they continue to move beyond approaches based solely in classic brain theory. correlograph in 1969 scientists d. wilshaw, o. p. buneman and h. longuet-higgins proposed an alternative, non-holographic model that fulfilled many of the same requirements as gabor's original holographic model. the gabor model did not explain how the brain could use fourier analysis on incoming signals or how it would deal with the low signal-noise ratio in reconstructed memories. longuet-higgin's correlograph model built on the idea that any system could perform the same functions as a fourier holograph if it could correlate pairs of patterns. it uses minute pinholes that do not produce diffraction patterns to create a similar reconstruction as that in fourier holography. like a hologram, a discrete correlograph can recognize displaced patterns and store information in a parallel and non-local way so it usually will not be destroyed by localized damage. they then expanded the model beyond the correlograph to an associative net where the points become parallel lines arranged in a grid. horizontal lines represent axons of input neurons while vertical lines represent output neurons. each intersection represents a modifiable synapse. though this cannot recognize displaced patterns, it has a greater potential storage capacity. this was not necessarily meant to show how the brain is organized, but instead to show the possibility of improving on gabor's original model. one property of the associative net that makes it attractive as a neural model is that good retrieval can be obtained even when some of the storage elements are damaged or when some of the components of the address are incorrect. p. van heerden countered this model by demonstrating mathematically that the signal-noise ratio of a hologram could reach 50% of ideal. he also used a model with a 2d neural hologram network for fast searching imposed upon a 3d network for large storage capacity. a key quality of this model was its flexibility to change the orientation and fix distortions of stored information, which is important for our ability to recognize an object as the same entity from different angles and positions, something the correlograph and association network models lack.