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Related Experiment Video

Updated: Jun 10, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

A symbolic/subsymbolic interface protocol for cognitive modeling.

Patrick Simen¹, Thad Polk

¹Princeton Neuroscience Institute Princeton University Princeton, NJ.

Logic Journal of the IGPL

|August 17, 2010

Summary

This summary is machine-generated.

This study introduces programmable neural networks to bridge symbolic and subsymbolic brain models. These models can simulate cognitive functions, including problem-solving behavior and the effects of brain damage.

Related Experiment Videos

Last Updated: Jun 10, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Mapping symbolic cognitive architectures to subsymbolic brain models is crucial for understanding complex cognition.
Existing models often struggle to integrate rule-based and connectionist approaches.

Purpose of the Study:

To propose parameterization techniques for building cognitive models as programmable, structured, recurrent neural networks.
To specify an interface between symbolic and subsymbolic descriptions of brain activity.

Main Methods:

Utilizing recurrent neural networks with adjustable feedback strengths.
Implementing parameterization techniques to control network component function (e.g., pattern recognition vs. logical rules).
Developing neural production systems capable of limited symbolic processing.

Main Results:

Demonstrated that feedback strength determines whether network components function as subsymbolic (pattern recognition) or symbolic (logical rules, digital memory).
Successfully implemented limited production systems within the neural network framework.
Showcased the potential to explain effects of brain damage on problem-solving behavior.

Conclusions:

The proposed techniques offer a viable method for integrating symbolic and subsymbolic cognitive architectures.
These neural production systems can leverage parallel processing principles to model cognitive functions.
This approach advances the understanding of neural mechanisms underlying complex cognition and its impairments.