Scholar

Dan F M Goodman

Google Scholar ID: 2HiNqI4AAAAJ

Imperial College London

NeuroscienceSpiking neural networksMachine learning

Citations & Impact

All-time

Citations

3,514

H-index

i10-index

Publications

Co-authors

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Publications

20 items

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Resume (English only)

Academic Achievements

Published extensively in top-tier journals including Nature Communications, PLoS Computational Biology, eLife, and Neuron
Key recent works include 'Beyond Rate Coding: Surrogate Gradients Enable Spike Timing Learning in Spiking Neural Networks' (preprint)
2025 paper in PLoS Computational Biology on innate mechanisms for spatial hearing
2025 Cognitive Computational Neuroscience paper showing long delays reduce weight precision requirements in SNNs
2025 GECCO publication on universal neural cellular automata
2024 PLoS Computational Biology paper demonstrating optimal nonlinear multisensory fusion
2021 Nature Communications paper on neural heterogeneity enabling robust learning
2019 eLife paper introducing Brian 2, a widely used neural simulator
Developed and maintains open-source software packages: Brian (Python-based spiking neural network simulator) and HumanlikeHearing (for psychophysical evaluation of speech recognition systems)

Background

Principal investigator of the Neural Reckoning Group at Imperial College London
Aims to discover unifying principles of intelligent systems, spanning biological (e.g., the brain) and artificial systems
Employs theoretical and computational approaches
Focuses particularly on spiking neural networks and their role in sensory processing
Believes machine learning is essential to understanding how the brain handles real-world complexity
Strongly committed to neuroinformatics and open-source software development for scientific reproducibility and accessibility

Co-authors

85 total