Publications by authors named "Marius Vieth"
Cortical networks are capable of unsupervised learning and spontaneous replay of complex temporal sequences. Endowing artificial spiking neural networks with similar learning abilities remains a challenge. In particular, it is unresolved how different plasticity rules can contribute to both learning and the maintenance of network stability during learning.
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- Spiking neural network simulations are key in fields like Computational Neuroscience, AI, and Neuromorphic Engineering, with various simulators available for different applications.
- PymoNNto is a new Python toolbox for spiking neural networks that allows users to embed custom code flexibly, operating with a NumPy backend and GPU support.
- PymoNNtorch builds on this by using PyTorch for better performance, showing faster results than traditional simulators like NEST and Brian 2 through its optimized GPU capabilities and modular design.
PymoNNto: A Flexible Modular Toolbox for Designing Brain-Inspired Neural Networks.
Front Neuroinform
November 2021
The Python Modular Neural Network Toolbox (PymoNNto) provides a versatile and adaptable Python-based framework to develop and investigate brain-inspired neural networks. In contrast to other commonly used simulators such as Brian2 and NEST, PymoNNto imposes only minimal restrictions for implementation and execution. The basic structure of PymoNNto consists of one network class with several neuron- and synapse-groups.
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