In order to build a reliable system it seems natural to require components that behave precisely and reliably as they should. Neuroscientists, however, know very well that neurons, the building blocks of brains, come with huge variances in their properties and that these properties also vary over time. Synapses, the connections between neurons, are known to be highly unreliable in forwarding signals: some 40%-80% percent of the time they simply ignore the incoming signal instead of forwarding it. There are many indications that this randomness in the brain is actually not a 'deficiency' that needs to be overcome, but that, quite to the contrary, it is an essential design principle. This is also our motivation: exhibiting examples of the usefulness of randomness in various aspects of neuroscience.
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions
F. Meier*, A. Mujika*, M. Gauy and A. Steger
Deep Reinforcement Learning Workshop at NeurIPS and Workshop on Optimization Foundation for Reinforcement Learning at NeurIPS, 2019
Decoupling Hierarchical Recurrent Neural Networks With Locally Computable Losses
A. Mujika*, F. Weissenberger* and A. Steger
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
F. Benzing*, M. Gauy*, A. Mujika, A. Martinsson and A. Steger
Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction
F. Weissenberger, M. Gauy, X. Zou and A. Steger
Neural Computation, 2019
A hippocampal model for behavioral time acquisition and fast bidirectional replay of spatio-temporal memory sequences
(M. Gauy., H. Einarsson, J. Lengler, F. Meier, F. Weissenberger, M. F. Yanik and A. Steger)
Frontiers in Neuroscience, Systems Biology, 2018.
On the origin of lognormal network synchrony in CA1
F. Weissenberger, H. Einarsson, M. Gauy, F. Meier, A. Mujika, J. Lengler and A. Steger
Approximating Real-Time Recurrent Learning with Random Kronecker Factors
A. Mujika, F. Meier, and A. Steger
Voltage dependence of synaptic plasticity is essential for rate based
learning with short stimuli
F. Weissenberger, M. Gauy, F. Meier, J. Lengler, H. Einarsson, and A. Steger
Scientific Reports, 2018
Fast-Slow Recurrent Neural Networks
A. Mujika, F. Meier, and A. Steger
Long synfire chains emerge by spike-timing dependent plasticity modulated by population activity
F. Weissenberger, F. Meier, J. Lengler, H. Einarsson, and A. Steger
International Journal of Neural Systems, 2017
A model of fast Hebbian spike latency normalization
H. Einarsson, M. Gauy, J. Lengler, and A. Steger
Frontiers in Computational Neuroscience, 2017
Multiassociative Memory: Recurrent Synapses Increase Storage Capacity
M. Gauy, F. Meier, and A. Steger
Neural Computation, 2017
Note on the coefficient of variations of neuronal spike trains
A. Steger and J. Lengler
Biological Cybernetics, 2017
Randomness as a Building Block for Reproducibility in Local Cortical Networks
J. Lengler and A. Steger
In Reproducibility: Principles, Problems, Practices, and Prospects, Wiley, 2016. Editors: H. Atmanspacher, S. Maasen.
Normalization phenomena in asynchronous networks
A. Karbasi, J. Lengler, and A. Steger
In Proceedings of the 42nd International Conference on Automata, Languages, and Programming (ICALP '15), 2015, 688-700.
Bootstrap Percolation with Inhibition (preprint)
H. Einarsson, J. Lengler, F. Mousset, K. Panagiotou, and A. Steger
A high-capacity model for one shot association learning in the brain
H. Einarsson, J. Lengler, and A. Steger
Frontiers in Computational Neuroscience, 07 November 2014.
Reliable neuronal systems: the importance of heterogeneity
J. Lengler, F. Jug, and A. Steger
PLOS ONE, December 2013.
Recurrent competitive networks can learn locally excitatory topologies
M. Cook, F. Jug, and A. Steger
In Proceedings of the International Joint Conference on Neural Networks (IJCNN '12), 2012, 1-8.
Interacting maps for fast visual interpretation
M. Cook, L. Gugelmann, F. Jug, C. Krautz, and A. Steger
In Proceedings of the International Joint Conference on Neural Networks (IJCNN '11), 2011, 770-776.
Neuronal Projections Can Be Sharpened by a Biologically Plausible Learning Mechanism
M. Cook, F. Jug, and C. Krautz
In Proceedings of the 21th International Conference on Artificial Neural Networks (ICANN '11)
Lecture Notes in Computer Science 6791, 2011, 101-108.
M. Cook, F. Jug, C. Krautz, A. Steger
Unsupervised Learning of Relations
In: Lecture Notes in Computer Science (ICANN 2010), 6352, 2010, 164-173.