Modeling and Visualizing Dynamic Associative Networks:

1. Modeling and VisualizingDynamic Associative Networks: Towards Developing a More Robust and Biologically-Plausible Cognitive Model Based on Dr. Anthony Beavers’ ongoing research By Michael Zlatkovsky, dual-major in Computer Science and Cognitive Science

2. I’m a PC... I’m a neural net

3. Why Neural Nets? • Pattern recognition • Inferring a function by observation • Robustness against errors • Parallel nature

4. Artificial Neural Networks

5. Artificial Neural Networks • Artificial way of adjusting: setting weights

6. Dr. Beaver’s Dynamic Associative Network Model • Dr. Beavers, Director of UE’s Cognitive Science Department, is attempting to explore a different model of cognition.

7. Dr. Beaver’s Dynamic Associative Network Model • No more mystery “hidden layer” • Learning through the order and structure of experience • No “unnatural” training • Organic network • Can incorporate new information

8. DAN’s Cognitive Abilities come from Long-Term Learning and Current State

9. Translation into a Node-Centric Model

10. Early Excel Prototype

11. The DAN Software Suite • Based on prototype, create a self-contained DAN Model • Written in Java; object-oriented approach • Expand on features of Excel Model (various activation modes, learning mode, settings) • Most importantly: focus on design fundamentals to ensure speedy operation and high capacity. • Create visualization routines

12. Re-Calculations • Most frequent operations • DANs are massively parallel • Re-computing from scratch: O(n2). • EX: for 1000 node-network, change in 2 nodes that impact 5 nodes each... • Instead of 10 re-calculations, 1,000,000! • My scheme: buffered change-propagating dependency-driven re-calculations

15. Other Design Considerations • General separation of concerns (59 classes) • Model-View-Controller • “Core framework” with “helper” controllers & GUI views/wrappers • GUI look, cross-platform

16. Visualization • PREFUSE framework • Radial tree layout (PREFUSE) • Color nodes based on activation • Color edges based on connection type • Highlighting, animation, etc.

19. Results: DAN Software Suite • Overall successful • Quick • Convenient UI • Adaptable • True to model

20. Results: DAN Model • Promising results: various rudimentary cognitive abilities: • “Initial Intelligence”: pattern recognition, feature detection, memorization of simple sequences, identification of similarities and differences, storage of relational data, comparison and classification, etc. • Possibly, building blocks of more sophisticated intelligence.

21. Results: DAN Model • Has not gone unchanged:

22. Results: DAN Model • Has not gone unchanged:

23. Results: DAN Model • Has not gone unchanged: training: “the boy woke up” “the boy fell asleep” “theboywoke up” “theboyfell asleep”

24. Results: DAN Model • Has not gone unchanged: training: “the boy woke up” “the boy fell asleep” “theboywoke up” “theboyfell asleep”

25. Results: Overall • More robust? • Don’t know... Yet. • Received with curiosity and some enthusiasm by researchers working in the field. • More biologically plausible? • Absolutely. • Hebbian Neurological Principle: nodes that “fire together, wire together”. • Contrast with ANNs’s statistically-based learning

26. I’m a DAN