# Statistical Channels

**URL:** <https://discourse.numenta.org/t/statistical-channels/7860>\
**Category:** Lounge\
**Created:** [September 7, 2020, 7:41am UTC](https://discourse.numenta.org/t/statistical-channels/7860 "2020-09-07T07:41:18Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![SeanOConnor](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/seanoconnor/32/3441_2.png) [@SeanOConnor](https://discourse.numenta.org/u/SeanOConnor)\
**Post date:** [September 7, 2020, 7:41am UTC](https://discourse.numenta.org/t/statistical-channels/7860/1 "2020-09-07T07:41:18Z")

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Each output dimension of a linear fast transform is a different dot product of the input vector. Each then is a different filter result on or statistic of the input vector. Applying parametric functions to those outputs allows you to nudge the statistics produced by a further fast transform this way and that. The pattern is statistics, nudges based on decision making, statistics, nudges…  
That is a fast transform neural network with preordained statistical channels.  
With a conventional artificial neural networks things get mixed together. The statistical channels emerge during training and are mixed in with the amount of statistic nudging and — separately ReLU or whatever does the decision making.  
You need to understand the statistics of the dot product for that comment to be helpful I suppose.