# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Recall Demonstration with Toy-Data
#### 3.5.2 Multiple-Cue Recall (MCR) Experiment  
The MCR experiments were carried out to determine the recall vector at input layer 0 for an E-A vector at layer 1. The constituents of the E-A vector are degree of confidence (DoC) values that define the expected representation of each abstract concept node at layer 1. To demonstrate MCR, five experiments were performed, and in every experiment, six regulation factors \(\rho\) (0%, 0.1%, 0.2%, 0.3%, 0.4%, and 0.5%) were used to make inferences. Table 2 lists the observations of O-A for the respective E-A in each MCR experiment. Figure 9 displays the trajectories of recalled activation in layer 0 for six regulation factors with respect to each experiment. The E-A vectors used in the MCR experiments are vectors obtained by propagating an input activation from layer 0 to layer 1. Hence, in MCR simulation, we also have an expected recall (E-R) vector to perform evaluations. The following are the MCR experiment descriptions along with observations:  
* **Exp-6:** In this experiment, an E-A vector of _[0.57, 0.16, 0.06, 0.15, 0.25]_ was provided to the GBP algorithm. With this E-A, we wanted to recall activation at input layer 0, that is 57%, 16%, 06%, 15% and 25% represented by nodes C1, C2, C3, C4, and C5, respectively. Table 2 lists the O-A observed for the six regulation factors. The results with \(\rho\) of 0% and 0.1% show the outcome, which is almost identical to that of E-A. The E-R vector of this experiment was _[0.2256, 0.7610]_. With \(\rho\) of 0% and 0.1%, the observed recall was _[0.2260781, 0.7647118]_ and _[0.2343844, 0.7602842]_, respectively, which are also similar to the expected recall vector. Figure 9a shows the trajectories of all the recalled activation vectors at layer 0 for the E-A vector w.r.t. their six regulation factors. * **Exp-7:** For this experiment, the GBP algorithm was injected with an E-A vector of _[0.07, 0.04, 0.23, 0.46, 0.05]_ for recalling an activation vector at input layer 0. The six O-A vectors obtained for the six regulation factors can be seen in Table 2. The O-A vector for \(\rho\) of 0% and 0.1% was almost the same as the E-A vector. The recalled activations regulation factor 0% was _[0.9875402, 0.6551013]_, which is almost similar to the E-R vector _[0.9896, 0.6568]_. Figure 9b shows all the recalled trajectories for this experiment. * **Exp-8:** In this experiment, the GBP algorithm was initialized with the E-A vector of _[0.09, 0.5, 0.22, 0.05, 0.52]_. The E-R vector for this experiment was _[0.3458, 0.1157]_, and two similar vectors, _[0.3444873, 0.1032499]_ and _[0.3489568, 0.1284956]_, were recalled in this experiment using regulation of 0% and 0.1%, respectively. Figure 9c shows all recalled trajectories. The O-A vectors obtained with the regulation of 0% and 0.1% were also identical to the E-A vector of this experiment--see Table 2.
* **Exp-9:** The recall simulation in this experiment was instantiated with an E-A vector of _[0.09, 0.40, 0.28, 0.07, 0.35]_, and a recall vector of _[0.4410, 0.1341]_ was expected at input layer 0. Upon using the GBP algorithm with six regulation factors, the recall operation without regulation, i.e., \(\rho\) of 0%, produced the most similar recall vector _[0.4370989, 0.1308456]_ and the corresponding O-A vector at layer 1. However, the outcome with 0.1% regulations was also similar to a recalled vector _[0.4392012, 0.1518065]_ at input layer 0--see Figure 9d. * **Exp-10:** This experiment used an E-A vector _[0.07, 0.09, 0.44, 0.26, 0.10]_ in order to obtain an E-R vector _[0.8813, 0.4145]_ at input layer 0. The six simulations were carried