A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment
Abstract
:1. Introduction
2. Issue and Modeling Challenges
3. Proposed Mechanism
3.1. Movement Representation
3.2. Quadrant Model for Grid Pattern Generation from the Body Movement
3.2.1. Finding the Coordinates of the Agent Based on the Magnitude and the Current Direction of Movement from a Reference Point
3.2.2. Grid Neuron Activation
Algorithm 1: Calculation for the grid neuron activation |
1: I ← ROUND (XCORD/width) // ROUND is the round off function |
2: J ← CEIL (YCORD/ H) −1 // CEIL is ceiling function |
3: while J < CEIL (YCORD/H) do |
4: J ← J +0.5; |
5: Gx ← I × W// Gx is the X coordinate of the chosen grid point; |
6: Gy ← J × H// Gy is the Y coordinate of the chosen grid point; |
7: D ← EUCLIDEAN ((XCORD,YCORD), (Gx, Gy)); |
8: // Euclidean to find the distance between agent and the chosen grid point |
9: if D < R then |
10: activation ← 1 − (D/R); |
11: else |
12: activation ← 0; |
13: end |
14: if Y is the 0.5 multiple of the height (h) then |
15: activation ← 1; |
16: else if X is the 0.5 multiple of the W(width) then |
17: If Y is the 0.25 multiple of the height (h) then |
18: activation ← 1; |
19: else |
20: activation ← 0; |
21: end |
22: end |
23: end |
3.3. Grid Code Learning and ItsRecalling
3.3.1. Calculation of Grid Point Distances of the Interfered Rings
3.3.2. Role of Grid Spacing in the Localization Accuracy
3.4. Modelling of Place and Grid Neuron Interaction System to Perform Predictions and Recognition
3.5. Place Sequence Learning
4. Experimental Detail
4.1. Object Identification
- Observe the sensation of the base sensor, and recall the grid code corresponds to the observed sensation.
- Other sensors observe their sensations and recall their corresponding grid codes called sensed grid code.
- Next, each sensor integrates their relative position of the base sensor with the grid codes of base sensors to generate new grid codes called path integrated grid code.
- The next comparison will be made between the path integrated grid codes and the sensed grid codes. Those sensed grid codes found similar will be chosen to activate their associated place neurons.
- The objects which are associated with the activated place neurons will be in the list of the active object, and others will be inhibited for future activation.
- A similar process will be repeated again and again until a single object survives in the activation list.
4.2. Prediction during Navigation, and Navigation towards a Goal Location
4.2.1. Finding the Direction of a Goal Location
4.2.2. Navigation Using Place Learner
5. Results and Discussion
5.1. Results of Object Recognition
5.2. Self-Localization Accuracy
5.3. Trip Forgetting
5.4. Comparison between the Navigation Using Tracking Goal Grid Code and Using the Place Sequence (Trip)
5.5. Accuracy Results on Different Grid Spacings and Size of the Activation Field
5.6. Localization Accuracy in Ambiguous Field
5.7. Space and Time Complexity
6. Conclusions and Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Shrivastava, R.; Kumar, P.; Tripathi, S.; Tiwari, V.; Rajput, D.S.; Gadekallu, T.R.; Suthar, B.; Singh, S.; Ra, I.-H. A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment. Appl. Sci. 2020, 10, 5147. https://doi.org/10.3390/app10155147
Shrivastava R, Kumar P, Tripathi S, Tiwari V, Rajput DS, Gadekallu TR, Suthar B, Singh S, Ra I-H. A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment. Applied Sciences. 2020; 10(15):5147. https://doi.org/10.3390/app10155147
Chicago/Turabian StyleShrivastava, Rahul, Prabhat Kumar, Sudhakar Tripathi, Vivek Tiwari, Dharmendra Singh Rajput, Thippa Reddy Gadekallu, Bhivraj Suthar, Saurabh Singh, and In-Ho Ra. 2020. "A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment" Applied Sciences 10, no. 15: 5147. https://doi.org/10.3390/app10155147
APA StyleShrivastava, R., Kumar, P., Tripathi, S., Tiwari, V., Rajput, D. S., Gadekallu, T. R., Suthar, B., Singh, S., & Ra, I. -H. (2020). A Novel Grid and Place Neuron’s Computational Modeling to Learn Spatial Semantics of an Environment. Applied Sciences, 10(15), 5147. https://doi.org/10.3390/app10155147