Neurodynamics and attractors in quantum associative memories
Integrated Computer-Aided Engineering
Fuzzy automata and neural associative memories compatible with principles of quantum computation
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Neural Processing Letters
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Nonlinear spectral associative memories are proposed as quantized frequency domain formulations of nonlinear, recurrent associative memories in which volatile network attractors are instantiated by attractor waves. In contrast to conventional associative memories, attractors encoded in the frequency domain by convolution may be viewed as volatile online inputs, rather than nonvolatile, off-line parameters. Spectral memories hold several advantages over conventional associative memories, including decoder/attractor separability and linear scalability, which make them especially well suited for digital communications. Bit patterns may be transmitted over a noisy channel in a spectral attractor and recovered at the receiver by recurrent, spectral decoding. Massive nonlocal connectivity is realized virtually, maintaining high symbol-to-bit ratios while scaling linearly with pattern dimension. For n-bit patterns, autoassociative memories achieve the highest noise immunity, whereas heteroassociative memories offer the added flexibility of achieving various code rates, or degrees of extrinsic redundancy. Due to linear scalability, high noise immunity and use of conventional building blocks, spectral associative memories hold much promise for achieving robust communication systems. Simulations are provided showing bit error rates for various degrees of decoding time, computational oversampling, and signal-to-noise ratio