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Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø NeuroAI Annual Conference 2024

The 2024 Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø NeuroAI Annual Conference was held on Wednesday 17 July 2024 at the Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø Great Ormond Street Institute of Child Health drawing together machine learning and neuroscience researchers. The conference included talks by speakers from academia and industry, ranging from PhD students to professors, as well as poster sessions for researchers to display and discuss research.Ìý

Topics spanned the full scope of NeuroAI from Large Language Models to mouse behaviour and included keynote talks as well as quickfire lightning talks. The conference aimed to inspire attendees by demonstrating the potential of this field for future scientific advancements and novel applications.

Keynote Talks


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Dr Rui Ponte Costa

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Back to the present: self-supervised learning in cortical layers


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Dr Erin Grant Ìý

Gatsby Computational Neuroscience Unit and Sainsbury Wellcome CentreÌý

Nonlinear dynamics of localization in neural receptive fields


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Dr Kevin Miller

Google DeepMind, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø Institute of Ophthalmology and Sainsbury Wellcome CentreÌý

Data-driven discovery of cognitive model


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Dr Anthony Zador

Cold Spring Harbor LaboratoryÌý

Brain wiring through the genomic bottleneck


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Professor Maneesh Sahani

Gatsby Computational Neuroscience Unit, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍøÌý

Neural circuits, distributional representations, and recognition-parametrised learning: principles for building models of the sensory world


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Panel discussion

Dr Rui Ponte Costa, Dr Erin Grant, Dr Kevin Miller and Professor Maneesh Sahani


Lightning Talks

Marco Abrate, PhD Student, Cell and Developmental Biology, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø

An artificial neural network model of cognitive map development


Yang Chu, PhD Student, HiPEDS – EPSRC Centre for Doctoral Training, Imperial College London

Bootstrapping the auditory space map via an innate circuit


Dr Kira Düsterwald, PhD Student, Gatsby Computational Neuroscience Unit, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø

Unsupervised ground metric learning with tree Wasserstein distance


Dr Marcus Ghosh, AI in Science Postdoctoral Research Fellow, Imperial College London

Non-feedforward architectures enable diverse multisensory computations


Zonglun Li, PhD Student, Mathematics, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø

When reservoir computing meets information theory: the tendency of entropy change through spike timing-dependent plasticity


Gabriel Ocana Santero, PhD Student, Department of Physiology, Anatomy and Genetics, University of Oxford

Understanding serotonin: gated Deep Neural Networks reveal a unified model across its role in learning, neurodevelopment and psychedelics


Dr Alice Plebe, Research Fellow, Department of Computer Science, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø

Autonomous vehicles inspired by human brain and cognition


Dilip Rajeswari, Co-founder and CTO, XYZ

Predicting sex using resting-state electroencephalogram and deep learningbased convolutional transformer


Dr Nathan Skene, UK Dementia Research Institute Group Leader, Lecturer and UKRI Future Leaders Fellow, Imperial College London

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects


Dr Michał Wójcik, Research Scientist, Department of Physiology, Anatomy and Genetics, University of Oxford

Learning dynamics in the PFC can be explained by an external controller


Weihao Xia, PhD Student, Department of Statistical Science, Ïã¸ÛÁùºÏ²ÊÖÐÌØÍø

UMBRAE: Unified multimodal brain decoding


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