
The Neural Reckoning Group is led by Dan Goodman at Imperial College London.
We aim to understand the brain, and intelligent behaviour more widely, via theoretical and computational models.
We are particularly interested in spiking neural networks and the role they play in sensory processing. Machine learning is essential to our work, as we believe that only by understanding how the brain copes with messy, real-world complexity can we hope to understand what makes it unique. A key part of our work is neuroinformatics, building open source software packages to make our methods freely available to all. For a brief overview of our interests, see the selection of papers, software and organisations below.
We maintain a short list of resources for learning computational neuroscience that might be useful to students and people new to the field.
Recent publications
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Weerts L, Rosen S, Clopath C, Goodman DFM
The Psychometrics of Automatic Speech Recognition.
Preprint -
Engel I, Goodman DFM, Picinali L (2022)
Assessing HRTF preprocessing methods for Ambisonics rendering through perceptual models.
Acta Acustica -
Perez-Nieves N, Goodman DFM (2021)
Sparse spiking gradient descent.
Advances in Neural Information Processing Systems -
Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2021)
Neural heterogeneity promotes robust learning.
Nature Communications
Selected publications
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Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2021)
Neural heterogeneity promotes robust learning.
Nature Communications -
Achakulvisut T, et al. (2021)
Towards democratizing and automating online conferences: lessons from the Neuromatch conferences.
Trends in Cognitive Sciences -
Zenke F, et al. (2021)
Visualizing a joint future of neuroscience and neuromorphic engineering.
Neuron -
Stimberg M, Brette R, Goodman DFM (2019)
Brian 2, an intuitive and efficient neural simulator.
eLife -
Rossant C, et al. (2016)
Spike sorting for large, dense electrode arrays.
Nature Neuroscience -
Goodman DFM, Benichoux V, Brette R (2013)
Decoding neural responses to temporal cues for sound localization.
eLife
Selected software packages
A Python simulator for spiking neural networks.
Python package for psychophysical tests of automatic speech recognition systems.
Graph layout using stochastic gradient descent.
Organisations
Inclusive networking and education for neuroscience.
Organisation for neuroscientists interested in spiking neural networks.
European network for immersive audio.
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