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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

NeuroLISP: High-level symbolic programming with attractor neural networks.

Gregory P Davis¹, Garrett E Katz², Rodolphe J Gentili³

¹Department of Computer Science, University of Maryland, College Park, MD, USA.

Neural Networks : the Official Journal of the International Neural Network Society

|December 11, 2021

Summary

This summary is machine-generated.

NeuroLISP, a novel attractor neural network, bridges the gap between symbolic AI and neural networks by executing LISP programs. This biologically-plausible model features a temporally-local working memory, enabling advanced cognitive tasks and paving the way for human-level artificial intelligence.

Keywords:

Associative learning Cognitive control Compositionality Programmable neural networks Symbolic processing Working memory

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Area of Science:

Neuroscience
Artificial Intelligence
Cognitive Science

Background:

Symbolic methods outperform current neural networks in compositional reasoning tasks like planning and inference.
A computational gap exists between cognitive and neurocomputational algorithms, hindering understanding of cognition and AI progress.
Existing neural networks have biologically-implausible working memory systems, limiting their cognitive abilities.

Purpose of the Study:

Introduce NeuroLISP, an attractor neural network capable of representing and executing LISP programs.
Address limitations in neural network working memory for high-level symbolic programming.
Demonstrate a neurocognitive controller for AI.

Main Methods:

Developed NeuroLISP, an attractor neural network with a temporally-local working memory.
Implemented itinerant attractor dynamics, top-down gating, and fast associative learning.
Incorporated high-level programming constructs: compositional data structures, scoped variable binding, and program manipulation in working memory.

Main Results:

Validated the NeuroLISP interpreter's correctness through computational experiments.
Showcased NeuroLISP's ability to learn programs for complex data structures (multiway trees).
Demonstrated NeuroLISP's capacity for compositional string manipulation (PCFG SET task) and symbolic AI algorithms (first-order unification).

Conclusions:

NeuroLISP functions as an effective neurocognitive controller, capable of replacing symbolic components in hybrid AI models.
NeuroLISP serves as a proof of concept for advancing high-level symbolic programming within neural networks.
This work contributes to bridging the gap towards human-level artificial intelligence.