Category: Modelling
Related videos
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Nonlinearity and network topology in multimodal circuitsTalk / 2024
Marcus Ghosh talk at ICNS -
Brain Inspired interviewInterview / 2024
Dan Goodman interview on Brain Inspired podcast -
Multimodal units fuse-then-accumulate evidence across channelsTalk / 2023
Talk on multimodal processing given at VVTNS 2023 seminar series -
The Psychometrics of Automatic Speech RecognitionTalk / 2022
Talk on applying psychometric testing to automatic speech recognition systems.
Related publications
2024
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Ghosh M, et al.
Spiking neural network models of sound localisation via a massively collaborative process.
Preprint -
Ghosh M, Béna G, Bormuth V, Goodman DFM (2024)
Nonlinear fusion is optimal for a wide class of multisensory tasks.
PLoS Computational Biology -
Habashy KG, Evans BD, Goodman DFM, Bowers JS
Adapting to time: why nature evolved a diverse set of neurons.
Preprint
2023
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Perez N (2023)
Robust and efficient training on deep spiking neural networks.
PhD thesis, Imperial College London
2022
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Goodman D, Fiers T, Gao R, Ghosh M, Perez N (2022)
Spiking Neural Network Models in Neuroscience - Cosyne Tutorial 2022.
Zenodo -
Weerts L, Rosen S, Clopath C, Goodman DFM
The Psychometrics of Automatic Speech Recognition.
Preprint
2021
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Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2021)
Neural heterogeneity promotes robust learning.
Nature Communications
2020
2019
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Stimberg M, Goodman DFM, Brette R, De Pittà M (2019)
Modeling neuron-glia interactions with the Brian 2 simulator.
Springer -
Chu Y, Goodman DFM (2019)
An Inference Network Model for Goal-directed Attentional Selection.
Cognitive Computational Neuroscience -
Weerts L, Clopath C, Goodman DFM (2019)
A Unifying Framework for Neuro-Inspired, Data-Driven Detection of Low-Level Auditory Features.
Cognitive Computational Neuroscience -
Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM (2019)
Advantages of heterogeneity of parameters in spiking neural network training.
Cognitive Computational Neuroscience -
Lestang J-H (2019)
The role of canonical neural computations in sound localization.
PhD thesis, Imperial College London - + 3 conference papers
2018
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Goodman DFM, Winter IM, Léger AC, de Cheveigné A, Lorenzi C (2018)
Modelling firing regularity in the ventral cochlear nucleus: mechanisms, and effects of stimulus level and synaptopathy.
Hearing Research
2017
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Goodman DF (2017)
On the use of hypothesis-driven reduced models in auditory neuroscience.
Acoustical Society of America -
Lestang JH, Goodman DF (2017)
The roles of inhibition and adaptation for spatial hearing in difficult listening conditions.
Acoustical Society of America - + 2 conference papers
2016
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Dietz M, et al. (2016)
A framework for auditory model comparability and applicability.
Acoustical Society of America - + 1 conference paper
2015
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Goodman DFM, de Cheveigné A, Winter IM, Lorenzi C (2015)
Downstream changes in firing regularity following damage to the early auditory system.
Computational Neuroscience - + 1 conference paper
2013
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Goodman DFM, Benichoux V, Brette R (2013)
Decoding neural responses to temporal cues for sound localization.
eLife
2011
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Kremer Y, Léger J-F, Goodman D, Brette R, Bourdieu L (2011)
Late emergence of the vibrissa direction selectivity map in the rat barrel cortex.
Journal of Neuroscience
2010
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Goodman DFM, Brette R (2010)
Learning to localise sounds with spiking neural networks.
Advances in Neural Information Processing Systems -
Goodman DFM, Brette R (2010)
Spike-timing-based computation in sound localization.
PLoS Computational Biology