Imperial College London

Dr Dan Goodman

Faculty of EngineeringDepartment of Electrical and Electronic Engineering




+44 (0)20 7594 6264d.goodman Website




1001Electrical EngineeringSouth Kensington Campus





The aim of my research is to uncover the principles underlying neural computation with precisely timed spikes. This is a form of computation specific to the brain, being radically different to both digital and analogue computation. I have developed several software tools for working with spiking neurons, notably the "Brian" spiking neural network simulator. My focus is on sensory processing of complex, realistic stimuli, primarily in the auditory system.

For more information, see my group webpage, the Neural Reckoning Group.



Achakulvisut T, Ruangrong T, Bilgin I, et al., 2020, Improving on legacy conferences by moving online, Elife, Vol:9, ISSN:2050-084X

Stimberg M, Goodman D, Nowotny T, 2020, Brian2GeNN: accelerating spiking neural network simulations with graphics hardware, Scientific Reports, Vol:10, ISSN:2045-2322, Pages:1-12

Steadman M, Kim C, Lestang J-H, et al., 2019, Short-term effects of sound localization training in virtual reality, Scientific Reports, Vol:9, ISSN:2045-2322

Zheng JX, Pawar S, Goodman DFM, 2019, Further towards unambiguous edge bundling: Investigating power-confluentdrawings for network visualization, Ieee Transactions on Visualization and Computer Graphics, ISSN:1077-2626


Perez-Nieves N, Leung VCH, Dragotti PL, et al., 2019, Advantages of heterogeneity of parameters in spiking neural network training, 2019 Conference on Cognitive Computational Neuroscience, Cognitive Computational Neuroscience

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