I've had an interest (as one can see in my other posts) in a wide range of distributed processing and parallel computing approaches and while not seen in any of my posts machine learning approaches as well. I looked at neural networks some years ago, and while they didn't suit the problems I worked on at the time I remembered the article Duncan and Tweney wrote as useful. A couple of others might also prove useful.
Three references follow:
AI AND STATISTICAL APPLICATIONS
Mathematica: A flexible design environment for neural networks
From the Journal:
Behavior Research Methods, Instruments, & Computers 1997,29 (2). 194-199
From 1997, a few years more recent than Freeman's.
Freely available as a pdf article:
Several neural networks were developed in Mathematica in order to
explore the role of "spiky" neurons in neural network memory
simulations. Using Mathematica for this task confirmed its value as a
powerful tool for neural network development: It exhibited distinct
advantages over other environments in programming ease, flexibility
of data structures, and the graphical assessment of network
One of its authors: Sean C. Duncan has moved from Bowling Green University to Miami University. He has a website: http://se4n.org/
Its other author: Ryan Tweney remains at Bowling Green University and has his own website: http://personal.bgsu.edu/~tweney/
You can find contact information for each of them on their respective websites. I've always found academics generous with what they know. The article or contacting them might lead you to better sources of information on this.
Mathematica Neural Networks package.
You can download the pdf of the manual for the Mathematica Neural Networks package. Pretty extensive, indeed.
The Power of Neural Networks
A review in which Brian Cogan briefly assesses NeuroSolutions from NeuroDimension, and Neural Networks, a Mathematica add-on, from Scientific Computing World March/April 2003