We describe a simple Hadamard design for neural architecture with an equal number of input and output elements
that is both error-tolerant and robust to missing information. The design provides a basis for calculation using a
classification scheme based on the Chinese remainder theorem, producing an abstract representation of the physical world.
The underlying co-prime arrays can be generated in a simple manner biologically and can evolve into more complex designs.
The approach differs from previously described neural network constructions in that all connectivity is specified by
design, with each correctly wired array producing a single output for each subset of inputs. The wiring is consistent with
the “On-Off” schema observed for different senses because only about half the inputs can be active at any one time. The
arrays can be tuned through by varying the number of simultaneous inputs required for activation within a range specified
by the array size. The architecture is scalable.