NEUROTECH event: Future Application Directions for Neuromorphic Computing Technologies: agenda and registration (free, but mandatory). A half-day event with special focus on potential application of neuromorphic computing.
Getting to the venue:
the nearest tram stop to the meeting venue is "Heidelberg Bunsengymnasium" (marked in the map linked above) [online timetable]https://reiseauskunft.bahn.de//bin/query.exe/en?Z=Neuenheim+Bunsengymnasium,+Heidelberg), provided by German Railway. Here you can also buy tickets online
Lightning talk: Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty
While there is considerable enthusiasm for the potential of spiking neural network (SNN) computing, there remains the fundamental issue of designing the topologies and parameters for these networks. We say the topology IS the algorithm. Here, we describe experiments using evolutionary computation (genetic algorithms, GAs) on a simple robotic sensory-motor decision task using a gene driven topology growth algorithm and letting the GA set all the SNN’s parameters.
We highlight lessons learned from early experiments where evolution failed to produce designs beyond what we called “cheap-tricksters”. These were simple topologies implementing decision strategies that could not satisfactorily solve tasks beyond the simplest, but were nonetheless able to outcompete more complex designs in the course of evolution. The solution involved alterations to the fitness function so as to reduce the inherent noise in the assessment of performance, adding gene driven control of the symmetry of the topology, and improving the robot sensors to provide more detailed information about its environment.
We show how some subtle variations in the topology and parameters can affect behaviors. We discuss an approach to gradually increasing the complexity of the task that can induce evolution to discover more complex designs. We conjecture that this type of approach will be important as a way to discover cognitive design principles.
J. David Schaffer (Binghamton University)
11:00-11:30 (30 min)
11:30-11:50 (20+5 min)
Natural density cortical models as benchmarks for universal neuromorphic computers
Markus Diesmann (Forschungszentrum Jülich GmbH)
11:55-12:15 (20+5 min)
Platform-Agnostic Neural Algorithm Composition using Fugu
Intel Loihi platform tutorial (Lecture style. To follow along from your own laptop your need to engage with Intel’s Intel’s Neuromorphic Research Community beforehand (email firstname.lastname@example.org for more information).