sdm.ai

We’re working on a book on Sparse Distributed Memory.

Desiderata:

A massively parallel framework in which users can test cognitive neuroscience theories;
a full documentation of the framework;
An open-source book, with a visual tutorial of the system, its relation to neuroscience and psychology, and numerous new scientific results;
A collection of SDM resources, links, chronology of papers, etc;
Slides following the Jupyter notebooks used;
A video-based mini-course on SDM.

Sparse Distributed Memory Framework

This is a framework which can be adapted to any usage of a Sparse Distributed Memory (Kanerva, 1988): new theoretical results, new applications, new machine learning models, etc.

We have been working on Pentii Kanerva’s Sparse Distributed Memory:

The first paper considered the range of dimensions that an SDM should have were it to respect i) chunking-by-averaging, and ii) the “magic number 7”;
a second paper studied the critical distance as the memory becomes saturated;
a third paper (underway) studies interaction effects between different attractors, and
a fourth paper will document this highly-palallel SDM framework developed by Dr. Marcelo Brogliato.

We would really like to ask you for feedback, and, should you find it useful, please consider a link or citation:

Brogliato, M.S.; Linhares, A. (2017) Sparse Distributed Memory: a reference implementation. Working Paper, FGV, Vialink.

How to build & test

To generate the library and run some tests:

cd src/
make
make tests
./test1

To run Python tests:

python tests.py

In dev mode, there should be a symbolic link from src/libsdm.so to sdm/_libsdm.so. If it does not exist, create one running:

cd sdm/
ln -s ../src/libsdm.so _libsdm.so

How to install

This framework has the following dependencies: libbsd and libOpenCL.