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