Performance and Algorithmic Optimization of a Novel Simulator for Spiking Neural Networks [Master Thesis – Theme: BrainFrame]

One of the most difficult challenges in Computational Neuroscience is to find adequate mathematical tools to simulate detailed biophysical models of neuronal networks accurately and fast. The computational demands of such simulations are huge and more so when we consider the problem size: thousands to millions of neurons connected via up to billions of synapses. In the NeuroComputing Lab (NCL) of the Erasmus Medical Center, Rotterdaml, we have developed the fastest-in-existence simulator engine that is flexible enough to simulate large-scale spiking neural networks (SNNs), named EDEN. “Fastest” isn’t even close enough to simulate the smallest part of a human brain, nor is it close enough to simulate even small neuronal networks at real-time speeds. EDEN is the product of a decade of research in the NeuroComputing Lab.

The subject of this MSc thesis topic is to do a performance analysis on EDEN and explore/ develop new algorithms and or data-structures to optimize the simulation performance without the loss of any functionality. Working on EDEN will directly challenge the established simulators in the community, such as NEST, NEURON and BRIAN2. This topic is part of the BrainFrame jointly developed by the Erasmus Medical Center and the Delft University of Technology.

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Keywords: SNN, simulator, parallel programming, hardware acceleration

Prerequisites: strong C/C++ background, algorithms, parallel programming MPI / openMP, Linux.
Optionally: Docker containers, jupyter notebooks.

Contact: Christos Strydis

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