Winner take all behavior in continuous rate-based and discrete spiking 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. 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.
One result of the workshop should be a tutorial and recipes on how to implement WTA on chip and in spiking networks.
|Wed, 25.04.2018||14:00 - 15:00||sea entrance of the lab|
|Thu, 26.04.2018||09:00 - 10:00||sea entrance of the lab|
|Fri, 27.04.2018||15:00 - 17:00||lab|
|Wed, 02.05.2018||14:00 - 15:00||lecture room|