Introduction

Introduction:

Purpose:

This code is designed to allow a user to build and simulate any neural networks, without needing to know how to code themselves. While the code is specifically designed to be used to model the olfactory system, it is versatile enough to model any neural network the user can build. The intention is for the user to computationally simulate neural pathways and make adjustments accordingly in order to gain insight towards biological systems.

The way it works:

The code has three main stages.

• Building: Fist is the Building stage, where the code reads the users specifications and constructs a model in accordance with them. The code can build neurons (or groups of neurons), and synapses (or groups of synapses, called connections). The neurons form the "backbone" or "structure" of the model, and the synapses connect different parts of the model together so that they can interact with one another.
• Simulating: Once the model is built, the code will begin the simulation. The user has little control over this stage, aside from the model itself. The simulation uses a time-step system. That is, it computes changes over small periods in time, called a time-step, and repeats this process for multiple time-steps. The simulation will run for a number of time-steps decided by the user.
• Analyzing: Once the simulation has finished, The user has the chance to analyze the state of the model during the simulation. The user can plot or save a lot of different data from the model. Some examples include: The voltages for each neuron over time, when each neuron fired, as well as how the weights of synapses changed over time. The user can also apply several functions to this data, such as computing means or power spectral densities. Using these tools they can understand what happened to the model during the simulation.