Question: I have two dropdowns DD1 & DD2 , I want that if user selects option HELLO (value="100") in DD1 and presses a key , then the same value option in DD2 is removed, DD1 will have that option,
it would only be removed from DD2

```
var selectedItems = $('#DD1').val();

 for ( var i = 0 ; i < selectedItems.length; i++) 
        {
           $("#DD2 option[value='"+selectedItems[i]+']"').remove();
        }                                                                       

```

Answer: 
The software which runs the HTM is called NuPIC (Numenta Platform for Intelligent Computing). A NuPIC region (representing a region of neocortex) can be configured to either use topology or not, depending on the type of data it's receiving. 

If you use topology, the usual setup maps each column to a set of inputs which is centred on the corresponding position in the input space (the connections will be selected randomly according to a probability distribution which favours the centre). The spatial pattern recognising component of NuPIC, known as the Spatial Pooler (SP), will then learn to recognise and represent localised topological features in the data. 

There is absolutely no restriction on the "linearity" of the input data which NuPIC can learn. NuPIC can learn sequences of spatial patterns in extremely high-dimensional spaces, and is limited only by the presence (or lack of) spatial and temporal structure in the data.

To answer the specific part of your question, yes, NuPIC can learn non-Euclidean and non-linear relationships, because NuPIC is not, and cannot be modelled by, a linear system. On the other hand, it seems logically impossible to infer relationships of a dimensionality which exceeds that of the data.

The best place to find out about HTM and NuPIC, its Open Source implementation, is at NuPIC's [community website](http://numenta.org) (and mailing list).
Yes HTM uses euclidean geometry to connect synapses, but this is only because it is mimicking a biological system that sends out dendrites and creates connections to other nearby cells that have strong activation at that point in time.

The Cortical Learning Algorithm (CLA) is very good at predicting sequences, so it would be good at determining "Time flies like an arrow, fruit flies like a" and predict "banana" if it has encountered this sequence before or something close to it. I don't think it could infer that a fruit fly is a type of insect unless you trained it on that sequence. Thus the T for Temporal. HTMs are sequence association compressors and retrievers (a form of memory). To get the pattern out of the HTM you play in a sequence and it will match the strongest representation it has encountered to date and predict the next bits of the sequence. It seems to be very good at this and the main application for HTMs right now are predicting sequences and anomalies out of streams of data.

To get more complex representations and more abstraction you would cascade a trained HTMs outputs to another HTMs inputs along with some other new sequence based input to correlate to. I suppose you could wire in some feedback and do some other tricks to combine multiple HTMs, but you would need lots of training on primitives first, just like a baby does, before you will ever get something as sophisticated as associating concepts based on syntax of the written word.