# How to interpret neuron spiking models one comes across in literature?

## Background

I'm in high school currently conducting research (obviously it is relatively rudimentary compared to what is being done in the labs, etc.) in computational neuroscience.

I'm dealing with multiple datasets each containing time series data for a majority of neurons in C. Elegans (the dataset is the one used in Saul Kato's Global Brain Dynamics Embed the Motor Command Sequence of Caenorhabditis elegans).

I'm looking to do a pairwise comparison of each neuron in every dataset; if $N_{ij}$ is the $j^{th}$ neuron in the $i^{th}$ dataset, then I will do a pairwise comparison of all neurons in the $j^{th}$ column.

## Question

I'm going through this paper which proposes a method of comparing time series data across neurons. The paper uses this spiking model:

I'm wondering how neuroscientists approach such models they read about in literature; how they dissect it mathematically, what crosses their mind when they look at these models, etc. and where I can go to gain a rigorous foundation to truly and intuitively understand the models I read about in the future.