Cognitive Neuromorphic Engineering Workshop

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Winner take all behavior in continuous rate-based (dynamic neural field) and discrete spiking (neuromorphic) systems

The goal of this project is to thoroughly characterize winner-take-all (WTA) behavior in 3 different systems and find a correspondence between them. WTA behavior is an ubiquitous (canonical) motive in neural computation and is used in many projects in our group and elsewhere, however, a thorough study of those dynamical systems in a comparative context including neuromorphic hardware is lacking. We will simulate WTAs in their different modes of operation (self-sustaining, hysteresis, soft, hard, etc.) and with different dimensionality (1d-3d) using 3 different systems:

  • dynamic neural fields, which are based on the Amari equation (continuous)
  • spiking neural networks (discrete, spiking) simulated in software
  • and spiking neural networks emulated on neuromorphic hardware.

Of particular interest are the role of neuronal noise, mismatch, spontaneous activity, refractory period, synaptic delays and spike timing. Also network size and network architecture architecture including excitatory and inhibitory connectivity kernels should be considered. Optionally it might be interesting to explore the role of plasticity. Work will be done with matlab (cosivina), python based brian2 and neuromorphic chips (dynap-se). For simulations, we will make use of a python package based on Brian2 that has been developed during the last months and the chip equations. Depending on the background of the participants, the project can go in a more theoretical (mathematical) or more in a more simulation based direction.

There will be an educational and a research component of the workshop. As a main side-effect, we will also get a better understanding and intuition of what kinds of computation can be done with WTA. One important result of the workshop will be a tutorial and recipes on how to implement WTA on chip and in spiking networks.

Literature (please add)


Coombes, S., beim Graben, P., Potthast, R., & Wright, J. (Eds.). (2014). Neural fields: theory and applications. Springer.

Schöner, G., & Spencer, J. (2015). Dynamic thinking: A primer on dynamic field theory. Oxford University Press.

WTA in general

Chen, Y., McKinstry, J. L., & Edelman, G. M. (2013). Versatile networks of simulated spiking neurons displaying winner-take-all behavior. Frontiers in computational neuroscience, 7, 16.

Compte, A., Brunel, N., Goldman-Rakic, P. S., & Wang, X. J. (2000). Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cerebral Cortex, 10(9), 910-923.

Ermentrout, B. (1998). Neural networks as spatio-temporal pattern-forming systems. Rep. Prog. Phys, 61, 353-430.

Laing, C. R., & Chow, C. C. (2001). Stationary bumps in networks of spiking neurons. Neural computation, 13(7), 1473-1494.

Lumer, E. D. (2000). Effects of spike timing on winner-take-all competition in model cortical circuits. Neural computation, 12(1), 181-194.

Mao, Z. H., & Massaquoi, S. G. (2007). Dynamics of winner-take-all competition in recurrent neural networks with lateral inhibition. IEEE transactions on neural networks, 18(1), 55-69.

Maass, W. (2000). On the computational power of winner-take-all. Neural computation, 12(11), 2519-2535.


Rutishauser, U., Douglas, R. J., & Slotine, J. J. (2011). Collective stability of networks of winner-take-all circuits. Neural computation, 23(3), 735-773.


Quinton, J. C. (2010, July). Exploring and optimizing dynamic neural fields parameters using genetic algorithms. In Neural Networks (IJCNN), The 2010 International Joint Conference on(pp. 1-7). IEEE.


Indiveri, G., Horiuchi, T., Niebur, E., & Douglas, R. (2001, September). A competitive network of spiking VLSI neurons. In World Congress on Neuroinformatics (pp. 443-455). Vienna, Austria: ARGESIM/ASIM Verlag.

Oster, M., Douglas, R., & Liu, S. C. (2009). Computation with spikes in a winner-take-all network. Neural computation, 21(9), 2437-2465.


Meijer, H. G., & Coombes, S. (2014). Travelling waves in a neural field model with refractoriness. Journal of mathematical biology, 68(5), 1249-1268.


S Coombes and M R Owen. Bumps, breathers, and waves in a neural network with spike frequency adaptation, Physical Review Letters, 94, 148102, 2005.

Other resources
cc18/winner-take-all-behavior-in-continuous-rate-based-and-discrete-spiking-systems/overview.1524609652.txt.gz · Last modified: 2020/01/09 20:31 (external edit)