the Supporter nomination form <https://forms.gle/bkVFg9pUvt2f4K1p9> *Note that Google Forms requires you to be signed in to a Google account to upload files and submit your responses. * *Selection Process* FRCCS Award Committee will evaluate all qualified candidates and propose the winner to the Advisory Board of CSS FRANCE, which will make the final decision. *Award Procedure* A prize of 1000 euros is awarded to the winner. The award also covers the registration fee of FRCCS <https://iutdijon.u-bourgogne.fr/ccs-france/> 2024 to facilitate attendance at the conference. The awardee will be recognized and present his (her) work at FRCCS <https://iutdijon.u-bourgogne.fr/ccs-france/> 2024. For more information contact: hocine.cherifi(a)gmail.com

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PhD position at Universitat de les Illes Balears
by Catalina Vich Llompart 05 Dec '23

05 Dec '23

The Dynamical Systems group at Universitat de les Illes Balears (UIB) is seeking applications for a PhD student. This Collaborative Research in Computational Neuroscience (CRCNS) funded position will focus on decision making, linking computational models and mathematical analysis to human and rodent data. The successful candidate will be mentored, supervised and trained directly by C.Vich, but they will also work and interact with Prof. Timothy Verstynen (CMU, Psychology) and Prof. Jonathan Rubin (University of Pittsburgh, Mathematics) so they will have to occasionally visit the USA team. The project specifically focuses on how dynamics of cortico-basal ganglia-thalamic pathways map to cognitive models of adaptive decision-making, and how these circuits regulate information flow in the mammalian brain. This position will be renewed annually for up to 3 years. Preferred skills from the PhD student: We are looking for highly motivated and self-driven candidate with strong interest in computational neuroscience and significant computational expertise. To be accepted in the PhD program, a MSc degree is required, preferably in mathematical biology, computational biology, dynamical systems or related disciplines. Students who do not hold a MSc degree but hold an undergraduate degree of 300ECTS are also encouraged to apply. A high level of written and spoken English and good programming skills (preferably python) are prioritized. Environment The Universitat de les Illes Balears (https://www.uib.eu <https://www.uib.eu/>) is located in Mallorca, the highest island of the Balearic Islands, at the foot of the Tramuntana mountains, an area declared a World Heritage Site by the UNESCO, as well as being just a few kilometres from Mallorca’s finest beaches. It is also ranked among the top 5 Spanish universities in education and in research quality. The university is well connected with the main city, Palma, and other villages. In fact, it has a bus and underground service with good timetables and frequencies. The accepted student will be a member of the group of Dynamical Systems (SSDD, http://www.ssdd.uib.es <http://www.ssdd.uib.es/>) and the institute of Applied Computing and Community Code (IAC3, http://iac3.uib.es <http://iac3.uib.es/>), with ample opportunities to interact with other young researchers in the fields of physics and mathematics in a highly interactive, dynamic, and multi-cultural environment. To apply For details on how to apply, please contact Catalina Vich by email at catalina.vich(a)uib.es <mailto:catalina.vich@uib.es> showing your interest on applying for this position. All qualified applicants will receive consideration for employment without regard to race, religion, sexual orientation, nationality, disability, etc. Demographic and cultural diversity will be considered in the selection of the trainee.

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Apply to the Bertalanffy Doctoral Student Award
by hocine Cherifi 05 Dec '23

05 Dec '23

*DOCTORAL STUDENT AWARD* <https://iutdijon.u-bourgogne.fr/ccs-france/doctoral-student-award/> *@ **FRCCS 2024* <https://iutdijon.u-bourgogne.fr/ccs-france/> The Bertalanffy Doctoral Student Award is part of CSS FRANCE's <https://www.cssfrance.org/> global initiative to support early career researchers in their quest to advance the frontiers of science across a broad range of disciplines. It is in place to recognize early career contributions and leadership in research in Complex Systems related fields. It is awarded to young researchers enrolled in a Ph.D. program. This competition consists in presenting your research in simple terms in a five minutes video to a lay audience. Your presentation should be clear, concise, and convincing. *Eligibility* - Eligible candidates must: - Be under 30. - Be enrolled in a Ph.D. program in whatever discipline Complex Systems covers. - Commit to presenting their work at the French Regional Conference on Complex Systems <https://iutdijon.u-bourgogne.fr/ccs-france/> FRCCS <https://iutdijon.u-bourgogne.fr/ccs-france/> if awarded - Accept and authorize the use and distribution of videos and photos related to the competition, including on social networks - Have informed their thesis supervisor of their participation in the competition - Present a sufficiently advanced state of the doctoral research project *Important dates* · Application deadline: *March 04, 2024* · Notification to applicants: *April 02, 2024* *Application Instruction* The application package must contain the following: - A CV of the candidate (maximum two pages) - A summary (maximum two pages) presenting the research work, its context, main contribution, and scientific impact. - A 5 minutes video presentation. *Application Process* Applications are made via a google form on the online portal. <https://docs.google.com/forms/d/e/1FAIpQLSe49vdpumpzjVJJ7_mWgC5zKSLS1AqelD_…> *Note that Google Forms requires you to be signed in to a Google account to upload files and submit your responses. * *Selection Process* FRCCS Award Committee will evaluate all qualified candidates and propose the winner to the Advisory Board of CSS FRANCE, which will make the final decision. *Award Procedure* A prize of 500 euros is awarded to the winner. The award also covers the registration fee of FRCCS <https://iutdijon.u-bourgogne.fr/ccs-france/> 2024 to facilitate attendance at the conference. The awardee will be recognized and present his (her) work at FRCCS <https://iutdijon.u-bourgogne.fr/ccs-france/> 2024. For more information contact: hocine.cherifi(a)gmail.com

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4 year fully-funded PhD studentship in ML and Parkinson’s disease at Nottingham
by Mark Humphries 04 Dec '23

04 Dec '23

A 4-year fully-funded PhD studentship with Prof Mark Humphries and Dr JeYoung Yung at the University Of Nottingham is available to start September 2024. “Optimising patient selection for Deep Brain Stimulation in Parkinson’s disease using multimodal machine learning” Parkinson’s disease has debilitating motor symptoms of tremor in the limbs, slowness of movement, and freezing, unable to move. A highly effective treatment is electrical stimulation deep in the motor regions of the midbrain. But surgery for this deep brain stimulation is only offered to around 2% of all patients, and about a quarter of those who receive it have poor outcomes. Optimising the selection of patients for deep brain stimulation will widen access to treatment, improve treatment outcomes, and prevent harm. The goal of this project is to test how fusing clinical data, neuroimaging, and video assessments could optimise the selection of patients. The project will be in collaboration with MachineMedicine (London), a MedTech company specialising in Parkinson’s disease, and the movement disorders clinical team at St George’s Hospital, London. In joining this collaboration, the PhD student will be trained in data-science and machine learning tools, including how to extract and analyse MRI and fMRI data, in fusing data across modalities, and in developing a machine-learning pipeline for predicting patient outcomes. These predictions will be tested against the 12-month follow-up data from the St George’s trial patients. The student’s further training will include a 3-month placement at MachineMedicine, and visits to St George’s clinic. Closing date:12 noon (GMT) Friday 12th January 2024. For full studentship details and how to apply see here: https://more.bham.ac.uk/mrc-aim/phd-opportunities/ Send all enquires to mark.humphries(a)nottingham.ac.uk<mailto:mark.humphries@nottingham.ac.uk> This message and any attachment are intended solely for the addressee and may contain confidential information. If you have received this message in error, please contact the sender and delete the email and attachment. Any views or opinions expressed by the author of this email do not necessarily reflect the views of the University of Nottingham. Email communications with the University of Nottingham may be monitored where permitted by law.

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Fully-funded PhD in mathematical neuroscience (Sheffield, UK)
by Hazem Toutounji 03 Dec '23

03 Dec '23

A fully-funded PhD in mathematical neuroscience is available with Dr Hazem Toutounji and Professor Eleni Vasilaki (University of Sheffield, UK). *Project title: *What can self-connected neurons do? A unifying theory of autaptic neural function in invertebrates and mammals The neuron is the fundamental building block in the nervous systems of all animal species, from C. elegans with its 302 neurons, to humans with their highly evolved cortex alone containing more than 10 billion neurons. The action potential, or spike, is the basic unit of neural signalling. Most neurons communicate through chemical synapses, which project from the axon of one neuron to the cell body or dendrites of another. An autapse is a synapse connecting the neuron to itself. In this project, we will use mathematical modelling to achieve two objectives: 1. Investigate the different roles of autapses in neural processing as observed experimentally. 2. Explore autapses as a potential route to chaos and its role in healthy and pathological neural function. The position is open to UK and overseas applicants. The ideal candidate will have a strong command of mathematical analysis and programming skills. Prior knowledge of dynamical systems theory is desirable. Application deadline: 5pm GMT 24 January 2024 For full details and how to apply see: https://www.findaphd.com/phds/project/what-can-self-connected-neurons-do-a-… *Please note: *Interested candidates are strongly encouraged to contact me ( h.toutounji(a)sheffield.ac.uk) to discuss your interest in and suitability for the project prior to submitting your application. -- *Dr. rer. nat. Hazem Toutounji (they / them)* *Lecturer in Computational Neuroscience and ECR Champion* *The Neural Dynamics and Computation Lab* Department of Psychology The Neuroscience Institute Insigneo Institute for in silico Medicine University of Sheffield ICOSS Building, 4th floor, desk 5 219 Portobello, Broomhall Sheffield S1 4DP, UK

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AREADNE 2024 Call for Abstracts
by John Pezaris 01 Dec '23

01 Dec '23

AREADNE 2024 Research in Encoding and Decoding of Neural Ensembles Eliopoulos Conference Center, Milos, Greece 25-29 June 2024 http://areadne.org info(a)areadne.org Dear Colleague, We are pleased to announce solicitation of abstracts for poster presentation at AREADNE 2024, 25-29 June 2024, our tenth meeting, to be held at the Eliopoulos Conference Center in Milos, Greece. Note that this is a change in location from previous meetings. Continuing with the same highly successful format, AREADNE 2024 will bring scientific leaders from around the world to present their theoretical and experimental work on the functioning of neuronal ensembles. The meeting will provide an informal and beautiful setting in which attendees can discuss their recent ideas and discoveries, with a relaxed pace that emphasizes interaction. Please see the Call for Abstracts for additional details, including links for templates, at http://areadne.org/call-for-abstracts Submissions of abstracts for poster presentations are due by 26 Jan 2024; notifications will be provided by late February 2024. We strongly encourage potential attendees to submit an abstract as presenters have registration priority. For information about the conference, please refer to the main web page http://areadne.org or send email to us at info(a)areadne.org . We hope to see you at AREADNE 2024! Best, - J. --- John S. Pezaris, Ph.D. AREADNE 2024 Co-Chair Harvard Medical School Massachusetts General Hospital 55 Fruit Street Boston, MA 02114, USA info(a)areadne.org

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MSc Internship - Evolving Reservoir Meta-RL - Inria, Bordeaux, France
by Xavier Hinaut 01 Dec '23

01 Dec '23

Please find below an offer for an intership from February/March to July/August 2024 in Mnemosyne team at Inria, Bordeaux, France # Title Title: Evolving Reservoirs for Meta Reinforcement Learning # Supervision Xavier Hinaut, Mnemosyne team, Inria Bordeaux Potential collaboration with Clément Moulin-Frier (Flowers Team) & Pierrick Legrand (Astral Team) # Duration: 6 months, spring/summer 2024. Level: Master 2 research internship # Keywords: meta reinforcement learning, reservoir computing, evolutionary computation, neuroevolution, simulation environments, scientific programming with Python. # Research team & internship location Mnémosyne Team: Inria Bordeaux Sud-Ouest, LABRI & Institut des Maladies Neurodégénératives (Centre Broca Aquitaine, Carreire campus) https://team.inria.fr/mnemosyne # Project How can a reinforcement learning (RL) agent autonomously learn a diversity of skills with minimal external supervision? Recent works have shown that a single goal-conditioned policy can be trained to solve multiple tasks in parallel (see e.g. Colas et al., 2021 for a recent review). Other works in Meta Reinforcement Learning (Meta-RL) have shown that artificial agents can “learn how to learn” (hence the term meta-learning) multiple skills without having access to goal-related information (see e.g. Weng, 2019 for an introduction). Meta-RL aims at equipping agents with the ability to generalize to tasks or environments that have not been encountered during training. Two nested processes of adaptation are traditionally considered: an outer adaptation loop optimizing the hyperparameters of an inner adaptation loop. The inner adaptation loop can be a standard RL algorithm operating on a given environment. The outer loop is tuning the hyperparameters of the inner loop such that it performs well on a wide distribution of environments. The end result of this nested training process is an algorithm that learns how to learn, i.e. learns how to adapt to tasks that have never been encountered during training. Works differ in the optimization technique and adaptation mechanism they consider in the two loops. The outer loop often optimizes higher-level structures such as the architecture of a neural network (Baker et al., 2017), the morphology (Gupta et al., 2021), a curiosity module providing intrinsic rewards (Alet et al., 2020) or plasticity rules (Najarro & Risi, 2021), employing either evolutionary or gradient-based optimization. In parallel, Reservoir Computing (RC, (Jaeger 2001+)) is particularly well suited for extracting information from data stream with complex temporal dynamics (e.g. weather forecast or language). It is a machine learning paradigm on sequential data where a recurrent neural network is only partially trained (e.g. only training a linear readout). One of the major interests of these recurrent neural networks is their reduced computational cost and the possibility to learn both in on-line and off-line fashion. They have been successfully applied on a wide variety of tasks, from prediction/generation of chaotic timeseries to discrimination of audio sequences, such as bird song recognition. They offer de facto a fast, simple yet efficient way to train RNNs. This "reservoir of computations" works thanks to random projections in large dimensions, and is thus similar to temporal Support Vector Machines (SVM). In this internship, we will explore how Reservoir Computing can be leveraged in the context of Meta-RL. In this context, we will consider evolving the architecture of a reservoir for performing well on a wide range of RL tasks (see Seoane, 2019 for theoretical arguments on this evolutionary perspective). For this aim, we will use the ReservoirPy Python library developed in the Mnemosyne team (Trouvain & Hinaut, 2022) that we will interface with evolutionary computation algorithms (e.g. Tang et al., 2022). The project will involve: ● Designing of a wide range of RL tasks in an existing simulation environment (ex MuJoCo) ● Learning how to learn multiple tasks without access to the goal information (i.e. meta-RL with a reservoir). ● Evolve reservoir architectures for meta-RL with evolutionary methods # How to apply Contact xavier.hinaut(a)inria.fr with a CV and letter of motivation. We also recommend sending documents or reports describing previous projects you have been working on (even if they are not directly related to the topic), as well as your grades and links to some of your code repositories. # Requirements We are looking for highly motivated MSc students (Master II). Programming skills and prior experience with Python and deep learning frameworks (Pytorch, Tensorflow) are expected. # Other internship offers https://github.com/neuronalX/internships/ # References Alet, F., Schneider, M. F., Lozano-Perez, T., & Kaelbling, L. P. (2020). Meta-learning curiosity algorithms. International Conference on Learning Representations (ICLR 2020). https://openreview.net/forum?id=BygdyxHFDS Baker, B., Gupta, O., Naik, N., & Raskar, R. (2017). Designing neural network architectures using reinforcement learning. ArXiv:1611.02167 [Cs]. http://arxiv.org/abs/1611.02167 Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019). Reinforcement Learning, Fast and Slow. Trends in Cognitive Sciences, 23(5), 408–422. https://doi.org/10.1016/j.tics.2019.02.006 Colas, C., Karch, T., Sigaud, O., & Oudeyer, P.-Y. (2021). Intrinsically Motivated Goal-Conditioned Reinforcement Learning: A Short Survey. ArXiv:2012.09830 [Cs]. http://arxiv.org/abs/2012.09830 Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature Communications, 12(1), 5721. https://doi.org/10.1038/s41467-021-25874-z Jaeger, H. (2007). Echo state network. Scholarpedia, 2(9), 2330. https://doi.org/10.4249/scholarpedia.2330 Najarro, E., & Risi, S. (2021). Meta-Learning through Hebbian Plasticity in Random Networks. ArXiv:2007.02686 [Cs]. http://arxiv.org/abs/2007.02686 Sakemi, Y., Morino, K., Leleu, T., & Aihara, K. (2020). Model-size reduction for reservoir computing by concatenating internal states through time. Scientific Reports, 10(1), Article 1. https://doi.org/10.1038/s41598-020-78725-0 Seoane, L. F. (2019). Evolutionary aspects of reservoir computing. Philosophical Transactions of the Royal Society B: Biological Sciences, 374(1774), 20180377. https://doi.org/10.1098/rstb.2018.0377 Tang, Y., Tian, Y., & Ha, D. (2022). EvoJAX: Hardware-Accelerated Neuroevolution. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 308–311. https://doi.org/10.1145/3520304.3528770 Trouvain, N., & Hinaut, X. (2022). reservoirpy: A Simple and Flexible Reservoir Computing Tool in Python. https://hal.inria.fr/hal-03699931 https://github.com/reservoirpy/reservoirpy Weng, L. (2019, June 23). Meta Reinforcement Learning. https://lilianweng.github.io/posts/2019-06-23-meta-rl/ Xavier Hinaut Inria Research Scientist www.xavierhinaut.com -- +33 5 33 51 48 01 Mnemosyne team, Inria, Bordeaux, France -- https://team.inria.fr/mnemosyne & LaBRI, Bordeaux University -- https://www4.labri.fr/en/formal-methods-and-models & IMN (Neurodegeneratives Diseases Institute) -- http://www.imn-bordeaux.org/en --- Our Reservoir Computing library: https://github.com/reservoirpy/reservoirpy

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Postdoc Position in Computational/Theoretical Neuroscience (Western University)
by Lyle Muller 01 Dec '23

01 Dec '23

The lab of Lyle Muller (http://mullerlab.ca) at Western University (London, Ontario) has a postdoctoral position available in computational and theoretical neuroscience. This position will involve collaboration between our laboratory and researchers with expertise in advanced methods of brain imaging (Mark Schnitzer, Stanford), neuroengineering (Duygu Kuzum, UCSD), theoretical neuroscience (Todd Coleman, Stanford), and neurophysiology of visual perception (John Reynolds, Salk Institute for Biological Studies). In collaboration with this multi-disciplinary team, this researcher will apply new signal processing techniques for multisite spatiotemporal data to understand cortical dynamics during visual perception. This project will also involve development of spiking network models to understand the mechanisms underlying observed activity patterns. The project may include intermittent travel between labs to present results and facilitate collaborative work. This research will involve in-depth analyses of neural recordings and computational modeling. Prior experience with scientific computing languages (MATLAB, Julia, or Python) and signal processing is preferred. Western University offers an excellent research environment and support. Candidates can write to lmuller2(a)uwo.ca<mailto:lmuller2@uwo.ca> with a CV. Selected applicants will be interviewed on a rolling basis starting immediately. Both the involved labs and the University are deeply committed to fostering diversity and scientific outreach. Applications from underrepresented groups are strongly encouraged, and any interested applicants are welcomed to write the PI directly with questions about the research environment. -- Lyle Muller http://mullerlab.ca

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