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On building a memory evolutive system for application to learning and cognition modeling
Julio de Lima do Rego Monteiro1, Joao Eduardo Kogler, Joao Henrique Ranhel Ribeiro
1University of Sao Paulo, Escola Politecnica, Sao Paulo, SP, 05586-090, Brazil.
Advances in Experimental Medicine and Biology
|December 19, 2009
Summary
This study explores implementing a memory evolutive system (MES) using spiking neural networks. A pulsed artificial neural network (ANN) with spike-time-dependent plasticity meets requirements for category theory-based cognition representation.
Area of Science:
- Computational Neuroscience
- Cognitive Science
- Category Theory in AI
Background:
- Memory Evolutive Systems (MES) offer a framework for cognitive modeling.
- Category theory provides a formal language for representing cognitive structures.
- Artificial Neural Networks (ANNs) are explored for implementing complex cognitive architectures.
Purpose of the Study:
- To investigate the implementation of a Memory Evolutive System (MES) using simulated spiking neural networks.
- To evaluate the applicability of category theory in representing cognition within the MES architecture.
- To determine the minimum requirements for ANNs to support MES functionalities.
Main Methods:
- Simulated network of spiking neurons with time-dependent plasticity.
- Application of category theory principles to cognitive representation.
- Analysis of Izhikevich's formal neuron model with STDP (spike-time-dependent plasticity).
Main Results:
- A pulsed ANN based on Izhikevich neurons and STDP demonstrates sufficient dynamical properties for MES requirements.
- Challenges and advantages of using category theory for cognitive representation in MES are identified.
- Topological requirements for the ANN are highlighted as a key consideration.
Conclusions:
- Spiking neural networks with STDP are viable for implementing category theory-based cognitive systems.
- Further research is needed on ANN topology to fully realize MES potential.
- The study validates a computational approach to understanding memory and cognition.
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