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

4 year fully-funded PhD studentship in ML and Parkinson’s disease at Nottingham

by Mark Humphries

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.

Dec. 4, 2023

Announcing Okinawa/OIST Computational Neuroscience Course 2024 (OCNC 2024)

by Erik De Schutter

OKINAWA/OIST COMPUTATIONAL NEUROSCIENCE COURSE 2024 Methods, Neurons, Networks and Behaviors June 17 to July 4, 2024 Okinawa Institute of Science and Technology Graduate University, Japan https://groups.oist.jp/ocnc The aim of the Okinawa/OIST Computational Neuroscience Course is to provide opportunities for young researchers with theoretical backgrounds to learn the latest advances in neuroscience, and for those with experimental backgrounds to have hands-on experience in computational modeling. We invite graduate students and postgraduate researchers to participate in the course, held from June 17th through July 4th, 2024 at an oceanfront seminar house of the Okinawa Institute of Science and Technology Graduate University. Applications are through the course web page (https://groups.oist.jp/ocnc) only; January 1 - January 31, 2024. Applicants will receive confirmation of acceptance end of March. Like in preceding years, the 19th OCNC will be a comprehensive three-week course covering single neurons, networks, and behaviors with ample time for student projects. The first week will focus exclusively on methods with hands-on tutorials during the afternoons, while the second and third weeks will have lectures by international and local experts. The course has a strong hands-on component based on student proposed modeling or data analysis projects, which are further refined with the help of a dedicated tutor. Applicants are required to propose their project at the time of application. There is no tuition fee. The sponsor will provide lodging and meals during the course. We hope that this course will be a good opportunity for theoretical and experimental neuroscientists to meet each other and to explore the attractive nature and culture of Okinawa, the southernmost island prefecture of Japan. Lecturers: Erik De Schutter (OIST) Kenji Doya (OIST) Tomoki Fukai (OIST) Izumi Fukunaga (OIST) N. Alex Cayco Gajic (École Normale Supérieure, Paris, France) Yukiko Goda (OIST) Arvind Kumar (KTH, Stockholm, Sweden) Bernd Kuhn (OIST) Matthew Larkum (Humboldt University, Berlin, Germany) Hakwan Lau (RIKEN CBS, Tokyo, Japan) Sukbin Lim (NYU Shangai, China) Gerald Pao (OIST) Thomas Parr (Oxford University, UK) Sam Reiter (OIST) Kazumasa Tanaka (OIST)

Dec. 4, 2023

Fully-funded PhD in mathematical neuroscience (Sheffield, UK)

by Hazem Toutounji

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

Dec. 3, 2023

AREADNE 2024 Call for Abstracts

by John Pezaris

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

Dec. 1, 2023

MSc Internship - Evolving Reservoir Meta-RL - Inria, Bordeaux, France

by Xavier Hinaut

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

Dec. 1, 2023

Postdoc Position in Computational/Theoretical Neuroscience (Western University)

by Lyle Muller

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

Dec. 1, 2023