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

Background: One of the most difficult challenges in Computational Neuroscience is to find adequate mathematics 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. So far, 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.

Thesis goal: The subject of this 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.

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 (, Said Hamdioui (, Sotiris Panagiotou (

Miscellaneous: This is topic offered jointly by the Erasmus Medical Center (Neuroscience department) and the Delft University of Technology (Quantum & Computer Engineering department). It capitalizes on the Convergence between the two universities and offers dual working locations (Rotterdam, Delft), access to extended resources and a truly interdisciplinary environment for conducting research.

Job Category: MSc Thesis topic
Job Type: Student Thesis

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