NeuroAI

The seventh annual UCL NeuroAI Conference brings together researchers working at the intersection of machine learning and neuroscience. UCL NeuroAI frames the meeting around the shared lineage of the two fields: machine learning and the brain use similar computations to process and compress information, AI models can emulate neural functions and the circuits supporting them, AI tools help discover, segment and track neural and behavioural states, and problems posed in neuroscience in turn inspire advances in AI. Keynote speakers are Wolfgang Maass (Technische Universität Graz), Agostina Palmig

…neuroscience, computational modelling, NeuroAI and data science. The 15th annual meeting takes place from 25 to 27 November 2026 at Université Paris Cité, Campus Odéon, Paris. Keynote speakers are Asli Ayaz (VIB-KU Leuven Center for Neuroscience), Sophie Bagur (ESPCI), Joao Bar

The second NeuroAI workshop at AAAI connects biological computation with adaptive multimodal AI. It examines how cortical organization, inhibitory–excitatory balance, sparse representations and context-sensitive plasticity can inform efficient learning and reduce forgetting. The reverse direction uses AI to study neural and behavioural data, circuit dynamics and biologically realistic models, with shared benchmarks connecting neuroscience and machine learning. Submissions are due 20 November 2026, with decisions on 5 December. Double-blind review follows AAAI-27 instructions. Short papers all

…and compute-in-memory architectures; NeuroAI, including theory of neural circuits and its experimental support, local learning and plasticity, and connectomics; neuroscience-ins

The Simons Computational Neuroscience Imbizo is a three-week school in Noordhoek, Cape Town, South Africa, that gives African and international students intensive training in research techniques in computational and theoretical neuroscience; imbizo is an isiXhosa and isiZulu word for a gathering to share knowledge. The 2027 school runs from 27 August to 17 September 2027 in three cycles of six working days, each focused on one theme: Molecules to Behavior, on biophysical mechanisms in individual cells and how they become building blocks of neural networks; Cells to Circuits to Behavior, on sys

…representations, discussing what a NeuroAI version of the Turing test could reveal about whether artificial and biological systems solve tasks in similar ways.

One of key hallmarks of cognitive flexibility is the capability of quickly performing neural computation to accommodate dynamic, context-switching environments and deal with task uncertainty [1-3]. Accumulating evidence from human and animal experiments have suggested that the mediodorsal (MD) thalamus and prefrontal cortex (PFC) are a pair of critical partners to achieve cognitive control. e found that such multiplicative thalamocortical gating enables a local Hebbian-weight amplification for prefrontal synaptic plasticity and is the key to facilitate a low-rank structure in prefrontal comput

…Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

…Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri | https://groups.google.com/g/medarc-fmri).

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). Title: Brain-optimized inference improves reconstructions of fMRI brain activity Abstract: The release of large datasets and developments in AI have led to dramatic improvements in decoding methods that reconstruct seen images from human brain activity. We evaluate the prospect of further improving recent decoding methods by optimizing for consistency between reconstructions and brain activity during inference. We sample seed reconstructions from a base decoding method, then iteratively re

Trends in NeuroAI is a reading group hosted by the MedARC Neuroimaging & AI lab (https://medarc.ai/fmri). This will be an informal journal club presentation, we do not have an author of the paper joining us. Title: Brain decoding: toward real-time reconstruction of visual perception Abstract: In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from