Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Fig 1.

Schematic illustration of MNS in the macaque monkey brain.

Area F5 is connected with the inferior parietal lobule (areas AIP-anterior intra-parietal, PF and PFG). Within the frontal lobe, area F5 is connected with hand/mouth representations of primary motor cortex (Source: Craighero et al. [16]).

More »

Fig 1 Expand

Fig 2.

Computational model of the mirror neuron system.

More »

Fig 2 Expand

Fig 3.

Architecture for incremental imitative learning through self-exploration.

More »

Fig 3 Expand

Fig 4.

Overview of TGAR-HMM learning architecture.

The observed behaviour sequence is first arranged through topological map. This topological map is then used to update the state structure for estimating the optimal number of states and the transition probabilities among these states.

More »

Fig 4 Expand

Fig 5.

Architecture for creation of topological map.

More »

Fig 5 Expand

Fig 6.

Associative Memory Architecture.

More »

Fig 6 Expand

Fig 7.

Simulation environment for experimentation consisting of two robots.

One acts as a demonstrator (right robot), while the second acts as an observer (left robot).

More »

Fig 7 Expand

Fig 8.

Example of different types of perspectives.

(a) v1, (b) v2, (c) v3, (d) v4, (e) v5, (f) v6.

More »

Fig 8 Expand

Fig 9.

Different samples of actions performed by the robot during experimentation.

More »

Fig 9 Expand

Table 1.

Summary of Different Types of Actions Performed and their identification accuracy.

More »

Table 1 Expand

Fig 10.

Output of the segmentation algorithm through Incremental Kernel Slow Feature Analysis.

More »

Fig 10 Expand

Fig 11.

Segmentation through Inc-KSFA for different view perspectives, v1, v2, v3, v4, v5, v6, v7 represents different viewpoints while v0 represents the self-perspective.

More »

Fig 11 Expand

Fig 12.

Ratio of average accuracy of segmentation results calculated between joint based segmentation and Incremental Kernel SFA segmentation.

More »

Fig 12 Expand

Fig 13.

(a) Plot for compression ratio and average mean square error for different values of vigilance parameter. (b) Effect of different values of vigilance parameter on the number of nodes.

More »

Fig 13 Expand

Fig 14.

Plot of original and generalized motion patterns for (a)–(b) Raising and Lowering Both Arms 180deg (RBA180–LBA180).

More »

Fig 14 Expand

Fig 15.

Effect of noise on the recall rate.

More »

Fig 15 Expand

Table 2.

Result of Associative Recall.

More »

Table 2 Expand