dynamic binding

[S H Srinivasan]

There is yet another solution to dynamic binding which
I have been working on.

Conceptually we can solve the problem of dynamic binding
if we use binary *vector* of activations for each *unit*
instead of the usual binary ({0,1}) activations. Taking the
example of Graham Smith <graham at charles-cross.plymouth.ac.uk>
assume that there are four features - red, blue, square, and
triangle. Also assume that we want to represent two patterns
simultaneously. Each unit now takes activation in {0,1}^{2}
so that the activation pattern
    ( (1 0) (0 1) (1 0) (0 1))
represents "red square and blue triangle" and
    ( (0 0) (1 1) (0 1) (1 0))
represents "blue triangle and blue square".

As Ron Sun <rsun at athos.cs.ua.edu> observes:

> The point of doing dynamic binding is to be able to use the
> binding in other processing tasks, for example, high level
> reasoning, especially in rule-based (or rule-like) reasoning.
> In such reasoning, bindings are constructed and deconstructed,
> passed around, checked against some constraints, and unbound
> or rebound to something else.  Simply forming an association
> is NOT the point.

the whole point of dynamic binding is the ability to *use* it.
Using binary vector activations for units, it is possible to
do tasks like multiple content-addressable memory (MCAM) - in
which multiple patterns are retrieved simultaneously - in a
straightforward manner.

We have also looked into a (conceptual) *implementation* of the
above scheme using complex-valued activations for the units. It
is possible to represent about five objects using complex
activations. It is also possible to perform tasks like MCAM.

Finally, a question to neurobiologists: Can the existence of
multiple neurotransmitters in neurons be related to the binary
vector of activations idea?

S H Srinivasan
Center for AI & Robotics
Bangalore - 560 012, INDIA.