Overview
This chapter aims to provide an overview of the main features of NuStudio which includes a brief explanation of how the features are organized in the main window.
This the main window of NuStudio:
Except the menu and tool bar, all window tools can be customized according to your taste. This means that you can move, resize, or hide any window tool.
Let's look at some tools...
This tool is certainly the essence of NuStudio. With it you will be able to model a hierarchy of regions as proposed by the HTM theory or simply select a node to see its details. A hierarchy is composed of regions and sensors:
- Region: it is represented by a trapezium and the same must have at least one lower region or sensor to feed it
- Sensor: it is represented by a rectangle and its only role is feeding a region with inputs that are accepted by this, i.e. an fixed array composed by 0's and 1's. For this, it will collect input data from a file or a database. This data may be converted values (i.e. the array of 0's and 1's ready to be fed) or raw values (i.e. natural values like numbers, string, etc, which must be converted to an array using an encoder).
The essence of the tool bar is to provide commands to initialize the simulation of a neural network and follow its changes with time. It also provides a slider bar which allows you to see its previous state in a time T, T-1, etc. This way you can check if the columns that were predicted are indeed currently active; what synapses were created or removed since the last iterations, etc.
When you initialize the simulation, this tool shows a 3D representation of the neural network. Using the mouse or keyboard you will be able to change camera position, hide or show a given elements (cells, synapses, etc) and their states (active, inactive, etc), and still save your customized view for future use.
This tool allows you to see the details of a region or sensor when you click on it. In the case of regions, these details usually include its statistics and a list of elements like columns, cells, etc, and their states and statistics. In case of sensors, these details include the original value of the input (i.e. the value that was converted by an encoder) and a list of bits and their states and statistics. Furthermore, when you select an element in the list, its respective representation on 3D Simulation is highlighted (in blue color).
This tool shows a report of the simulation as you interact through time. It can be useful for tuning your neural network and to test what parameters produce better results of performance and accuracy, for example.
Next topic: Simulation