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 [1]. 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.

[1] P. U. Diehl and M. Cook, “Learning and inferring relations in cortical networks”, arXiv preprint, arXiv:1608.08267, 2016.

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Day Time Location
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


Moritz Milde
Nicoletta Risi
Dmitrii Zendrikov


Abhishek Banerjee
chama bensmail
Thomas Dalgaty
Ismael Tito Freire González
Giacomo Indiveri
aamir khan
Brent Komer
Renate Krause
Alpha Renner
Nicoletta Risi
Sergio Solinas
Juan Camilo Vasquez Tieck
Jayawan Wijekoon
Dmitrii Zendrikov