Tag: htm.core

10. June 2020 .NET Core, Artificial Intelligence, C#, Hierarchical Temporal Memory, Learning

htm.core and C#

In case you weren’t aware, htm.core now also provides a REST interface to it’s Network API. With this in place, it is possible to utilize the built-in encoders, Spatial Pooler and Temporal Memory from almost any programming language. In this post I briefly want to touch on how to use htm.core and C# together.

Introduction

Before you can start playing around with the REST interface, you will need to have the example REST server running.

To do that, you can either

  • download the source and build it yourself. Once built, the server executable (rest_server) should
… more
26. April 2020 Artificial Intelligence, Big Data, Hierarchical Temporal Memory, Learning

htm.core parameters – High Order Sequence Memory

In part 2, I used htm.core as a single order sequence memory by allowing only one cell per mini-column. In this post I’ll finally have a first look at the high order sequence memory.

Before we do that, I want to show you one last single order memory example however.

Single Order Sequence Memory Recap

As you might remember from the last post, these were the settings for our htm.core temporal memory (aka sequence memory).

    columns = 8
    inputSDR = SDR( columns )
    cellsPerColumn = 1
    tm = TM(columnDimensions          = (inputSDR.size,),
            cellsPerColumn            = cellsPerColumn,
… more
8. April 2020 Artificial Intelligence, Big Data, Development, Hierarchical Temporal Memory, Learning, Python

htm.core parameters – Single Order Sequence Memory

To allow the htm.core temporal memory to learn sequences effectively, it is import to understand the impact of the different parameters in more detail.

In this part I will introduce

  • columnDimensions
  • cellsPerColumn
  • maxSegmentsPerCell
  • maxSynapsesPerSegment
  • initialPermanence
  • connectedPermanence
  • permanenceIncrement
  • predictedSegmentDecrement

Temporal Memory – Previously on this blog…

Part 1 just covered enough basics of htm.core to get us started, and we actually saw how the single order memory got trained.
A cycle of encoded increasing numbers from 0 to 9 was very easy to predict, as there was always just one specific value that could follow the … more

12. February 2020 Artificial Intelligence, Big Data, Docker, Jupyter, Machine Learning, Python

Using the htm.core-jupyter docker image

htm.core-jupyter

To make it easier to get started with some of my htm.core experiments or with htm.core in general, I thought it would make sense to provide a docker image with htm.core preinstalled. So here it is:
Please welcome the htm.core-jupyter image.

This image is using the scipy-notebook as foundation, with the htm.core package installed on top of it.

For a list of the other preinstalled python packages, just look here.
There are also a lot of other Jupyter docker images available. I recommend starting with the Jupyter Docker Stacks quick start page for a … more

4. February 2020 Artificial Intelligence, Big Data, Development, Hierarchical Temporal Memory, Learning, Python

Hierarchical Temporal Memory – part 1 – getting started

Hierarchical Temporal Memory - Neuron

Prologue

I came across the concept of Hierarchical Temporal Memory (HTM) and its implementation a while ago, and am still very fascinated about this approach to artificial intelligence.

When, about one year ago, the active development shifted towards the community fork named htm.core, which supports Python 3, it became finally time to have a closer look and try it out by myself.

BTW: According to this forum post, there are no plans to upgrade the older NuPIC library to Python 3.

A lot of documentation about the theory of HTM is available at numenta.org… more

Scroll to top