Models and interventions of cognition.

This review by Summerfield and Stachenfeld surveys how the dialog between neuroscience and AI research has evolved over the past decade. They highlight continued opportunities for synergy between these disciplines.

CBS CoCoNUT: A new interest group that comes together every two weeks to have an informal exchange about cognitive computational neuroscience.

All content on this site: Copyright © 2026 Elsevier B.V., its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.

Progress in artificial intelligence is turning machine consciousness from a philosophical curiosity into a societal issue, and has led to criticism of the widespread computational functionalist framew...

Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on univer...

Human and macaque studies of inferotemporal cortex (IT) have shaped our understanding of object vision, yet the extent of their representational alignment and the precise nature of this correspondence...

Abstract. Recent advances in neural networks trained on natural language have revealed that category-selective regions encode complex semantics and contextual information of natural scenes in addition...

Videos from CCN 2026, held in New York City, USA, August 3-6 2026 For full details of the 2026 program, visit https://2026.ccneuro.org/schedule-of-events/

Recent studies reveal striking representational alignment between artificial neural networks (ANNs) and biological brains, leading to proposals that all sufficiently capable systems converge on universal representations of reality. We argue that this claim of Universality is premature. We introduce the Umwelt Representation Hypothesis, which proposes that alignment arises not from convergence toward a single global optimum but from overlap in the ecological constraints under which systems develop. We review empirical evidence showing that representational differences between species, individuals, and ANNs are systematic and adaptive, which is difficult to reconcile with Universality. Finally, we reframe ANN model comparison as a method for mapping clusters of alignment in the ecological constraint space rather than as a search for a single optimal world model.

Modern artificial intelligence (AI) systems have achieved remarkable capabilities, but at an extraordinary energy cost. Training and running large-sca…

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared towards high-dimensional, highly separable neural representations.

Adaptive cognition requires structured internal models of objects and their relations. Predictive neural networks are often proposed to learn such world models, but how these are instantiated and how ...

What transforms a novice into an expert? In an fMRI study of chess players, the authors show that expert codes prioritise relational content, are more compressed, and reside in domain-general frontopa...

Learning is thought to arise from synaptic modifications embedded in brain-wide circuits, yet how such circuits coordinate plasticity to support complex behaviour is not known. Inspired by deep learni...

The success of large language models (LLMs) across many domains of AI research has generated intense debate. Some attribute their impressive performance on complex tasks to human-like linguistic and ...

LAION-fMRI (LfMRI / LAION MRI dataset): 5 subjects, 25,052 launch-release natural images, 165 acquired 7T fMRI sessions with single-trial GLMsingle betas, retinotopy, localizers, and diffusion.

Influential themed journal issues across the physical mathematical and engineering sciences.

Cambridge Core - Neurosciences - Scientific Theories of Consciousness

Foundation models in neuroscience: representational alignment versus mechanistic understanding

Spatial representations are a fundamental aspect of cognition. It remains largely unknown when and why the capacity to neurally represent space first …

Cheon and Paik show that overconfidence in deep neural networks arises from standard initialization practices, and that brief warm-up training with random noise improves uncertainty calibration and meta-cognitive recognition of unknown inputs.

A 2-week summer course hosted at Chalmers University of Technology on analytical approaches to language acquisition and higher-level cognition.