In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.
Animals rely on different decision strategies when faced with ambiguous or uncertain cues.
1 code implementation • 16 Dec 2021 • Jasper Albers, Jari Pronold, Anno Christopher Kurth, Stine Brekke Vennemo, Kaveh Haghighi Mood, Alexander Patronis, Dennis Terhorst, Jakob Jordan, Susanne Kunkel, Tom Tetzlaff, Markus Diesmann, Johanna Senk
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease.
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.
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.