Topical focus: Language Acquisition
August 17–28 2026; Chalmers University of Technology, Gothenburg
https://www.analytical-connectionism.net/school/2026/
Apply by 17 April 2026
We are delighted to announce the 4th edition of the School on Analytical Connectionism. This 2-week course will focus on analytical theories of cognition, bringing together ideas from connectionism, cognitive science, and modern theories of machine learning. This course is appropriate for graduate students, postdoctoral fellows and faculty in fields from psychology and neuroscience, to physics, computer science, and mathematics.
This year we will discuss questions like:
- How can structured linguistic knowledge emerge from learning?
- What theoretical principles govern generalisation, compositionality, and representation in neural systems?
- How can we move beyond empirical demonstrations toward mechanistic, mathematically grounded explanations of cognitive phenomena like language acquisition?
The course will bring together neuroscience, psychology, and machine-learning communities, introducing participants to connectionist theories of higher-level cognition and psychology as well as analytical methods for neural-network analysis. Participants will work in teams, mentored by course lecturers and organisers, to develop novel research projects on topics related to analytical connectionism. Participants will present the research proposals in Week 1 and interim results at the end of the course.
Confirmed Faculty:
- Michael Biehl (University of Groningen)
- Caroline Rowland (Max Planck Institute for Psycholinguistics)
- Paul Smolensky (Microsoft Research & Johns Hopkins)
- Marco Baroni (ICREA & Pompeu Fabra University)
- Francesco Cagnetta (SISSA)
- Alex Cristia (ENS & CNRS)
- Bruno Loureiro (ENS & CNRS)
- Kanishka Misra (UT Austin)
- Isabel Papadimitriou (University of British Columbia)
- Luca Saglietti (Bocconi University)
- Asad Sayeed (Gothenburg University)
- Larissa Samuelson (University of East Anglia)
- Kenny Smith (University of Edinburgh)
- Chris Summerfield (Oxford University)
Organisers: SueYeon Chung, Erin Grant, Flavio Nicoletti, Stefano Sarao Mannelli, Andrew Saxe
Neural Computation Unit at Okinawa Institute of Science and Technology Graduate University (Prof. Kenji Doya) has research positions open for developing learning algorithms and use cases for the optical quantum neural networks developed by the Moonshot R&D Project in optical quantum computers (lead by Prof. Akira Furusawa):
https://optical-quantum-computers.jp/en/
While most quantum computers today resort to error correction of qubits, which is highly costly, this project aims to achieve efficient system-level computation despite noisy elements, like the brain appears to be doing.
The specification of the current machine is described in this preprint (https://arxiv.org/abs/2506.16147) and the software development kit is open in GitHub (https://github.com/HidehiroYonezawa/mqc3) The machine is being upgraded to allow nonlinear (non-Gaussian) operations.
Applicants should have strong background in mathematics, computer science, and/or machine learning. Experiences in quantum physics and/or computational neuroscience are welcome.
If you are interested, please send your CV, publication list, research statement, and referees (up to three names with e-mail) using the application form from our web page: https://www.oist.jp/ncu
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Kenji Doya <doya(a)oist.jp>
Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University
1919-1 Tancha, Onna, Okinawa 904-0495, Japan
Phone: +81-98-966-8594; Fax: +81-98-966-2891
https://www.oist.jp/ncu
