Search Results for author:
Found 6 papers, 1 papers with code
Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
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.
Sequence learning, prediction, and replay in networks of spiking neurons
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.
A Modular Workflow for Performance Benchmarking of Neuronal Network Simulations
Modern computational neuroscience strives to develop complex network models to explain dynamics and function of brains in health and disease.
Coherent noise enables probabilistic sequence replay in spiking neuronal networks
Animals rely on different decision strategies when faced with ambiguous or uncertain cues.
Sequence learning in a spiking neuronal network with memristive synapses
In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.
Phenomenological modeling of diverse and heterogeneous synaptic dynamics at natural density
This chapter sheds light on the synaptic organization of the brain from the perspective of computational neuroscience.
