Bipolar
spectral associative memories
- Spencer, R.G.
Dept. of Electr. Eng., Texas A&M; Univ., College Station, TX, USA
This paper appears in: Neural Networks, IEEE
Transactions on
On page(s): 463 - 474
May 2001
Volume: 12 Issue: 3
ISSN: 1045-9227
References Cited: 29
CODEN: ITNNEP
INSPEC Accession Number: 6948548
Abstract:
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.
Index Terms:
digital communication; content-addressable storage; recurrent
neural nets; decoding; encoding; bipolar spectral associative memories;
nonlinear spectral associative memories; quantized frequency domain
formulations; nonlinear recurrent associative memories; volatile
network attractors; attractor waves; volatile online inputs; linear
scalability; bit patterns; noisy channel; spectral attractor; recurrent
spectral decoding; massive nonlocal connectivity; high symbol-to-bit
ratios; autoassociative memories; noise immunity; heteroassociative
memories; robust communication systems; bit error rates; decoding time;
computational oversampling; signal-to-noise ratio