# Universal Encoder **URL:** \ **Category:** Numenta Theory\ **Tags:** encoders\ **Created:** [May 14, 2016, 11:31am UTC](https://discourse.numenta.org/t/universal-encoder/354 "2016-05-14T11:31:21Z")\ **Posts on this page:** 1\ **Showing post:** 120

**Author:** ![Bitking](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/bitking/32/3099_2.png) [@Bitking](https://discourse.numenta.org/u/Bitking)\ **Post date:** [February 21, 2018, 4:07pm UTC](https://discourse.numenta.org/t/universal-encoder/354/120 "2018-02-21T16:07:29Z")

This sounds like an excellent analysis project. I have been thinking about three different lines that may give you some things to think about. I think think that at the most basic level the older brain structures are the initiating force that drives behavior. A baby is mostly a bundle of uncoordinated instincts that drive actions before the cortex is trained to deal with the world. > [@Old versus new neural network](https://discourse.numenta.org/t/old-versus-new-neural-network/3475/10): > > I am invested in this model: “Another way of looking at all this is that the lower brain structures “drags” the cortex along as is does what a lizard brain might do and the cortex responds by projecting back learned shaping influences to improve this behavior.” A baby has instinctual behavior to look at faces and pair that with comfort and nursing.[1] (priming things like visual search and social behavior) I could go on at length but I will just jump right to the summation: without these train… As the encoding/learning progresses islands of learning form in the various maps and are refined as you continue to experience the world and are “surprised” by things that are different than the previously learned items. Please look at the Calvin references at the bottom of this post. HTM neurons with Mexican hat connection distribution/sampling are perfect for forming the hex patterns described as a basic unit of organization: > [@How HTM is supposed to deal with spatial invariance?](https://discourse.numenta.org/t/how-htm-is-supposed-to-deal-with-spatial-invariance/3051/47): > > Let’s do a bit of a deep dive on this - the representation of sounds as a time sequence. This leads to more general questions that seems relevant to this conversation. In programming of computers, we can assign a space for variables, pull them apart and bring them back together as needed for various operations. In neural tissues, we don’t have this luxury. The various sensory streams come in on the same stream with successive waves laying one atop the next. In vision, we have the overall scene… The popular school of thought on distributed/deep learning usually starts from the intuitive approach that the meanings in each map forms automatically which cascades up through the layers to build up to higher level representations. This paper makes a compelling case for the opposite approach - the high-level representation is pushed down through the layers. While this paper focuses on the vision system I can see the concepts described being appropriate throughout the cortex. If you think of this with the lizard brain as the driver for the top-down trainer suddenly a whole bunch of things fall into place: Deep Predictive Learning: A Comprehensive Model of Three Visual Streams Randall C. O’Reilly, Dean R. Wyatte, and John Rohrlich > **[Deep Predictive Learning: A Comprehensive Model of Three Visual Streams](https://arxiv.org/abs/1709.04654)** > > How does the neocortex learn and develop the foundations of all our high-level cognitive abilities? We present a comprehensive framework spanning biological, computational, and cognitive levels, with a clear theoretical continuity between levels,... --- _[View the full topic](https://discourse.numenta.org/t/universal-encoder/354)._