Sorry, error in the principles of HTM: should include Sequence Memory as a key principle. Also, that should read "10-15 years"!
On Fri, Jan 9, 2015 at 1:27 PM, Fergal Byrne <[email protected]> wrote: > Hi Dinesh, > > HTM refers to the general theory developed by Jeff Hawkins and Numenta > over the past 1-15 years. You can think of HTM as the general "big idea" of > how we believe the neocortex works. The key aspects of HTM are Jeff's six > principles, which refer to hierarchy, sparse distributed representations, > online learning from streaming data, a uniform algorithm, combination of > sensory and motor function everywhere, and attention. While the theory will > accumulate detail (for example what the roles of the layers inside a region > might be doing), it grows outwards stably from this kernel. > > Officially, CLA refers to the particular detailed algorithmic design for a > single layer of neurons, which is outlined in the 2011 White Paper and > (partially) implemented by NuPIC. Jeff Hawkins and Numenta have indicated > that they wish to "freeze" this meaning of CLA and use a different name for > new versions of their detailed algorithmic designs. > > The rest of us have become accustomed to using "CLA" to refer to an > algorithmic design which is close to Numenta's, but might differ in some > minor or major aspects. The key features of CLA, which generalise across > most of our models, are: > > - Neurons arranged in columns ("mini-columns" in neocortex) which share > feedforward inputs and have similar feedforward responses. > - Sparsity imposed by a columnar inhibition algorithm. > - Feedforward inputs appear on proximal dendrites (to a column in official > CLA, also to cells in some models). > - Neurons in a layer have axons connected to distal dendrites in the same > layer, allowing for prediction. > - Proximal dendrites perform some version of linear summing. > - Distal dendrite segments act independently as coincidence detectors. > - Layers can learn first-order transitions between feedforward patterns, > and also higher-order sequences using choices of active cells in an active > column. > - Columns which correctly predict their activity have one cell active, > otherwise several cells activate (burst). > > HTM is quite general, allowing for many more detailed theories and designs > to be claimed to correspond to HTM, but It's much easier to quantify how > well a design matches up with CLA proper. > > We tend to use CLA when referring to processes in some detail (at the > layer, column, neuron, dendrite, synapse levels), and HTM when talking > about how things work at the layer, region and brain levels. We'll also be > seen using "HTM" when we propose ideas which supercede or contradict > assumptions underlying Numenta's "official" CLA design. > > The other thing to bear in mind is that CLA is an internal name (within > the community) which has no general currency in either neuroscience or > AI/ML, while HTM is well-known (at least by name) to researchers in both > fields. > > Regards, > > Fergal Byrne > > > On Fri, Jan 9, 2015 at 12:48 PM, David Ragazzi <[email protected]> > wrote: > >> Dear Dinesh, >> >> > 1.What is the difference between CLA and HTM? 2.Is CLA generalization >> of HTM as the CLA(the agorithms based on cortex) name suggests so? >> Explain if wrong. >> >> CLA => Cortical Learning **ALGORITHMS** >> HTM => Hierarchical Temporal **THEORY** >> >> As the names say, CLA tries simulate what the HTM states about how cortex >> could work. Something we use wrongly HTM acronym to refer to CLA. But the >> names are clear, one is the theory, the other is the algorithmic model of >> it. Just remember neither all features addressed on HTM are implemented on >> CLA (yet). >> >> David >> >> On 9 January 2015 at 10:08, Dinesh Deshmukh <[email protected]> wrote: >> >>> Hi >>> >>> 1.What is the difference between CLA and HTM? >>> 2.Is CLA generalization of HTM as the CLA(the agorithms based on cortex) >>> name >>> suggests so?Explain if wrong. >>> >>> Thank you. >>> >>> >> >> >> -- >> David Ragazzi >> MSc in Sofware Engineer (University of Liverpool) >> OS Community Commiter at Numenta.org >> -- >> "I think James Connolly, the Irish revolutionary, is right when he says that >> the only prophets are those who make their future. So we're not >> anticipating, we're working for it." >> > > > > -- > > Fergal Byrne, Brenter IT > > http://inbits.com - Better Living through Thoughtful Technology > http://ie.linkedin.com/in/fergbyrne/ - https://github.com/fergalbyrne > > Founder of Clortex: HTM in Clojure - > https://github.com/nupic-community/clortex > > Author, Real Machine Intelligence with Clortex and NuPIC > Read for free or buy the book at https://leanpub.com/realsmartmachines > > Speaking on Clortex and HTM/CLA at euroClojure Krakow, June 2014: > http://euroclojure.com/2014/ > and at LambdaJam Chicago, July 2014: http://www.lambdajam.com > > e:[email protected] t:+353 83 4214179 > Join the quest for Machine Intelligence at http://numenta.org > Formerly of Adnet [email protected] http://www.adnet.ie > -- Fergal Byrne, Brenter IT http://inbits.com - Better Living through Thoughtful Technology http://ie.linkedin.com/in/fergbyrne/ - https://github.com/fergalbyrne Founder of Clortex: HTM in Clojure - https://github.com/nupic-community/clortex Author, Real Machine Intelligence with Clortex and NuPIC Read for free or buy the book at https://leanpub.com/realsmartmachines Speaking on Clortex and HTM/CLA at euroClojure Krakow, June 2014: http://euroclojure.com/2014/ and at LambdaJam Chicago, July 2014: http://www.lambdajam.com e:[email protected] t:+353 83 4214179 Join the quest for Machine Intelligence at http://numenta.org Formerly of Adnet [email protected] http://www.adnet.ie
