update readmes for the categorized network variables, and implement M…

…ichael's much cleaner way of comparing weights vs inputs in the detector model.

ch2/detector/README.md

@@ -11,7 +11,7 @@ We will see how a particular pattern of weights makes a simulated neuron respond
 
 We begin by examining the `Network` tab, showing the Detector network. The network has an `Input` layer that will have patterns of activation in the shape of different digits, and these input neurons are connected to the receiving neuron (`RecvNeuron`) via a set of weighted synaptic connections. We can view the pattern of weights (synaptic strengths) that this receiving unit has from the input, which should give us an idea about what this unit will detect.
 
-* Select `r.Wt` as the value you want to display (on the left side of the 3D network view) and then click on the `RecvNeuron` to view its receiving weights.
+* Select the `Wts` tab at the top of the list of network variables at the left of the Network view, then click `r.Wt` as the value you want to display, and then click on the `RecvNeuron` to view its receiving weights.
 
 You should now see the `Input` grid lit up in the pattern of an `8`. This is the weight pattern for the receiving unit for connections from the input units, with the weight value displayed in the corresponding sending (input) unit. Thus, when the input units have an activation pattern that matches this weight pattern, the receiving unit will be maximally activated. Input patterns that are close to the target `8` input will produce graded activations as a function of how close they are. Thus, this pattern of weights determines what the unit detects, as we will see. First, we will examine the patterns of inputs that will be presented to the network.
 
@@ -23,7 +23,7 @@ The display that comes up shows all of the different *input patterns* that will
 
 To see the receiving neuron respond to these input patterns, we will present them one-by-one, and determine why the neuron responds as it does given its weights. Thus, we need to view the activations again in the network window.
 
-* Select `Act` in the `Network` to view activations, then click the `Step` button in the toolbar at the top of the window, which will step one `Trial` as indicated.
+* Select `Act` in the `Network` (under the `Act` tab) to view activations, then click the `Step` button in the toolbar at the top of the window, which will step one `Trial` as indicated.
 
 This activates the pattern of a `0` (zero) in the `Input`, and shows 20 cycles of **settling** process where the activation of the receiving unit is iteratively updated over a series of **cycles** according to the point neuron activation function (just as the unit in the `neuron` simulation was updated over time).  We have selected 20 cycles as enough time for the receiving neuron to fully respond to the input.
 
@@ -47,9 +47,9 @@ The graph shows the activation (`Act`) for the unit as a function of trial (and
 
 Now, let's try to understand exactly why the unit responds as it does. The key to doing so is to understand the relationship between the pattern of weights and the input pattern.
 
-* Click the `Digits` button again (if the window was closed) and scroll inside so that the `8` digit is visible. Now resize the whole window so it is roughly big enough to only show the `8` and position the window so it is next to the main window that includes the `Network` display. The idea is that you can see both side-by-side, so you may want/need to shrink the main window to prevent occlusion of the `Digits` window.  Then do `Init` in the toolbar and `Step` for each input digit in turn.
+* Go back to the `Wts/r.Wt` display in the Network, then click the `Digits` button again (if the window was closed), and make sure you can see the weights as well.  The idea is to compare the weights with the digit patterns.
 
-> **Question 2.8:** For each digit, report the number of active `Input` units where there is also a weight of 1 according to the `8` digit pattern.  In other words, report the *overlap* between the input activity and the weight pattern. *HINT: Strictly speaking, the `8` display in the `Digits` window is NOT representing the weights per se, but as we saw earlier using the `r.Wt` functionality in `Network,` they are the same pattern -- and displaying the windows side-by-side just makes the counting easier.* 
+> **Question 2.8:** For each digit pattern, report the number of active units in the pattern where there is also a weight of 1 according to the `8` digit pattern shown in the `r.Wt` view in the Network.  In other words, report the *overlap* between the digit input activity and the weight pattern.
 
 The number of inputs having a weight of 1 that you just calculated should correspond to the total excitatory input `Ge`, also called the **net input**, going into the receiving unit, which is a function of the average of the sending activation `Act` times the weight `Wt` over all the units, with a correction factor for the expected activity level in the layer, `Alpha`:
 
@@ -75,7 +75,7 @@ Next, we will explore how we can change how much information is conveyed by the
 
 **IMPORTANT:** you must press `Init` for changes in `GbarL` to take effect!
 
-* Reduce the `GbarL` value from 2 to 1.8, and do `Init` then `Step` (you might want to change `View update` `Test` to `AlphaCycle` instead of `Cycle` so it only shows the final result of setting for each input).  You can alternatively just hit `Test Run` and look at the `Test Trial Plot`.
+* Reduce the `GbarL` value from 2 to 1.8, and do `Init` then `Step` (you might want to change `View update` `Test` to `AlphaCycle` instead of `Cycle` so it only shows the final result of setting for each input, and go back to viewing `Act`).  You can alternatively just hit `Test Run` and look at the `Test Trial Plot`.
 
 > **Question 2.9:** What happens to the pattern of receiving neuron activity over the different digits when you change GbarL to 1.8, 1.5, and 2.3 -- which input digits does it respond to for each case?  In terms of the tug-of-war model between excitatory and inhibition & leak (i.e., GbarL = leak), why does changing leak have this effect (a simple one-sentence answer is sufficient)?
 

ch2/detector/detector.go

@@ -80,7 +80,7 @@ type Sim struct {
 	Net *leabra.Network `new-window:"+" display:"no-inline"`
 
 	// network parameter management
-	Params emer.NetParams `display:"add-fields"`
+	Params emer.NetParams `display:"-"`
 
 	// contains looper control loops for running sim
 	Loops *looper.Manager `display:"-"`

ch2/neuron/README.md

@@ -112,7 +112,7 @@ Cortical pyramidal neurons exhibit the property of spike rate adaptation. We are
 
 You should observe that spiking is perfectly regular throughout the entire period of activity without adaptation, whereas with adaptation the rate decreases significantly over time. One benefit of adaptation is to make the system overall more sensitive to changes in the input -- the biggest signal strength is present at the onset of a new input, and then it "habituates" to any constant input. This is also more efficient, by not continuing to communicate spikes at a high rate for a constant input signal that presumably has already been processed after some point. As we will see in some other simulations later on, this adaptation also allows us to account for various perceptual and cognitive phenomena. 
 
-For those who want to explore the software a bit more: If you want to make the adaptation effect more extreme, you can click on the "Neuron" label in the Netview, and a dialog box will open up. If you scroll down, you will see various parameters associated with the neuron layer, including GBarE and GBarL (which should be the same values as those you altered in the control panel). But you will also see others that were not in the control panel. To increase the effect of adaptation you can increase GBarK -- the magnitude of KNA adaptation effect as a conductance. Increase that from the default of 1 to a much larger value (e.g., 10) and you should see much stronger adaptation effects. 
+For those who want to explore the software a bit more: If you want to make the adaptation effect more extreme, you can click on the "Neuron" label in the Netview, and a dialog box will open up. If you scroll down, you will see various parameters associated with the neuron layer, including `GBarE` and `GBarL` (which should be the same values as those you altered in the control panel). But you will also see others that were not in the control panel. To increase the effect of adaptation you can increase `GBarK` -- the magnitude of KNA adaptation effect as a conductance. Increase that from the default of 1 to a much larger value (e.g., 10) and you should see much stronger adaptation effects. 
 
 
 

ch3/faces/README.md

@@ -20,7 +20,7 @@ Let's first examine the network, shown in the tab in the right 3D panel.  It has
 
 * `Identity` with 6 labeled units with the names given to the different faces in the input (Alberto, Betty, Lisa, Mark, Wendy, Zane) -- the network can categorize the individual despite differences in emotional expression. Four additional units are available if you want to explore further by adding new faces.
 
-* Select the `r.Wt` variable to view in the `Network` tab, and click on each of the different output category neurons in the network. This will display the weight values going into each neuron. 
+* Select the `Wts/r.Wt` variable to view in the `Network` tab, and click on each of the different output category neurons in the network. This will display the weight values going into each neuron. 
 
 These weights were learned in a way that makes their representations particularly obvious by looking at these weights, so you can hopefully see sensible-looking patterns for each unit. To further understand how this network works, we can look at the input face patterns and corresponding categorization values that it was trained on (this learning process is explained in the chapter on *Learning* in the textbook).
 
@@ -30,7 +30,7 @@ These weights were learned in a way that makes their representations particularl
 
 The next step in understanding the basics of the network is to see it respond to the inputs.
 
-* Select the `Act` value to view the neuron activities in the Network tab, and then change the `Step` level from `Trial` to `Cycle`, and click on the `Step` button to see the network respond to the first face, `Alberto_happy`, one cycle of updating at a time.  You can also use the VCR buttons in the lower-right of the Network tab, after the `Time` label, to review how the network responded cycle-by-cycle -- use the fast-reverse to skip back to the start and then single-step forward in time to see things unfolding cycle-by-cycle.
+* Select the `Act/Act` value to view the neuron activities in the Network tab, and then change the `Step` level from `Trial` to `Cycle`, and click on the `Step` button to see the network respond to the first face, `Alberto_happy`, one cycle of updating at a time.  You can also use the VCR buttons in the lower-right of the Network tab, after the `Time` label, to review how the network responded cycle-by-cycle -- use the fast-reverse to skip back to the start and then single-step forward in time to see things unfolding cycle-by-cycle.
 
 You should see the network process the face input and activate the appropriate output categories for it (e.g., for the first pattern, it will activate `happy`, `male`, and `Alberto`).