MODELING AND VISUALIZING DYNAMIC 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
M ODELING AND V ISUALIZING D YNAMIC A SSOCIATIVE N ETWORKS : Towards Developing a More Robust and Biologically-Plausible Cognitive Model Based on Dr. Anthony.
Dec 17, 2015
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MODELING AND VISUALIZING
DYNAMIC 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
I’m a neural net
I’M A PC...
WHY NEURAL NETS?
Pattern recognition Inferring a function by observation Robustness against errors Parallel nature
ARTIFICIAL NEURAL NETWORKS
ARTIFICIAL NEURAL NETWORKS
Artificial way of adjusting: setting weights
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.
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
DAN’S COGNITIVE ABILITIES COME FROM LONG-TERM LEARNING AND CURRENT STATE
TRANSLATION INTO A NODE-CENTRIC MODEL
EARLY EXCEL PROTOTYPE
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
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
OTHER DESIGN CONSIDERATIONS
General separation of concerns (59 classes)
Model-View-Controller“Core framework” with “helper” controllers
& GUI views/wrappers
GUI look, cross-platform
VISUALIZATION
PREFUSE framework Radial tree layout
(PREFUSE) Color nodes based
on activation Color edges based
on connection type Highlighting,
animation, etc.
RESULTS: DAN SOFTWARE SUITE
Overall successfulQuickConvenient UIAdaptableTrue to model
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.
RESULTS: DAN MODEL
Has not gone unchanged:
RESULTS: DAN MODEL
Has not gone unchanged:
RESULTS: DAN MODEL
Has not gone unchanged:
training:“the boy woke up”“the boy fell asleep”
“the boy woke up”“the boy fell asleep”
RESULTS: DAN MODEL
Has not gone unchanged:
training:“the boy woke up”“the boy fell asleep”
“the boy woke up”“the boy fell asleep”
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
I’m a DAN
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