Add tutorial diagram for GaussianErrorCells (#107)

* add GaussianErrorCell figure

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* Rename GaussianErrorCells_figure copy.png to GaussianErrorCells_figure.png

* Update error_cell.md

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* Update error_cell.md

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* Delete docs/images/tutorials/neurocog/GaussianErrorCells_figure.png

Author: Faezehabibi

docs/images/tutorials/neurocog/GEC.png

@@ -1,5 +1,9 @@
+
+
 # Lecture 3B: Error Cell Models
 
+<img src="../../images/tutorials/neurocog/SingleGEC.png" width="250" align="right"/>
+
 Error cells are a particularly useful component cell that offers a simple and
 fast way of computing mismatch signals, i.e., error values that compare a
 target value against a predicted value. In predictive coding literature, mismatch
@@ -13,8 +17,11 @@ key examples of where error neurons come into play). In this lesson, we will
 briefly review one of the most commonly used ones -- the
 [Gaussian error cell](ngclearn.components.neurons.graded.gaussianErrorCell).
 
+
 ## Calculating Mismatch Values with the Gaussian Error Cell
 
+<img src="../../images/tutorials/neurocog/GEC.png" width="200" align="right"/>
+
 The Gaussian error cell, much like most error neurons, is in fact a derived
 calculation when considering a cost function. Specifically, this error cell
 component inherits its name from the fact that it is producing output values