Towards a “Brain-Guided” Cognitive Architecture - Bioinspired Approaches for Human-Centric Technologies - page 225

as he/she gradually helps the developmental curve, without directly suggesting the
solution, but creating situations that can aid new learning, contradictions, and
abstractions. At the same time, this scenario is used to explore the organization
and flexible use of episodic memory of the robot. The main contents of the episodic
memory for this scenario were identified as the temporal order of the robot's
“action” on objects and the final reward received by the user. At the same time,
the activations in the neurons directly correspond to activations in the “object hubs”
and “action hubs” that were active also during explorative learning. For the stacking
scenario (depicted in Fig. 7.8 ), let us consider a very small patch of a simulated
neocortex, consisting of 1,000 pyramidal cells. For simplicity in visualization, the
1,000 neurons are organized in a sheetlike structure with 20 rows each containing
50 neurons. Every row may be thought as an event in time (related to object, action,
or reward) and the complete memory as an episode of experience (e.g., picking a
cylinder and placing it on a mushroom and getting a null reward from the user and
vice versa).
This neural network consisting of a sheet of 1,000 pyramidal cells acts as an
auto-associative memory that builds up on a recent excitatory-inhibitory neural
network proposed by Hopfield ( 2008 ). So next time the robot perceives a mushroom
(through activations in the color and shape maps), the partial cue is sufficient to
recall its past experiences with mushroom (e.g., placing a cylinder on top of it and
getting a reward of 0 or placing it on top of the cylinder that was more rewarding).
The right panel shows what is “remembered” when these objects are encountered in
the future. The neural map (shown in green) depicts the activations in the object
connector hub due to the result of bottom-up perception (case 1 only green
mushroom and case 2 both mushroom and cylinder). Note that, under such circum-
stances, the anticipated reward can be used to trigger competition between “remem-
bered episodic experiences” in a way that all memories “compete to survive”:
survival based on their capability to reenact

their plans once again through

the body.

7.4.2.1 Interplay Between Episodic Memory and Abstraction

Colors of objects do not affect the way they move when they are used to create the
tallest stack. Can this information be abstracted through playful explorative learn-
ing and recall of such past experiences? Suppose that we started with the robot
playing with green sphere and a yellow cylinder; the teacher now presents the robot
with a blue cylinder and orange sphere. Since activity in object hubs reflects activity
in property-specific maps that drive them, there is partial similarity in the neural
activation of the object hubs; the objects are of different colors but same shapes.
Approximate similarity is enough to generate the partial cue and reconstruct the
related past experiences. When presented with a blue cylinder and orange sphere,
still the past memories of playing with green sphere and a yellow cylinder can be
retrieved successfully. Also note that the partial cue is different and contains less
information as compared to the partial cues. This is because the objects in the world