Implemented a model of spiking neurons using a Graph Neural Network with tunable parameters. The model can be simulated over time to give training data for the inverse problem. The framework allows for both randomly drawn and custom initial connectivity. There is also support for adding external stimulation, either as a custom callable or using the RegularStimulation, SinStimulation or PoissonStimulation classes. These classes have parameters that can be tuned to increase or decrease activity in the network. There is also dependency management through conda and poetry and extensive testing of all functions and classes in the package.
Implemented a model of spiking neurons using a Graph Neural Network with tunable parameters. The model can be simulated over time to give training data for the inverse problem. The framework allows for both randomly drawn and custom initial connectivity. There is also support for adding external stimulation, either as a custom callable or using the RegularStimulation, SinStimulation or PoissonStimulation classes. These classes have parameters that can be tuned to increase or decrease activity in the network. There is also dependency management through conda and poetry and extensive testing of all functions and classes in the package.