# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Recall Demonstration with Toy-Data
#### 3.5.1 Single-Cue Recall (SCR) Experiment  
In SCR experiments, the objective was to determine the recalled activation at input layer 0 by injecting binary activation values as expected activation in abstract concept layer 1. The expected activation vector contains value 1 for only one abstract concept node, and for the remaining nodes, 0 is assigned. In all SCR experiments, six regulation factors \(\rho\) (0%, 0.5%, 0.75%, 1%, 1.25% and 1.5%) were used. Table 1 logs the E-A and the thousandth iteration value of O-A for the five SCR experiments. The following are the five experiments to demonstrate the SCR operation along with observations:  
* **Exp-1:** This is the first experiment in which we injected an E-A vector [0, 0, 0, 0, 1] of activation at abstract concept layer 1. The objective was to recall activations at input layer 0 for which a very high activation was observed at node C\({}_{5}\) at layer 1 and comparatively lower activation for the other four nodes at layer 1. The GBP algorithm was executed six times with an E-A of [0, 0, 0, 0, 1] for the six different regulation factors (\(\rho\)). The observation for Exp-1 (see Table 1) shows that with a \(\rho\) of 0.75%, the maximum activation of 0.85 was observed at node C\({}_{5}\). As expected, the node C\({}_{2}\) received good activation because nodes C\({}_{2}\) and C\({}_{5}\) represent clusters 1 and 5 (see Figure 3), which are close to one another. Figure 8a shows the six trajectories for the six regulation factors; each trajectory is formed by one thousand iterations. In Figure 8a, the yellow marker shows the CRDP of cluster C\({}_{5}\) and the trajectory with \(\rho\) of 0.75% converge closest to this CRPD. Thus, an activation vector [0.1, 0.24] is recalled at input nodes [S\({}_{1}\), S\({}_{2}\)] for the given E-A vector [0, 0, 0, 0, 1]. * **Exp-2:** In this experiment, the E-A provided to the GBP algorithm was [0, 0, 0, 1, 0] to recall activation at layer 0, which is strongly represented by node C\({}_{4}\). For each regulation factor, the GBP algorithm was run; the O-As obtained at layer 1 are listed in Table 1, and the corresponding recalled activation at layer 0 is shown in Figure 8b. From the observations, it can be deduced that the experiment with \(\rho\) of 0.75% produced the best outcome and recall activation [0.9, 0.9] for input layer 0. * **Exp-3:** In this experiment, the GBP algorithm was supplied with an E-A vector of [0, 0, 1, 0, 0] to recall the input layer 0 vector, which is represented by node C\({}_{3}\) at layer 1. Figure 8c and Table 1 shows the recall trajectories at layer 0 and the O-A vector at layer 1, respectively. The experiment with the regulation of 0.75% displayed the best representation. A vector [0.92, 0.11] was recalled at layer 0 for the injected E-A vector. * **Exp-4:** The aim of this experiment was to recall an input vector that closely represents the abstract concept node C\({}_{2}\) by feeding the GBP algorithm with an E-A vector of [0, 1, 0, 0, 0]. After applying the six regulation factors to each GBP operation, it was observed that the experiment with \(\rho\) of 0.75% displayed the best result. Table 1 shows the O-A for the E-A. Figure 8d shows the trajectories of the recalled values and shows the best outcome with \(\rho\) of 0.75% that converges to an activation vector [0.14, 0.07]. * **Exp-5:** This experiment shows the recall vector obtained by initializing an E-A vector [1, 0, 0, 0, 0] with all GBP experiments with the six regulation values. Unlike the previous experiments, the best O-A was obtained with a regulation factor of 1%. Figure 8d shows the recall outcome for all six regulation factors. At input layer 0, the recall operation with \(\rho\) of 1% results into an activation vector [0.15, 0.91] for the given E-A.