# Allocentric location inference from feature data

**URL:** <https://discourse.numenta.org/t/allocentric-location-inference-from-feature-data/3098>\
**Category:** Numenta Theory\
**Created:** [November 24, 2017, 3:15pm UTC](https://discourse.numenta.org/t/allocentric-location-inference-from-feature-data/3098 "2017-11-24T15:15:58Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![rhyolight](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/rhyolight/32/3922_2.png) [@rhyolight](https://discourse.numenta.org/u/rhyolight)\
**Post date:** [November 24, 2017, 3:30pm UTC](https://discourse.numenta.org/t/allocentric-location-inference-from-feature-data/3098/2 "2017-11-24T15:30:06Z")

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> [@tommo](#):
>
> is it possible to present a model with only feature data and have it infer the corresponding predicted location

Yes, that is what we do in our code supporting the paper, and that’s what’s happening in your brain too.

> [@tommo](#):
>
> If so, how would the model be structured?

This is happening naturally in the “input layer” of the SMI circuit we detailed in the paper. Every proximal sensation entering the layer will cause predictions to be made representing where on a library of learned objects that sensation has been felt before.

> [@tommo](#):
>
> Also, I haven’t quite grasped how this location data is presented to the input layer, would it be encoded in the same SDR as the feature data?

No, the location data is a distal signal. It is presented to the layer through distal input. If it were encoded in the same SDR as the sensory input, it would be a part of the proximal signal. See [How the allocentric locations are encoded for SMI?](https://discourse.numenta.org/t/how-the-allocentric-locations-are-encoded-for-smi/3092) for similar discussion.

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