1. Practical Heirarchical Temporal Memory for Time Series Prediction
Author: Nicholas Hainsey Faculty Advisor: Dr. C. David Shaffer

2. Heirarchical Temporal Memory
Neural network
Created by Jeff Hawkins
Designed to mimic the human neocortex
Network A B C
INPUT
Implemented one region of the neocortex
Prediction
Time

3. Input Encoding 1 A B C 1 01110 01000 01000 01000 01110 Time A B C1 A B C
Time 1 A B C
Time

4. Spatial Pooler HTM Region
Input bits

5. Spatial Pooler
Proximal dendrite
Input bits

6. Spatial Pooler
Overlap Score
Input bits

7. Spatial Pooler
Goal:
Each input will activate a small percentage of the columns
Similar inputs will activate overlapping sets of columns

8. Temporal Pooler Inactive Active Predicted Getting Started
Dystal Dendrites, Interconnections between active and predicted Make one of the B cells unpredicted then show bursting How does temporal pooler learn
Inactive
Active
Predicted

9. Temporal Pooler Inactive Active Predicted Getting Started
Dystal Dendrites, Interconnections between active and predicted Make one of the B cells unpredicted then show bursting How does temporal pooler learn
Inactive
Active
Predicted

10. HTM Implementations HTMCLA HTM-CLA-Visualizer NuPIC
A C++ implementation based off Numenta’s CLA white paper
HTM-CLA-Visualizer
Java interface for visualization of HTMs
NuPIC
Created by Numenta, used in Nustudio

11. Nustudio

12. Nustudio

13. Nustudio
Run Simulation Stop

14. Nustudio
Connect to server

15. Nustudio
Connect to server

16. Nustudio
Run simulation from server

17. Nustudio
Pos (z): 2 Was Predicted: True Is Active: True Activation Rate: .050
Prediction Rate: .500

18. Nustudio

19. Nustudio
P1: Mean P2: Standard Deviation

20. Nustudio

21. Predictions with noise
Learning SD: SD: SD: 20.0

22. Noise with learning
No Noise
5.0 SD
10.0 SD
20.0 SD

23. Conclusions Additions to Nustudio Noise Comparison
Constant simulation from file
Live simulation from server
Adding noise to incoming data
Viewing individual regions of the HTM at any step
Noise Comparison
Still seems stable under varying levels of noise

24. Future Work More robust test of noisy data More customization of noise
More distributions to choose from
More control over where noise is applied
Ability to export prediction data