Simulated dataset on coordinated reset stimulation of homogeneous and inhomogeneous networks of excitatory leaky integrate-and-fire neurons with spike-timing-dependent plasticity

Description

We present simulated data on coordinated reset stimulation (CRS) of plastic neuronal networks. The neuronal network consists of excitatory leaky integrate-and-fire neurons and plasticity is implemented as spike-timing-dependent plasticity (STDP). A synchronized state with strong synaptic connectivity and a desynchronized state with weak synaptic connectivity coexist. CRS may drive the network from the synchronized state into a desynchronized state inducing long-lasting desynchronization effects that persist after cessation of stimulation. This is used to model brain stimulation-induced transitions between a pathological state, with abnormally strong neuronal synchrony, and a physiological state, e.g., in Parkinson’s disease. During CRS, a sequence of stimuli is delivered to multiple stimulation sites – called CR sequence. We present simulated data for the analysis of long-lasting desynchronization effects of CRS with shuffled CR sequences versus non-shuffled CR sequences in which the order of stimulus deliveries to the sites remains unchanged throughout the entire stimulation period. We provide data on long-lasting desynchronization effects of shuffled and non-shuffled CRS in networks with homogeneous synaptic connectivity and networks with inhomogeneous synaptic connectivity. Homogeneous synaptic connectivity refers to a network in which the probability of a synaptic connection does not depend on the pre- and postsynaptic neurons’ locations. In contrast, inhomogeneous synaptic connectivity refers to a network in which the probability for a synaptic connection depends on the neurons’ locations. The presented neuronal network model was used to analyze the impact of the CR sequences and their shuffling on the long-lasting effects of CRS [1]. More details can be found in Ref. [2]. [1] Kromer, J. A., and Tass, P. A. (2024). Sequences and their shuffling may crucially impact coordinated reset stimulation – a theoretical study. Brain Stimulation 17, P194–P196. [2] Kromer, J.A., and Tass, P.A. (2024). Simulated dataset on coordinated reset stimulation of homogeneous and inhomogeneous networks of excitatory leaky integrate-and-fire neurons with spike-timing-dependent plasticity. Data in Brief 54, 110345. Note: Version 2 includes the following changes: 1) References [1] and [2] added. 2) run_sim/3_run.py "data" -> dataDirectory in line 159 3) Corrections related to the stimulation amplitude of non-shuffled CRS. In version 2, non-shuffled and shuffled CRS have the same stimulation amplitude for the same value of Astim. The following files have been updated accordingly: - CRS/CRS_non_shuffled.py - figures/Fig2/Figure_2.png - figures/Fig2/data/dic_mean_Weights_nonShuffled_homogeneous.pickle - figures/Fig2/data/dic_mean_Weights_nonShuffled_inhomogeneous.pickle

Steps to reproduce

Simulations of the network of leaky integrate-and-fire neurons can be performed using the Python scripts in folder "run_sim". These scripts generate shell commands for running simulations of network dynamics in the synchronized state ("run_sim/1_run.py"), simulations for networks with different mean synaptic weights at t=0, to analyze the coexistence of desynchronized and synchronized states ("run_sim/2_run.py"), and simulations of coordinated reset stimulation of inhomogeneous and homogeneous ("run_sim/3_run.py"), as well as intermediate networks ("run_sim/4_run.py"). The figures in folder "figures" show data on the coexistence of synchronized and desynchronized states in inhomogeneous and homogeneous networks ("figures/Fig1") and data on long-lasting effects of non-shuffled and shuffled coordinated reset stimulation ("figure/Fig2"). Python scripts with which these figures can be obtained from the output of simulations can be found in the respective figure folders. Simulations were performed using Stanford's Sherlock Computing cluster and Python 2.7.16. Further instruction can be found in the IPython script "main.ipynb". A detailed description of the dataset can be found in Ref. [2].

Institutions

Categories

Funding

Licence