I studied physics at the Technical University of Darmstadt from 2011 to 2016. For the work on my master’s thesis, I joined the lab of Jochen Triesch at the FIAS Frankfurt. In my thesis, I used a self-organizing recurrent neural network to model sequence learning in primary visual cortex. Since 2017 I am part of the Neural Network Dynamics and Computation group of Raoul-Martin Memmesheimer at the University of Bonn, first as a PhD student and since 2022 as a Postdoctoral fellow.
Broadly speaking, I am interested in the computational capabilities and dynamical features of neural network models. More specifically, I am interested in learning algorithms that lie at the intersection of neuroscience and machine learning, such as gradient descent learning in spiking neural networks, perturbation-based learning (weight and node perturbation) and learning without weight changes (dynamical learning).
Publications:
C. Klos and R.-M. Memmesheimer (2023)
Smooth Exact Gradient Descent Learning in Spiking Neural Networks
arXiv:2309.14523
Link
P. Züge, C. Klos, and R.-M. Memmesheimer (2023)
Weight versus Node Perturbation Learning in Temporally Extended Tasks:
Weight Perturbation Often Performs Similarly or Better
Phys. Rev. X 13:021006
Link
Y.F. Kalle Kossio, S. Goedeke*, C. Klos*, and R.-M. Memmesheimer (2021)
Drifting assemblies for persistent memory: Neuron transitions and unsupervised compensation
Proc. Natl. Acad. Sci. USA, .
Link
C. Klos, Y.F. Kalle Kossio, S. Goedeke, A. Gilra, R.-M. Memmesheimer (2020)
Dynamical learning of dynamics
Phys. Rev. Lett. 125:088103
Link
, , , , , (2019)
Altered dynamics of canonical feed-back inhibition predicts increased burst transmission in chronic epilepsy
J. Neurosci. 2594-18.
Link
C. Klos, D. Miner and J. Triesch (2018)
Bridging structure and function: A model of sequence learning and prediction in primary visual cortex
PLoS Comput. Biol. 14 (6): e1006187.
Link
Neural network dynamics and computation 