Posting FYI - I have tested this out and it is quite robust

Fri May 31 16:51:03 EDT 1991

PRESS RELEASE 
AND CORPORATION INTRODUCES FIRST HOLOGRAPHICALLY BASED 
NEUROCOMPUTING SYSTEM

AND CORPORATION
4 Hughson St. Suite 305
Hamilton, Ontario
Canada
L8N 3Z1
phone	(416) 570 0525
fax	(416) 570 0498

AND Corporation based in Canada has developed a new technology related 
to the current field of artificial neural systems.  This new field is referred to 
as holographic neural technology. 

The operational basis stems from holographic principles in the superposition 
or "enfolding" of information by convolution of complex vectors.  An 
analogous, albeit far more limited, process occurs within interacting 
electromagnetic fields in the generation of optical holograms.  Information 
as dealt with in the neural system represents analog stimulus-response 
patterns and the holographic process permits one to superimpose or enfold  
very large numbers of such patterns or analog mappings onto a single 
neuron cell.  Analog stimulus-response associations are learned in one non-
iterative transformation on exposing the neuron cell to a stimulus and 
desired response data field.  Similarly, decoding or expression of a response 
is performed in one non-iterative transformation. The process exhibits the 
property of non-disturbance whereby previously learned mappings are 
minimally corrupted or influenced by subsequent learning.

During learning of associations, the holographic neural process generates a 
true deterministic mapping of analog stimulus input to desired analog 
response.  Large sets of such stimulus-response mappings are enfolded onto 
the identically same correlation set (array of complex vectors) and may be 
controlled in such a manner that these mappings have modifiable properties 
of generalization.  Specifically, this generalization characteristic refers to a 
stimulus-response mapping in which input states circumscribed within a 
modifiable region of the stimulus locus will accurately regenerate the 
associated analog response. In addition, neuron cells may be configured to 
exhibit a range of dynamic memory profiles extending from short  to long 
term memory.  This feature applies variable decay within the correlation sets 
whereby encoded stimulus-response mappings may be attenuated at 
controlled rates.  A single neuron cell employing the holographic principle 
displays vastly increased capabilities over much larger ANS networks 
employing standard gradient descent methods.

AND Corporation has constructed an applications development system based 
on this new technology it has called the HNeT system (for Holographic 
NeTwork). This system executes within an INMOS transputer hardware 
platform. The HNeT development system permits the user to design an entire 
neural configuration which remains resident and executes within an INMOS 
transputer based co-processing board. The neural  engine executes 
concurrently with the host resident application program, the host processor 
providing essentially I/O and operator interface services.  Internally the 
neural engine is structured by the user as a configuration of cells having 
data field flow paths established between these cells. Data fields within the 
holographic system are structured as matrices of complex values 
representing analog stimulus and response data fields.  The manner in 
which data sets, normally expressed as real-numbered values in the external 
domain, are mapped to complex data fields within the neural engine is not 
of necessary importance to the neural system designer as data transfer 
functions provided within the HNeT development system perform these data 
field conversions automatically. 

An extensive set of 'C' neural library routines provide the user flexibility in 
configuring up to 16K cells per transputer and 64K synaptic inputs per cell 
(memory limited).  Functions for configuring these cells within the neural 
engine are provided within the HNeT library, and may be grouped into the 
following general categories:

Neural cells - These cell types form the principle operational component 
within the neural engine, employing the holographic neural process for 
single pass encoding (learning) and decoding (expression) of analog 
stimulus-response associations.  These cells generate the correlation sets or 
matrices which store the enfolded stimulus-response mappings.

Operator cells - Cells may also be configured within the neural engine to 
perform a wide variety of complex vector transform operations and data 
manipulation over data fields.  These cells principally perform preprocessing 
operations on data fields fed into neural cells.

Input cells - These cells operate essentially as buffers within the neural 
engine for data transferred between the host application program and the 
transputer resident neural configuration.  This category of cells may also be 
used to facilitate recurrent data flow structures within the neural engine.

In configuring the neural engine the user has the flexibility of constructing a 
wide range of cell types within any definable configuration, and specifying 
any data flow path between these diverse cell types.  Configuration of the 
neural engine is simple and straightforward using the programming 
convention provided for the HNeT neural development system.  For instance, 
a powerful configuration comprised of two inputs cells and one neural cell 
(cortex), capable of encoding large numbers of analog stimulus- response 
mappings (potentially >> 64K mappings), can be configured using three 
function calls . i.e.

			stim	 = receptor(256,255);
			des_resp = buffer(1,1);
			output	 = cortex(des_resp, stim, ENDLIST);

The above 'C' application code configures one cortex cell type within the 
neural engine receiving a stimulus field of 256 by 255 elements (stim), and 
returns a label to the output  data field containing the generated response 
value (output) for that cell.  This configuration may be set into operation 
using an execute instruction to perform either decoding only, or both 
decoding/encoding functions concurrently.  The cortex cell has defined 
within its function variable list two input data fields, that is the stimulus 
field (stim) and the desired response data field (des_resp).  On encoding the 
stimulus-to-desired-response association, an analog mapping is generated 
employing holographic neural principles, and this mapping enfolded onto 
the cortex cells correlation set.  On a decoding cycle, the cortex cell 
generates the response from a stimulus field transformed through its 
correlation set, returning a label to that data field (output).  An entire 
neural configuration may be constructed using the above convention of 
function calls to configure various cells and establishing data flow paths via 
labels returned by the configuration functions.

The users application program performs principally I/O of stimulus-response 
data fields to the neural engine and establishes control over execution 
cycles for both learning and expression (response recall) operations.  The 
transputer resident neural engine independently performs all the transform 
operations and data field transfers for the established neural configuration.  
The host IBM resources may be fully allocated to ancillary tasks such as 
peripheral and console interface, retrieval/storage of data, etc.  This format 
allows maximum concurrency of operation.

For the control engineer, the holographic neural system provides a new and 
powerful process whereby input states may be deterministically mapped to 
control output states over the extent of the control input or state space 
domain.  The mapping of these analog control states is generated simply by 
training the neural engine.  Realizing the property of non-disturbance 
exhibited by the holographic process, the neural configuration may be 
constructed to learn large sets of spacio-temporal patterns useful in robotics 
applications. The neural system designer may explicitly control and modify 
the generalization characteristics and resolution of this mapping space 
through design and modification of higher order statistics used within the 
system.  In other words, stimulus-response control states are mapped out 
within the cell, and allowing the user to explicitly define the higher order 
characteristics or generalization properties of the neural system.  Control 
states are encoded simply by presenting the neural engine with the suite of 
analog stimulus-response control actions to be learned.

This technology is patent pending in North and South America, Europe, 
Britan, Asia, Australia and the USSR. The HNeT applications development 
system is commercially available for single transputer platforms  at a base 
price of  $7,450.00 US. For further information call or write to AND 
Corporation, address given above.