Category browser: Neuromorphic
Related software
Related talks
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Intelligence as resource efficiency
Dan Goodman at Mathematical Neuroscience conference (2025) -
Spikes are cool again! What's next?
Dan Goodman at Institute for Neuroinformatics, University of Zurich (2025)
Related publications
2026
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AlKilany A, Goodman DFM
Neuromodulation enhances the capability and efficiency of spiking neural networks.
Preprint
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Sun P, Su Z, Achterberg J, Indiveri G, Goodman DFM, Akarca D
(2026)
Algorithm-hardware co-design of neuromorphic networks with dual memory pathways.
Nature Machine Intelligence
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Yu Z, Sun P, Goodman DFM
(2026)
Beyond rate coding: surrogate gradients enable spike timing learning in spiking neural networks.
Neuromorphic Computing and Engineering
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Béna G
(2026)
Physical constraints and functional demands shape modular neuromorphic intelligence.
PhD thesis, Imperial College London
2025
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Sun P, Achterberg J, Su Z, Goodman DFM, Akarca D
Exploiting heterogeneous delays for efficient computation in low-bit neural networks.
Preprint
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Béna G, Faldor M, Goodman DFM, Cully A
(2025)
A Path to Universal Neural Cellular Automata.
GECCO
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Béna G, Goodman DFM
(2025)
Dynamics of specialization in neural modules under resource constraints.
Nature Communications
2023
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Perez N
(2023)
Robust and efficient training on deep spiking neural networks.
PhD thesis, Imperial College London
2021
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Perez-Nieves N, Goodman DFM
(2021)
Sparse spiking gradient descent.
Advances in Neural Information Processing Systems
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Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM
(2021)
Neural heterogeneity promotes robust learning.
Nature Communications
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Zenke F, et al.
(2021)
Visualizing a joint future of neuroscience and neuromorphic engineering.
Neuron
2020
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Stimberg M, Goodman DFM, Nowotny T
(2020)
Brian2GeNN: a system for accelerating a large variety of spiking neural networks with graphics hardware.
Scientific Reports
2019
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Perez-Nieves N, Leung VCH, Dragotti PL, Goodman DFM
(2019)
Advantages of heterogeneity of parameters in spiking neural network training.
Cognitive Computational Neuroscience
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Stimberg M, Brette R, Goodman DFM
(2019)
Brian 2, an intuitive and efficient neural simulator.
eLife
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Stimberg M, Goodman DFM, Brette R, De Pittà M
(2019)
Modeling neuron-glia interactions with the Brian 2 simulator.
Springer
2018
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Blundell I, et al.
(2018)
Code Generation in Computational Neuroscience: A Review of Tools and Techniques.
Frontiers in Neuroinformatics
2014
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Stimberg M, Goodman DFM, Benichoux V, Brette R
(2014)
Equation-oriented specification of neural models for simulations.
Frontiers in Neuroinformatics
2013
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Goodman DFM, Brette R
(2013)
Brian simulator.
Scholarpedia
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Goodman DFM, Brette R
(2013)
Brian Spiking Neural Network Simulator.
SpringerReference
2011
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Brette R, Goodman DFM
(2011)
Vectorised algorithms for spiking neural network simulation.
Neural Computation
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Rossant C, Goodman DFM, Fontaine B, Platkiewicz J, Magnusson AK, Brette R
(2011)
Fitting neuron models to spike trains.
Frontiers in Neuroscience
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Fontaine B, Goodman DFM, Benichoux V, Brette R
(2011)
Brian Hears: online auditory processing using vectorisation over channels.
Frontiers in Neuroinformatics
2010
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Rossant C, Goodman DFM, Platkiewicz J, Brette R
(2010)
Automatic fitting of spiking neuron models to electrophysiological recordings.
Frontiers in Neuroinformatics
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Goodman DFM
(2010)
Code Generation: A Strategy for Neural Network Simulators.
Neuroinformatics
2009
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Brette R, Goodman D
(2009)
Brian: a simple and flexible simulator for spiking neural networks.
The Neuromorphic Engineer
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Goodman DFM, Brette R
(2009)
The Brian simulator.
Frontiers in Neuroscience