Search Results for author: Tom Tetzlaff

Found 5 papers, 1 papers with code

Sequence learning in a spiking neuronal network with memristive synapses

no code implementations29 Nov 2022 Younes Bouhadjar, Sebastian Siegel, Tom Tetzlaff, Markus Diesmann, Rainer Waser, Dirk J. Wouters

In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.

Self-Learning

Sequence learning, prediction, and replay in networks of spiking neurons

no code implementations5 Nov 2021 Younes Bouhadjar, Dirk J. Wouters, Markus Diesmann, Tom Tetzlaff

These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences.

Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution

no code implementations11 May 2021 Stefan Dasbach, Tom Tetzlaff, Markus Diesmann, Johanna Senk

For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution.

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