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Found 6 papers, 2 papers with code
Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices
Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory.
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.
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.
Connectivity Concepts in Neuronal Network Modeling
We hope that the proposed standardizations will contribute to unambiguous descriptions and reproducible implementations of neuronal network connectivity in computational neuroscience.
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.
Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space
Based on model predictions of spiking activity and LFPs, we find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental data recorded in the sensory cortex.
