In this repository I will detail the code and algorithms used during the development of my thesis.
The core of the thesis is the evaluation of how the topology of a spiking neural network affects the associated activity, measured by a series of sample metrics such as Multiscale Relevance, Burstiness Coefficient, Coefficient of Local Variation, and Memory Coefficient. The results are used as basis for a simple hypothesis on the ideal subnetwork structure that allows fast and reliable responses to a wide array of inputs, varying in intesnity and quality. The code can be used and modified freely, granted citations of the work.