About the summer school notes
Gothenburg · 17–28 August 2026
These handwritten notes were taken at the 2026 School on Analytical Connectionism. The eight notebooks span the core lecture series and frontier talks on neural and symbolic representation, child language acquisition, statistical-physics and high-dimensional approaches to learning, curricula, linguistic generalization, hierarchical grammar, and language evolution.
Each notebook is one continuous scrolling page!
- Paul Smolensky — From Brain Signals to Language Distributed representations, tensor-product binding, compositional structure, and LLMs.
- Caroline Rowland — The Puzzle of Language Acquisition Multimodal input, frequency effects, grammar development, and computational models.
- Michael Biehl — Statistical Physics of Learning Perceptrons, generalization, replica methods, layered networks, and unsupervised learning.
- Chris Summerfield — Curriculum Learning Critical periods, loss of plasticity, off-manifold learning, and cognitive flexibility.
- Kanishka Misra — Linguistic Generalizations Input evidence, controlled language-model rearing, and dative alternation.
- Francesco Cagnetta — From Correlations to Grammar Hierarchical compositionality, latent tree structures, and the emergence of grammar.
- Bruno Loureiro — What Makes Neural Nets Good Learners? High-dimensional asymptotics, two-layer networks, generalization, and feature learning.
- Kenny Smith — How Learning and Use Shape Evolving Linguistic Systems Learnability, communicative use, compositional structure, and language evolution.
Eight Lecture Notebooks
Open a cover to read
Paul Smolensky
From Brain Signals to Language
Neural and distributed representations, tensor-product binding, compositional structure, generative linguistics, and links between LLMs and language.
Caroline Rowland
The Puzzle of Language Acquisition
The language acquisition problem, child-directed input, frequency effects, inductive biases, emerging grammar, and computational models of learning.
Michael Biehl
Statistical Physics of Learning
Perceptrons, statistical mechanics and generalization, prototype learning, hidden-unit specialization, layered networks, and unsupervised learning.
Chris Summerfield
Curriculum Learning in Natural and Artificial Intelligence
Critical periods, structured training, loss of plasticity, off-manifold learning, dimensionality, ageing, and cognitive flexibility.
Kanishka Misra
The Role of Input in Shaping Linguistic Generalizations
Direct and indirect evidence, controlled rearing of language models, adjective ordering, dative alternation, and development.
Francesco Cagnetta
From Correlations to Grammar: How Neural Networks Acquire Hierarchical Language Structures
Hierarchical compositionality, tree-like generative models, latent random hierarchies, and the emergence of grammatical structure in neural networks.
Bruno Loureiro
What Makes Neural Nets Good Learners?
High-dimensional asymptotics of two-layer networks, empirical risk minimization, generalization, feature learning, and data structure.
Kenny Smith
How Learning and Use Shape Evolving Linguistic Systems
Artificial-language learning, learnability and communicative use, compositional structure, and the evolution of linguistic systems.