Cognition workshops

CIFMA connects researchers studying cognition across psychology, neuroscience, linguistics, philosophy, education, robotics and computer science. The workshop explores attention, memory, reasoning, language, learning and human interaction with physical and computational environments. It brings formal modelling and analysis methods into dialogue with cognitive science, with applications in cognitive robotics, biomedical signals and imaging, neural interfaces, human–computer interaction and intelligent educational systems. The meeting is co-located with SEFM 2026 in Malta. Abstract and paper submission deadlines have passed.

How reliably can non-invasive brain stimulation establish causal relationships in cognition, and which factors change its effects? This workshop examines the empirical, methodological and theoretical limits of causal inference with transcranial stimulation. It connects stimulation protocols, individual variability and biomarkers with multi-laboratory replication and open-science practice. The programme covers concurrent TMS-fMRI, white-matter pathways, paired associative stimulation, electric-field modelling and memory-related electrical stimulation. A satellite session includes practical TMS-EEG work. The satellite starts on 2 December with check-in at 13:30 and talks at 14:00. Main-workshop check-in begins at 08:20 on 3 December, followed by the introduction at 09:20; sessions resume at 09:00 on 4 December. Times are local CET (Europe/Rome). Registration and abstracts close on 31 October 2026, or earlier if capacity is reached. Fees are €100 for postdocs, doctoral students, residents and master's students; €200 for faculty and non-academic research or clinical staff; and €360 for company participants. The first 20 IMT doctoral students may register free. Registration includes the satellite, main programme, breaks and receptions; accommodation, lunches and the social dinner are additional. Fees are non-refundable.

UniReps asks when, how and why different neural models learn the same representations. Findings in neuroscience and artificial intelligence show that biological brains and artificial models often form similar representations when exposed to similar stimuli. The workshop brings machine learning, neuroscience and cognitive science together to discuss theoretical findings, empirical evidence and practical applications of this phenomenon: learning dynamics in neuroscience, identifiability in the functional and parameter spaces of artificial networks, representation alignment for model merging, stitching and reuse and for multimodal settings, and the invariances that emerge across learning processes, with the aim of unifying internal representations across distinct neural models. The 2026 workshop takes place in Paris on 12 December 2026, coinciding with NeurIPS in Paris. Invited speakers are Marco Baroni (Pompeu Fabra University), Alex Cayco-Gajic (École Normale Supérieure), Sophia Koepke (TUM/University of Tübingen), Joao Sacramento (Google) and Greta Tuckute (Harvard University). Technical paper submissions were due on 10 October 2026 (AoE).

The programme follows memory mechanisms from molecular and cellular processes through synapses, microcircuits and distributed brain networks. It connects engram formation and synaptic plasticity with encoding, consolidation, persistence and flexibility. Sessions examine transcriptional, translational, epigenetic and RNA regulation, together with neuromodulation, before considering circuit vulnerability and memory disruption in neurodegenerative and neuropsychiatric disorders. Novel methods and unresolved questions connect the different biological scales. A satellite symposium on synapses and cognition takes place on 26 April. On-site and virtual participation are available; applicants submit an abstract or a brief statement of interest and are selected for scientific relevance.

Methods in Computational Neuroscience (MCN) is a four-week Marine Biological Laboratory course that introduces the computational and mathematical techniques used to address how the brain solves problems at levels of neural organisation ranging from single membrane channels to operations of the entire brain. In each of the first three weeks the course moves up a level of complexity, from molecular and cellular to network to cognitive and behavioural, asking whether biologically plausible mechanisms can explain how nervous systems solve specific computational problems, how such problems can be decomposed, and how those decompositions relate to observable properties of neurons and circuits and their molecular building blocks. Core presentations are given jointly by theorists and experimentalists who have worked on the same problems, with optional first-week tutorials on dynamical systems, information theory, dimensionality reduction, neural network modelling and simulation in Python and MATLAB. Laboratory exercises combine quantitative analysis of experimental data with analytic and numerical exploration of models, often in teams mixing theoretical and experimental backgrounds, and the fourth week is reserved for student projects with resident faculty. The 2027 course, directed by Stefano Fusi (Columbia University) and Roozbeh Kiani (New York University), runs from 24 July to 21 August 2027 and is limited to 24 graduate students, postdocs and faculty from fields ranging from zoology and neurobiology to physics, engineering and mathematics. Applications are due on 10 March 2027, and financial aid is offered.

The School on Analytical Connectionism is a two-week summer course on analytical tools, including methods from statistical physics and probability theory, for probing neural network models of higher-level cognition, bringing together neuroscience, psychology and machine-learning communities. The 2027 school, held in Boston from 2 to 13 August 2027, focuses on interpretability in reasoning and mathematical cognition: how children or artificial systems use and acquire mathematical concepts, how mathematical intuition and expertise develop and interact, and how studying the heuristics and abstractions that AI systems develop may deepen understanding of human mathematical cognition. The first week offers core lectures on mathematical methods for neural network analysis and on key connectionist models linked to experimental observations; the second week offers frontiers lectures. Participants also join tutorials, poster sessions and contributed talks, work in teams on research projects that they pitch in week one and present at the end, and co-author peer-reviewed lecture notes collected into a special issue. The school is directed by Erin Grant (University of Alberta & Amii) and Stefano Sarao Mannelli (Chalmers University of Technology & University of Gothenburg), with SueYeon Chung (Harvard University & Flatiron Institute) and Andrew Saxe (University College London) as advisors. Applications open on 1 February 2027 and close on 1 April 2027 (AoE); first-round outcomes are communicated on 17 May 2027.