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Introduction
Publications
Publications (7)
The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop. This paper contributes to bringing...
The endeavor to understand the brain involves multiple collaborating research fields. Classically, synaptic plasticity rules derived by theoretical neuroscientists are evaluated in isolation on pattern classification tasks. This contrasts with the biological brain which purpose is to control a body in closed-loop. This paper contributes to bringing...
While extracting spatial features from images has been studied for decades, extracting spatio-temporal features from event streams is still a young field of research. A particu-larity of event streams is that the same network architecture can be used for recognition of static objects or motions. However, it is not clear what features provide a good...
NEST is a simulator for spiking neural network models that focuses on the dynamics, size and structure of neural systems rather than on the exact morphology of individual neurons. For further information, visit http://www.nest-simulator.org. The release notes for this release are available at https://github.com/nest/nest-simulator/releases/tag/v2.1...
Spiking neural networks are in theory more computationally powerful than rate-based neural networks often used in deep learning architectures. However, unlike rate-based neural networks, it is yet unclear how to train spiking networks to solve complex problems. There are still no standard algorithms and it is preventing roboticists to use spiking n...
Background
In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian...
Background
In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian...
