- Comp-neuro - lists.cnsorg.org

Announcement: pymdp 1.0.0 (JAX backend for active inference modeling)
by Conor Heins March 23, 2026

March 23, 2026

Dear colleagues, I would like to announce the release of pymdp 1.0.0, an open-source Python package for building and simulating active inference agents with discrete POMDP generative models. Repository: https://github.com/infer-actively/pymdp Documentation: https://pymdp-rtd.readthedocs.io/en/latest/ Examples: https://pymdp-rtd.readthedocs.io/en/latest/tutorials/notebooks/ Release notes: https://github.com/infer-actively/pymdp/releases/tag/v1.0.0 pymdp was originally developed as a NumPy-based library implementing core active inference routines for perception, planning, learning, and action selection. Version 1.0.0 is a substantial update that rebuilds the library around a JAX backend. Main changes in 1.0.0 include: - GPU/TPU-ready simulation of agents and environments - autodifferentiable inference, planning, learning, and action selection - JIT-compiled agent-environment loops for substantially faster execution - straightforward batching over agents and environments via vmap() - easier integration with JAX-native probabilistic programming tools such as NumPyro <https://github.com/pyro-ppl/numpyro> and pybefit <https://github.com/dimarkov/pybefit> In addition to the backend rewrite, the release includes several algorithmic and modeling improvements: - tree-search planning with sophisticated inference <https://arxiv.org/abs/2006.04120>, with compatibility with Monte Carlo Tree Search through DeepMind's mctx package - inductive inference, which augments planning with backward goal-reachability constraints and is particularly useful in long-horizon, deterministic or near-deterministic settings - exact HMM filtering and smoothing with associative scan - optimized differentiable implementations of marginal message passing and variational message passing - support for sparse dependency structure in large graphical models - more flexible many-to-many action dependencies between control factors and hidden-state factors One motivation for the JAX transition was to make active inference models easier to integrate into modern differentiable and probabilistic workflows. We expect this to be especially useful for researchers working in computational neuroscience, cognitive modeling, and computational psychiatry, where fitting decision-making models to behavior is a common goal. Feedback, bug reports, and contributions are very welcome. Best wishes, Conor Heins

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Consciousness Commons (ASSC satellite): build a study in 2 days + $25,000 seed funding
by Lucia Melloni March 23, 2026

March 23, 2026

Dear all, What if we created a space to move from ideas to concrete research on consciousness, together? Right after ASSC 2026, we are organizing Consciousness Commons: Building Collaborative Science with Open Consciousness Data, a two-day satellite workshop in Santiago, Chile (July 4–5, 2026). Consciousness Commons is designed for early-career researchers who want to move from ideas to concrete research. Working in interdisciplinary teams, participants will develop hypothesis-driven projects using openly available datasets, including those from the Cogitate consortium, with a strong focus on reproducibility and preregistration. By the end of the workshop, each team will leave with a fully specified, preregistered study. Projects will also be eligible to compete for $25,000 in seed funding to support further development. Key details: Open to PhD students, postdocs, and early-career researchers (≤10 years post-PhD) Pre-event virtual kick-off: May 18–19 (TBC) Application deadline: April 13, 2026 More information: https://consciousness-commons.github.io/cc-assc-2026 Apply here: https://forms.gle/c1xpSTk2sJ3oTzHZ6 Please share this opportunity with interested students and colleagues. Best wishes, The Consciousness Commons Team -- Lucia Melloni

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Registration is now open for BrainNet 2026 workshop!
by Pascal Helson March 23, 2026

March 23, 2026

Dear all, We are thrilled to announce the BrainNet 2026 workshop, the event's 5th edition! If you're interested in exploring multidisciplinary approaches to brain data and brain functions, we cordially invite you to attend the BrainNet 2026 in Stockholm, Sweden, from May 25th to 27th. Our interdisciplinary set of speakers will showcase state-of-the-art methods for analysing and explaining brain data, offering insights from both theoretical and experimental perspectives. This year’s theme is ”A multi-scale view of the brain". The event is hybrid, you can participate either in person or virtually. For on-site participants, you can submit an abstract for giving a contributed talk! Talk submission closes on 12th of April 2026 and on-site registration closes on 10th of May 2026. The confirmed speakers are: Cornelis Stam, VU University Medical Center Francesca Mastrogiuseppe, SISSA Alex Roxin, Centre de Recerca Matemàtica Marilyn Gatica, Northeastern London Stefano Sarao Mannelli, Chalmers and Gothenburg University Rikkert Hindriks, VU University Amsterdam Nicolas Brunel, Bocconi University Moritz Helmstaedter, Max Planck Institute for Brain Research Tomoki Fukai, OIST Sofie Ährlund-Richter, MIT Ramòn Nartallo-Kaluarachchi, University of Oxford Gilad Silberberg, KI Sarah Vandenbulcke, KTH Madeleine Skeppås, Uppsala University Paolo Medini, Umeå University This is a unique opportunity to connect with the experts and the participants, the event is structured with ample time for discussions. You can find more details, including the registration link, on the BrainNet 2026 webpage <https://brainnet26.github.io/>. We look forward to seeing you there! Please distribute the link in your networks for anyone who might be interested in attending. Best regards, The BrainNet 2026 Organising Committee: Pascal Helson, IMN University of Bordeaux Arvind Kumar, KTH Henri Riihimäki, Nordita / Stockholm University Jason Smith, Nottingham Trent University Sarah Vandenbulcke, KTH BrainNet funding: BrainNet 2026 is supported by the Strategic Research Area Neuroscience (StratNeuro) [Karolinska Institute, Umeå University and KTH], the Wallenberg Initiative on Networks and Quantum Information (WINQ) program [Nordita institute] and Digital Futures [KTH, Stockholm University and RISE].

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World wide VVTNS series (6th season): Neural Manifolds in Spinal Networks that Orchestrate Movement | Wednesday, March 25, 2026, at 11:00 am ET -Rune Berg, University of Copenhagen
by David Hansel March 22, 2026

March 22, 2026

[image: VVTNS.png] https://www.wwtns.online <https://streaklinks.com/A9c7PbbpKY7PxB6PaAJWGD3-/https%3A%2F%2Fwww.wwtns.on…> - on twitter: wwtns@TheoreticalWide You are cordially invited to the lecture Rune Berg University of Copenhagen on the topic of Neural Manifolds in Spinal Networks that Orchestrate Movement The lecture will be held on zoom on March 25 ,2026, at *11:00 am ET * WARNING: USA IS AT SUMMER TIME. 11am ET = 3pm GMT=4pm CET > To receive the link: https://www.wwtns.online/register-page > *Abstract: *How does a cat gracefully walk and suddenly freeze when spotting a mouse? In this talk, we look at how networks in the spinal cord generate movement. In particular, we address the fundamental yet poorly understood question of motor control: How can rhythmic movements, such as walking, be generated and stopped at any point in the cycle while posture is preserved? Since conventional models of spinal motor function rely on alternation between flexor and extensor modules, which are limited to just two phases, this question exposes the essential shortcoming of the conventional understanding: How can a system with only two phases generate and stop walking in any phase? To address this question and better understand the generation and stopping of motor activity, we use Neuropixels probes in the rat spinal cord during voluntary, freely moving locomotion. We utilize optogenetic activation of a brainstem nucleus to induce stopping. During locomotion, neuronal manifold activity exhibits robust rotational patterns that are topologically invariant with respect to speed (Linden 2022). Furthermore, this trajectory converges on a stable point-attractor precisely at the moment of arrest, and it persists until the movement is resumed. Through computational modeling, we propose that the walk-to-stop represents a bifurcation from a limit cycle to a fixed point attractor. We also propose a structural network mechanism for its physical implementation (Komi 2026). The structural mechanism entails a longitudinal projectome with a skewed Mexican hat topology, i.e., primarily local recurrent excitation and longer-range inhibition. Such a network can generate motor patterns via traveling waves, with frequency and amplitude controlled independently, and rhythm induced without requiring cellular pacemaker mechanisms. Together, our experimental observations support a new theory for the mechanism behind the generation of movement by networks in the spinal cord. *About VVTNS : Launched as the World Wide Theoretical Neuroscience Seminar (WWTNS) in November 2020 and renamed in homage to Carl van Vreeswijk in Memoriam (April 20, 2022), Speakers have the occasion to talk about theoretical aspects of their work which cannot be discussed in a setting where the majority of the audience consists of experimentalists. The seminars, **held on Wednesdays at 11 am ET,** are 45-50 min long followed by a discussion. The talks are recorded with authorization of the speaker and are available to everybody on our YouTube channel.* ᐧ ᐧ

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PhD position in Neurotechnology in the Reichenbach lab, Friedrich-Alexander-University (FAU) Erlangen-Nürnberg, Germany
by Reichenbach, Tobias March 20, 2026

March 20, 2026

The Chair for Sensory Neuroengineering at the Department Artificial Intelligence in Biomedical Engineering is looking for a PhD student to work on an innovative project in neurotechnology. Together with partners in the Cluster4Future SEMECO (TU Dresden, MED-EL, Fraunhofer) you will develop a Brain-Computer-Interface (BCI) for the next generation of hearing prosthetics. To this end you will develop AI methods for analyzing brain signals and for steering the processing of acoustic signals. The prototype hearing prosthetic with neurofeedback should be then be able to abadjust to the needs of their users and to adapt in real time. AI at the interface to neurobiology is a highly timely topic of immense societal relevance. We offer you the possibility to actively shape this exciting and rapidly developing field. We support your work through: - A lively scientific environment within the Department and the possibility to cooperate with excellent partners at the TU Dresden, at the Friedrich-Alexander-University Erlangen-Nürnberg as well as with industry partners - An excellent environment to conduct leading science and to realise own scientific ideas - An excellent training in the development of AI methods for neuronal data - Development of your personal strengths, for instance through extensive courses on personal development - 26 days of holiday per year The renumeration is according to Group 13 of the German public service (TV-L). FAU is the most innovative university in Germany and ranks second among the most innovative European universities. It is located in one of the most attractive regions in Germany. Erlangen is a bicycle-friendly city with 100,000 inhabitants, former home of the Huguenots as well as a key location for industry. It is close to Nuremberg, a city with a population of half a million and a metropolitan flair. The mixture of academia, innovative companies, art and culture makes the region the perfect place for creative minds, keen athletes and nature lovers. To apply, please send a letter detailing your motivation, a CV, copies of your degrees and details of two referees. Please combine all documents into a single pdf and send them per email with subject “Application PhD student” to Prof. Dr. Tobias Reichenbach (tobias.j.reichenbach(at)fau.de). Applications will be considered until the 10th of April 2026. The preferred start date of the PhD position is the 1st of October 2026. More information is available here: https://www.neurotech.tf.fau.eu/phd-position-in-sensory-neurotechnology/ ------------------------------------------- Prof. Dr. Tobias Reichenbach Chair of Sensory Neurotechnology Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-University (FAU) Erlangen-Nürnberg Nürnberger Strasse 74 91052 Erlangen https://www.neurotech.tf.fau.eu/

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call for papers: Formal Models and Empirically Informed Simulations of Interpersonal Dynamics
by Peter Dayan March 19, 2026

March 19, 2026

Dear colleagues, We would like to draw your attention to the Call for Papers for the Special Issue "Formal Models and Empirically Informed Simulations of Interpersonal Dynamics" in the journal Zeitschrift für Psychologie (a journal published in English with a German title). The submission deadline for proposals is July 15, 2026, and the submission deadline for full manuscripts is November 15, 2026. Further information can be found at: https://econtent.hogrefe.com/doi/10.1027/2151-2604/a000619 Kind regards, Stefan Westermann and Peter Dayan --- Topic of the Special Issue: Within a dyad of two interacting individuals, each serves as the environment for the other and, in turn, experiences the other as their environment. Capturing the complex interpersonal dynamics that emerge from this interdependence is a major psychological and computational challenge. Interactions become even richer given polyadic relationships and larger groups. Mathematical and computational methods offer powerful tools for uncovering the mechanisms that shape such interact ions. The topical issue "Formal models and empirically informed simulations of interpersonal dynamics" will highlight recent advances in applying such methods to the study of interpersonal processes. We invite contributions from across psychological disciplines - including social, clinical, and personality psychology - that employ frameworks such as agent-based modeling, evolutionary game theory, Bayesian modeling, active inference, and related approaches to provide formal ways of representing, simulating, and analyzing interpersonal phenomena. Both purely theory-driven, formal models and empirically informed simulations are welcome, offering insights into the emergence, maintenance, and change of interpersonal patterns.

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Computational Neuroscience Introductory School (Stream I): Training Instructors Application
by Tolulope Gbayisomore March 19, 2026

March 19, 2026

The African Computational Neuroscience Education Initiative (ACNEI) invites applications for Training Instructors (TIs) for our *Computational Neuroscience Introductory School (Stream I) for young African scholars.* *About the Training Program* Virtual Training: 11th-22nd May 2026 (TIs participate alongside students) Training at specific regional hubs (optional for participants): 23th -27th May 2026 (Hands-on projects with open-access datasets and networking) *Training Instructor Responsibilities* Each Training Instructor will manage 5-10 undergraduate students during the virtual training, guiding them through: - Provided course resources and materials - Practical computational neuroscience exercises - Mentorship and Q&A support *Timeline * *Application Window*: *16th March - 1st April 2026* *How to Apply:* Complete your application at: https://forms.gle/FA6EPPhKsxnfx3m28 before 1st April 2026, 11:59 PM UTC. NB: If you need more information about the program modality, you can register for any of the Q&A sessions (*24th March - 28th March 2026) *before submitting the application. Register here: https://forms.gle/vrqUQD1rg3UeU79a8 For inquiries, contact contact(a)acnei.org or our Science Communications Lead at c.martina(a)acnei.org Regards, Program Development Lead, African Computational Neuroscience Education Initiative (ACNEI <www.acnei.org>)

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2026 NEURON Summer Course
by McDougal, Robert March 19, 2026

March 19, 2026

The 2026 NEURON Summer Course will be held at Duke University in Durham, NC from Wednesday, June 10, through Monday, June 15, 2026. The course will present a thorough introduction to computational modeling of neurons and networks with the NEURON simulator (https://www.neuronsimulator.org) and is suitable for individuals at all levels of expertise. The registration deadline is Monday, June 1, 2026. No applications will be accepted after that date, and there will be no on-site registration. Space is limited, and applications will be considered in the order received. For more information and the on-line registration form, see https://github.com/neuronsimulator/nrn/discussions/3729

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PhD Position on Digital Twins for Brain Health, University of the Balearic Islands (UIB)
by ruben.herzog＠postgrado.uv.cl March 18, 2026

March 18, 2026

PhD Position — Digital Twins for Brain Health Neuroscience PhD Program (Health Sciences), University of the Balearic Islands (UIB), Palma de Mallorca, Spain. https://edoctorat.uib.es/es/doctorat/TNEU/ We offer a fully funded PhD position focused on developing biophysical digital twins using M/EEG and fMRI data for large-scale in silico treatment evaluation in aging, neurodegenerative, and other brain conditions. This is a fully computational PhD involving intensive modelling and data analysis, combining computational neuroscience and precision medicine for in silico treatment discovery. The project uses large international clinical datasets (>6000 participants) and aims to develop model-based markers of treatment susceptibility with a focus in heterogeneous populations. Research will take place at IFISC (UIB–CSIC), a María de Maeztu Unit of Excellence internationally recognized for research in complex systems, dynamical systems, statistical physics, and network science, with applications ranging from neuroscience and biology to social and technological systems. The project will be developed in collaboration with research groups from the Department of Psychology at the University of the Balearic Islands, with access to HPC resources and an international collaboration network including INRIA Sophia-Antipolis, Trinity College Dublin, Imperial College London, BrainLat, Universidad de Buenos Aires, Paris Brain Institute, among others. Profile: quantitative background (physics, math, computer science, data science, neuroscience, quantitative psychology, engineering, biomedicine or related), Python programming, statistics, data analysis, and autonomous working style. Experience in modelling or neuroimaging analysis is valued. Candidates must be admitted to, or eligible for admission to, the Neuroscience PhD Program at UIB. See https://edoctorat.uib.es/es/doctorat/TNEU/desc.html#CriterisAdmissio for more information. Conditions: - Predoctoral contract (UIB) - ~€20,000 gross/year (annual adjustment) - 3 years guaranteed - €1000/year for conferences + €1000/year for research stays - Additional funding opportunities available - Support in grant applications and career development Based in Palma, Mallorca, a Mediterranean island combining a high quality of life, year-round mild climate, and an international environment —an ideal setting to pursue a PhD while enjoying one of Europe’s most livable regions. Mainly onsite work with flexibility. Working languages: English and Spanish. Deadline: mid of April Start: July Applications from all regions are welcome. Apply: send CV and motivation letter by email to ruben.herzog(a)uib.es

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Entropy-Aware Graph Neural,Networks: Theory, Methods,,and Applications - SI in MDPI Entropy
by Friedhelm Schwenker March 17, 2026

March 17, 2026

*Entropy-Aware Graph Neural,Networks: Theory, Methods,,and Applications* - Special Issue in MDPI Entropy For submission information, please visit the SI webpage: https://www.mdpi.com/journal/entropy/special_issues/1028YT5CB9 Submission deadline: June 30, 2026 Guest Editors: Junran Wu, Nan Wang, Friedhelm Schwenker Graph neural networks (GNNs) have emerged as a fundamental framework for learning from graph-structured data, demonstrating remarkable success across domains such as social network analysis, recommender systems, bioinformatics, and financial modeling. Entropy and information-theoretic principles provide a natural lens for analyzing the expressivity, generalization, and robustness of GNNs. From the viewpoint of Fisher information, mutual information, and information bottlenecks, entropy-aware frameworks can help explain and improve the propagation, compression, and preservation of structural information in networks. This Special Issue aims to advance the understanding of entropy-aware graph neural networks by bridging information theory and graph representation learning. We invite original research and review articles that - provide information-theoretic analysis of GNN mechanisms and architectures, - propose new entropy- or information-driven GNN methods, or - explore applications of entropy-aware graph learning in scientific, industrial, and social domains. -- Prof. Dr. Friedhelm Schwenker University of Ulm Institute of Neural Information Processing D-89069 Ulm, Germany phone: +49-731-50-24159 fax: +49-731-50-24156 email:friedhelm.schwenker@uni-ulm.de www:https://www.uni-ulm.de/in/neuroinformatik/institut/hidden/f-schwenker/

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