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We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware.
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Accessed: 2017-08-12
“Projects - The neuromorphics project - Stanford University.” · 2017
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J. Gosmann and C. Eliasmith, “Automatic optimization of the computation graph in the Nengo neural network simulator,”
2017
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A. R. Voelker and C. Eliasmith, “Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells,”
2017
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2015
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2016
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S. Sharma, S. Aubin, and C. Eliasmith, “Large-scale cognitive model design using the Nengo neural simulator,”
2016
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J. Knight, A. R. Voelker, A. Mundy, C. Eliasmith, and S. Furber, “Efficient SpiNNaker simulation of a heteroassociative memory using the Neural Engineering Framework,” in
2016
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M. Berzish, C. Eliasmith, and B. Tripp, “Real-time FPGA simulation of surrogate models of large spiking networks,” in
2016
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PhD thesis, University of Manchester, 2016
A. Mundy, · 2016
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Closest in time.
A. R. Voelker, B. V. Benjamin, T. C. Stewart, K. Boahen, and C. Eliasmith, “Extending the Neural Engineering Framework for nonideal silicon synapses,” in
2017
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“Nengolib – Additional extensions and tools for modelling dynamical systems in Nengo.” · 2017
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2017
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A. Stöckel, “Point neurons with conductance-based synapses in the Neural Engineering Framework,” tech. rep., Centre for Theoretical Neuroscience, Waterloo, ON, 2017
2017
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