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Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control.
Spiking neural network on neuromorphic hardware for energy-efficient unidimensional slam
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A wafer-scale neuromorphic hardware system for large-scale neural modeling
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Smoothing and differentiation of data by simplified least squares procedures
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An image synthesizer
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Chaos in random neural networks
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The storage of time intervals using oscillating neurons
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Modeling brain function: The world of attractor neural networks
Amit, D. J. (1992) · 1992
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Chaos in neuronal networks with balanced excitatory and inhibitory activity
Van Vreeswijk, C. and Sompolinsky, H. (1996) · 1996
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Networks of spiking neurons: the third generation of neural network models
Maass, W. (1997) · 1997
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Attractor neural network models of spatial maps in hippocampus
Tsodyks, M. (1999) · 1999
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Dynamics of networks of randomly connected excitatory and inhibitory spiking neurons
Brunel, N. (2000) · 2000
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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note
Jaeger, H. (2001) · 2001
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Movement imitation with nonlinear dynamical systems in humanoid robots
Ijspeert, A. J., Nakanishi, J., and Schaal, S. (2002) · 2002
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Real-time computing without stable states: A new framework for neural computation based on perturbations
Maass, W., Natschläger, T., and Markram, H. (2002) · 2002
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Neural engineering: Computation, representation, and dynamics in neurobiological systems
Eliasmith, C. and Anderson, C. H. (2004) · 2004
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Role of delays in shaping spatiotemporal dynamics of neuronal activity in large networks
Roxin, A., Brunel, N., and Hansel, D. (2005) · 2005
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Regularization and variable selection via the elastic net
Zou, H. and Hastie, T. (2005) · 2005
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Nest (neural simulation tool)
Gewaltig, M.-O. and Diesmann, M. (2007) · 2007
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Echo state network
Jaeger, H. (2007) · 2007
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Local excitation-lateral inhibition interaction yields oscillatory instabilities in nonlocally interacting systems involving finite propagation delay
Hutt, A. (2008) · 2008
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Generating coherent patterns of activity from chaotic neural networks
Sussillo, D. and Abbott, L. F. (2009) · 2009
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The smoothed spectral abscissa for robust stability optimization
Vanbiervliet, J., Vandereycken, B., Michiels, W., Vandewalle, S., and Diehl, M. (2009) · 2009
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Recurrent networks with short term synaptic depression
York, L. C. and Van Rossum, M. C. (2009) · 2009
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A theoretical analysis of feature pooling in visual recognition
Boureau, Y.-L., Ponce, J., and LeCun, Y. (2010) · 2010
Activity dynamics and signal representation in a striatal network model with distance-dependent connectivity
Spreizer, S., Angelhuber, M., Bahuguna, J., Aertsen, A., and Kumar, A. (2017) · 2017
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Bio-inspired multi-layer spiking neural network extracts discriminative features from speech signals
Tavanaei, A. and Maida, A. (2017) · 2017
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Methods for applying the neural engineering framework to neuromorphic hardware
Voelker, A. R. and Eliasmith, C. (2017) · 2017
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Loihi: A neuromorphic manycore processor with on-chip learning
Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G., Cao, Y., Choday, S. H., Dimou, G., Joshi, P., Imam, N., Jain, S., et al. (2018) · 2018
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Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning
Lee, C., Panda, P., Srinivasan, G., and Roy, K. (2018) · 2018
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Cited alongside, same era.
Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex
London, M., Roth, A., Beeren, L., Häusser, M., and Latham, P. E. (2010) · 2010
Cited alongside, same era.
Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus
Itskov, V., Curto, C., Pastalkova, E., and Buzsáki, G. (2011) · 2011
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Online anomaly detection in unmanned vehicles
Khalastchi, E., Kaminka, G. A., Kalech, M., and Lin, R. (2011) · 2011
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Robust timing and motor patterns by taming chaos in recurrent neural networks
Laje, R. and Buonomano, D. V. (2013) · 2013
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Nengo: a Python tool for building large-scale functional brain models
Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T., Rasmussen, D., Choo, X., Voelker, A., and Eliasmith, C. (2014) · 2014
Cited alongside, same era.
The SpiNNaker project
Furber, S. B., Galluppi, F., Temple, S., and Plana, L. A. (2014) · 2014
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Programming spiking neural networks on intel’s loihi
Lin, C.-K., Wild, A., Chinya, G. N., Cao, Y., Davies, M., Lavery, D. M., and Wang, H. (2018) · 2018
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Robust visual localization across seasons
Naseer, T., Burgard, W., and Stachniss, C. (2018) · 2018
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Braindrop: A mixed-signal neuromorphic architecture with a dynamical systems-based programming model
Neckar, A., Fok, S., Benjamin, B. V., Stewart, T. C., Oza, N. N., Voelker, A. R., Eliasmith, C., Manohar, R., and Boahen, K. (2018) · 2018
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Flexibility in motor timing constrains the topology and dynamics of pattern generator circuits
Pehlevan, C., Ali, F., and Ölveczky, B. P. (2018) · 2018
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Benchmarking keyword spotting efficiency on neuromorphic hardware
Blouw, P., Choo, X., Hunsberger, E., and Eliasmith, C. (2019) · 2019
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From space to time: Spatial inhomogeneities lead to the emergence of spatiotemporal sequences in spiking neuronal networks
Spreizer, S., Aertsen, A., and Kumar, A. (2019) · 2019
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Brian 2, an intuitive and efficient neural simulator
Stimberg, M., Brette, R., and Goodman, D. F. (2019) · 2019
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Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., and Maida, A. (2019) · 2019
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Associative memory in spiking neural network form implemented on neuromorphic hardware
Hampo, M., Fan, D., Jenkins, T., DeMange, A., Westberg, S., Bihl, T., and Taha, T. (2020) · 2020
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Learning spatiotemporal signals using a recurrent spiking network that discretizes time
Maes, A., Barahona, M., and Clopath, C. (2020) · 2020
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Learning long temporal sequences in spiking networks by multiplexing neural oscillations
Vincent-Lamarre, P., Calderini, M., and Thivierge, J.-P. (2020) · 2020
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Humans predict action using grammar-like structures
Wörgötter, F., Ziaeetabar, F., Pfeiffer, S., Kaya, O., Kulvicius, T., and Tamosiunaite, M. (2020) · 2020
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