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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:
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.
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