Jeff Hawkin's book is available online for reading.

The book http://www.scribd.com/doc/2942162/HawkinsJeff-On-Intelligence introduces the neocortical model that inspires this project. It can also be downloaded.
It is a boon for searching.

This should be the last main release of the version 1.4 series.
It tidies up afew user interface items and also has the facilty to visualise training saccades on the main window.

A brief introduction is on a previous recent posting, below.
The user guide is in the folder with the download on SkyDrive:



Note - the download is self contained - you need not install any other software for it to run.

Thinking about how to achieve temporal pooling is quite hard and I think further tinkering is just on the level
of procrastination!

If anyone should ever look at this please let me know how you get on with it!!

Finally I have it working with MinGW.
The recommended procedure is to use the bundled QT/MinGW from the Trolltech/Nokia site!
See:
http://trolltech.com/downloads/opensource/appdev/windows-cpp

The QT .pro files and full instructions are in the version 1.4.2 Neo folder (see previous entry).

The files at the folder link below have been updated for version, 1.4.2c above.
14th December 2008.

Updated 17th November 2008.
I've put a new version into the folder and called it 1.4.2b.  The source is on SourceForge but I have not yet
published to Source Forge this archive.
The new version is a lot faster.  Thanks to Greg Kochaniak for the suggestions (to Elite).

Original entry 16th November 2008.
This version has the following main fixes and enhancements:
1.  Out of range saccades fixed (Originally version 1.4A)
2.  Fixed menus.  These no longer worked in version 1.4.
3.  The current directory was not always retained.
4.  Hover help on Parameters dialog (Hold mouse over ? boxes)
5.  Visualise training saccades
More explanation and notes to follow

Introduction

[Removed superseeded downloads 16/11/2008]
Artificial Intelligence has a strong element of pattern recognition.

The two basic methods of pattern recognition are:

• Explicit analysis of the patterns using pre-defined specific algorithms.
• Learning from sets of patterns presented to the system using a general algorithm.

It is the latter approach that Neocortex and similar systems use to recognise patterns.
The aim of using the learning approach is that the AI system can act autonomously and learn in the way that an animal might learn.
The name ‘Neocortex’ is meant to indicate a biologically inspired model although this is to be taken as a metaphor and not literally.
Neocortex is not a direct analogue of the brain, but an approximation of the unified cortical algorithm as described by Hawkins and Blakeslee [1].
Neocortex models the function of some of the key large scale structures of the real neocortex of mammals.
In other words we do not model individual neurons or cortical columns, but rather work on the level of, say, the V1, V2, and V4 regions
of the visual cortex as well as hippocampus.
There is nothing restrictive about this approach. It provides a superstructure into which any detailed processing methods can be placed.

The current status of the model is of a training and research tool.
It allows you to explore the effect of varying certain important aspects of the model such as the number of levels (e.g. thinking of V1, V2 etc. regions),
the size of each level and how one level maps onto another (the model is a hierarchy among levels).

Please read the previous entries here for a list of changes since earlier versions if interested.
[Removed superseded information about this download]

[1] Jeff Hawkins, Sandra Blakeslee “On Intelligence” Henry Holt and Company, 2004