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).
ok guys, dont get silly, htms just copy data into them, if you want a concept, its going to be a group of the data, and then you can have motor depend on the relation, and then it all works.

our cortex, is probably way better, and actually generates new images, but a computer cortex WONT, but as it happens, it doesnt matter, and its very very useful already.

but drawing concepts from a data pool, is tricky, the easiest way to do it is by recording an invarient combination of its senses, and when it comes up, associate everything else to it, this will give you organism or animal like intelligence.

drawing harder relations, is what humans do, and its ad hoc logic, imagine a set explaining the most ad hoc relation, and then it slowly gets more and more specific, until it gets to exact motor programs... and all knowledge you have is controlling your motor, and making relations that trigger pathways in the cortex, and tell it where to go, from the blast search that checks all motor, and finds the most successful trigger.

woah that was a mouthful, but watch out dummies, you wont get no concepts from a predictive assimilator, which is what htm is, unless you work out how people draw relations in the data pool, like a machine, and if you do that, its like a program thats programming itself.

no shit.