Connectionists: a new mechanism for immediate nearest-neighbor memory access (for both storage and retrieval)

[gerard rinkus]

Dear Connectionists,

 

I would like to announce publication of a new paper "A cortical sparse
distributed coding model linking mini- and macrocolumn-scale functionality"
describing a new computational model of cortex.  The article
(http://frontiersin.org/neuroscience/neuroanatomy/paper/10.3389/fnana.2010.0
0017/) is published in Frontiers in Neuroanatomy.  Its primary focus is to
offer a hypothesis as to the functions and relations of the minicolumn and
macrocolumn.  However, its core neural processing algorithm should be of
more general interest to the computational community because it is a novel
mechanism for doing immediate (i.e., no sequential search), nearest-neighbor
access of stored memories.  Thus, it is an alternative to other recent
methods for achieving this goal, e.g., semantic hashing and
locality-sensitive hashing.

 

The abstract:

 

No generic function for the minicolumn-i.e., one that would apply equally
well to all cortical areas and species-has yet been proposed.  I propose
that the minicolumn does have a generic functionality, which only becomes
clear when seen in the context of the function of the higher-level,
subsuming unit, the macrocolumn.  I propose that: a) a macrocolumn's
function is to store sparse distributed representations of its inputs and to
be a recognizer of those inputs; and b) the generic function of the
minicolumn is to enforce macrocolumnar code sparseness.  The minicolumn,
defined here as a physically localized pool of ~20 L2/3 pyramidals, does
this by acting as a winner-take-all (WTA) competitive module, implying that
macrocolumnar codes consist of ~70 active L2/3 cells, assuming ~70
minicolumns per macrocolumn.  I describe an algorithm for activating these
codes during both learning and retrievals, which causes more similar inputs
to map to more highly intersecting codes, a property which yields ultra-fast
(immediate, first-shot) storage and retrieval.  The algorithm achieves this
by adding an amount of randomness (noise) into the code selection process,
which is inversely proportional to an input's familiarity.  I propose a
possible mapping of the algorithm onto cortical circuitry, and adduce
evidence for a neuromodulatory implementation of this familiarity-contingent
noise mechanism.  The model is distinguished from other recent columnar
cortical circuit models in proposing a generic minicolumnar function in
which a group of cells within the minicolumn, the L2/3 pyramidals, compete
(WTA) to be part of the sparse distributed macrocolumnar code.

 

Sincerely,

Gerard Rinkus

 

Gerard Rinkus, PhD

President, 

Neurithmic Systems

1647 Beacon St., Suite 4

Newton, MA 02468

617-997-6272

 

Visiting Scientist, Lisman Lab

Volen Center for Complex Systems

Brandeis University, Waltham, MA

http://people.brandeis.edu/~grinkus/

 

 

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