Stringent Structure of NeuralDB

**Description**
The NeuralDB is currently hard-coded to have several different fields of collections that it can write to and query from. This seems like a rigid system that might be hard to change when more input fields and sensors are added. The hard-coded nature also makes it impractical for live applications

**Potential solutions**
I am not really sure yet. If the community has any ideas, they are welcome to post a comment below. One way would be to make a handler that dynamically makes new fields and functions upon request. This would involve using macro-coding or polymorphic code that updates its own methods as new fields emerge. I doubt Python can do this.

Perhaps if I had a template function that modified its parameters as new fields and collections were added.