Neurotune Architecture

Neurotune provides three important classes: Optimizers, Evaluators and Controllers. Any optimization of a model must utilise (usually) one instance of each of these classes.

Optimizer Class

The optimizer controls the progress of the optimization - the mating, survival and death of individuals (each individaul in this case being a simulation). The optimizer generally acheives this by starting with an initial population chosen randomly (Say a population of 100, with 3 free paramaters x,y,z, the population is therefore 100 members [x1,y1,z1]. . . [x100,y100,z100]). Seeds chosen by the user may also be present in the initial population. In order to evaluate this population, the optimizer passes every member to the evaluator which assigns each indivudal a fitness value based on the simulation output.

Evaluator Class

But how does the Evaluator do this? The evaluator does the above by looking at simulation output and targets and assigning a fitness (via its cost function). The evaluator needs a way to run each simulation - this is why it needs a controller. Hence there are things like ClampEvalutor - This evaluator specifially evaluates the finess of a current clamp simulation compared to the exeperimental data. - It passes those finess results back to the optimizer, the optimizer doesn’t need to “know”“what kind of simulation is being run to obtain these finesses.

Controller Class

The controller class implements the actual running of the simulation given the parameters. A controller will provide the evaluator with a run() method and hide the details of the simulation implementation from the evaluator.


Why this architecture?

This architecture decouples the optimization from the actual assigining of fitness and the actual control of a simulation. The ability easily mix and match optimizers, controllers and evaluators gives great exibility. For in- stance, the CLI Controller used in the musclemodel doesn’t need to be a Python script- it just needs to be a program which can accept a command line argument and run a simulation based on it.

What is a dumb evaluator?

Most evaluators work by receiving a trace back from the simulation and assessing its fitness. A dumb evalutor is a bit special - The simulation itself reports a fitness and writes it to a file. The evaluator is dumb because all it needs to do is read this finess value and report that back to the Optimizer. This allows a lot of flexibility (If a user finds that the given evaluators don’t match their needs but doesn’t want the hassle of writing their own evaluator (e.g doesn’t feel comfortable with OOP) they don’t have to).