# No influence of learning based on the permanence of proximal connections

**URL:** <https://discourse.numenta.org/t/no-influence-of-learning-based-on-the-permanence-of-proximal-connections/2757>\
**Category:** Numenta Theory\
**Tags:** spatial-pooling\
**Created:** [August 28, 2017, 10:20pm UTC](https://discourse.numenta.org/t/no-influence-of-learning-based-on-the-permanence-of-proximal-connections/2757 "2017-08-28T22:20:58Z")\
**Posts on this page:** 1\
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**Author:** ![jakebruce](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/jakebruce/32/996_2.png) [@jakebruce](https://discourse.numenta.org/u/jakebruce)\
**Post date:** [August 29, 2017, 11:28pm UTC](https://discourse.numenta.org/t/no-influence-of-learning-based-on-the-permanence-of-proximal-connections/2757/13 "2017-08-29T23:28:33Z")

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I’ve had some success by training autoencoders to represent arbitrary data (of arbitrary density) as sparse vectors, and then feeding that in as input to HTM, which may be an option for you. I described the approach here:

> [@Proof of concept: Trainable universal encoder architecture](https://discourse.numenta.org/t/proof-of-concept-trainable-universal-encoder-architecture/2541):
>
> Howdy everyone. I’ve been thinking about the process of encoding arbitrary high-dimensional data for input to networks such as HTM. For low-dimensional data such as scalars, the traditional hand-crafted encoders probably work pretty well. However, high-dimensional data such as images contain a lot of redundant structure, so encoding them as a concatenation of scalar quantities may be wasteful of computation and memory. I’ve produced a proof-of-concept encoder architecture that uses the methodol…

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