Relational Networks for goal-directed sensory-motor task
We will build a (relatively) simple spiking neuronal network implementing a goal-directed state-to-action mapping (SAG) unit. As a core architecture we will use a three-way relation network proposed by . However, we would like to translate the rate-coding scheme into spike time coding scheme to exploit spatio-temporal sparsity of the network. The aim of this workshop is to implement and test a "hard-wired" version of this relational network on a neuromorphic processor (e.g. the DYNAP-SE chip), encoding behavior (i.e. mapping the Stimulus position to the Pointer position) of an "agent" for various goals: (a) following the stimulus, (b) avoiding the stimulus, (c) keeping a fixed distance to the stimulus.
 P. U. Diehl and M. Cook, “Learning and inferring relations in cortical networks”, arXiv preprint, arXiv:1608.08267, 2016.
|Tue, 24.04.2018||22:00 - 23:00||Lecture Room|
|Wed, 25.04.2018||21:00 - 22:00||Lab / Disco|
|Thu, 26.04.2018||22:00 - 23:00||Lecture Room|
|Tue, 01.05.2018||10:00 - 12:00||Lab|