World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
53
Citations
12358
World Ranking
4784
National Ranking
285

Overview

Murray Shanahan is affiliated with Imperial College London in the United Kingdom. Their research focuses primarily within the field of Computer Science, with 69 publications. Within this broad field, Shanahan has contributed extensively to subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Safety Research, and Social Psychology.

The scientist's work covers a range of topics including Topic Modeling, Natural Language Processing Techniques, Explainable Artificial Intelligence (XAI), Reinforcement Learning in Robotics, Domain Adaptation and Few-Shot Learning, Ethics and Social Impacts of AI, and Neural dynamics and brain function.

Shanahan's recent publications include:

  • "Role play with large language models" (2023, Nature)
  • "Talking about Large Language Models" (2024, Communications of the ACM)
  • "Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning" (2022, arXiv (Cornell University))
  • "Rethink reporting of evaluation results in AI" (2023, Science)
  • "Talking About Large Language Models" (2022, arXiv (Cornell University))

The main publication venues for Shanahan include arXiv (Cornell University) with 33 papers, Trends in Cognitive Sciences with 2 publications, Nature, Communications of the ACM, and Science with one publication each.

Frequent collaborators in Shanahan's research include:

  • Antonia Creswell
  • Lucy G. Cheke
  • Kyriacos Nikiforou
  • Borja G. León
  • Francesco Belardinelli

Best Publications

  • The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs

    Robin Lester Carhart-Harris;Robert Leech;Peter John Hellyer;Murray Shanahan

  • The event calculus explained

    Murray Shanahan

  • Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

    Nat Dilokthanakul;Pedro A. M. Mediano;Marta Garnelo;Matthew C. H. Lee

  • Solving the Frame Problem: A Mathematical Investigation of the Common Sense Law of Inertia

    Murray Shanahan

  • Role play with large language models

    Unknown

  • Prediction is deduction but explanation is abduction

    Murray Shanahan

  • A cognitive architecture that combines internal simulation with a global workspace

    Murray Shanahan

  • Metastable chimera states in community-structured oscillator networks.

    Murray Shanahan

  • Conditional Neural Processes

    Marta Garnelo;Dan Rosenbaum;Christopher Maddison;Tiago Ramalho

  • Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning

    Unknown

  • Reconciling deep learning with symbolic artificial intelligence: representing objects and relations

    Marta Garnelo;Murray Shanahan

  • Large-scale network organization in the avian forebrain: a connectivity matrix and theoretical analysis.

    Murray Shanahan;Verner P. Bingman;Toru Shimizu;Martin Wild

  • Applying Global Workspace Theory to the Frame Problem.

    Murray Shanahan;Bernard J. Baars

  • Accelerated simulation of spiking neural networks using GPUs

    Andreas K. Fidjeland;Murray P. Shanahan

  • The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention.

    Peter J. Hellyer;Murray Shanahan;Gregory Scott;Richard J. S. Wise

  • Relational Deep Reinforcement Learning.

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

  • Training a spiking neural network to control a 4-DoF robotic arm based on Spike Timing-Dependent Plasticity

    Alexandros Bouganis;Murray Shanahan

  • An abductive event calculus planner

    Murray Shanahan

  • Towards Deep Symbolic Reinforcement Learning

    Marta Garnelo;Kai Arulkumaran;Murray Shanahan

  • Cognitive Flexibility through Metastable Neural Dynamics Is Disrupted by Damage to the Structural Connectome

    Peter J. Hellyer;Gregory Scott;Murray Shanahan;David J. Sharp

  • Perception as abduction: turning sensor data into meaningful representation.

    Murray Shanahan

  • The Event Calculus in Classical Logic - Alternative Axiomatisations.

    Rob Miller;Murray Shanahan

  • Deep reinforcement learning with relational inductive biases

    Vinícius Flores Zambaldi;David Raposo;Adam Santoro;Victor Bapst

Frequent Co-Authors

Claudia Clopath
Claudia Clopath Imperial College London
Anil K. Seth
Anil K. Seth University of Sussex
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
Demis Hassabis
Demis Hassabis Google (United States)
Robert Leech
Robert Leech King's College London
Matthew Botvinick
Matthew Botvinick Yale University
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Onur Güntürkün
Onur Güntürkün Ruhr University Bochum
David J. Sharp
David J. Sharp Imperial College London

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