Improved Cleanup and Decoding of Fractional Power Encodings

Alicia Bremer, Jeff Orchard

Abstract:

High-dimensional vectors have been proposed as
a neural method for representing information in the brain
using Vector Symbolic Algebras (VSAs). While previous work
has explored decoding and cleaning up these vectors under
the noise that arises during computation, existing methods are
limited. Cleanup methods are essential for robust computation
within a VSA. However, cleanup methods for continuous-value
encodings are not as effective. In this paper, we present an
iterative optimization method to decode and clean up Fourier
Holographic Reduced Representation (FHRR) vectors that are
encoding continuous values. We combine composite likelihood
estimation (CLE) and maximum likelihood estimation (MLE) to
ensure convergence to the global optimum. We also demonstrate
that this method can effectively decode FHRR vectors under
different noise conditions, and show that it outperforms existing
methods.