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Reactive, Proactive, and Inductive Agents: An Evolutionary Path for Biological and Artificial Spiking Networks
Lana Sinapayen1,2, Atsushi Masumori3, Takashi Ikegami3
1Sony Computer Science Laboratories, Inc., Tokyo, Japan.
Frontiers in Computational Neuroscience
|February 11, 2020
Summary
Organisms evolve proactive behaviors by developing predictive abilities in neural networks. This study outlines evolutionary steps and conditions for neural networks to transition from reactive to predictive strategies.
Area of Science:
- Computational neuroscience
- Evolutionary algorithms
- Artificial intelligence
Background:
- Complex environments demand organisms anticipate stimuli consequences.
- Reactive behavior is insufficient for optimal information exploitation.
- Evolutionary pathways can lead to proactive and inductive behaviors.
Purpose of the Study:
- To propose an evolutionary path for neural networks from reactive to proactive and inductive behaviors.
- To define conditions for embodied neural networks to evolve predictive abilities.
Main Methods:
- Simulations of neural networks with spike-timing dependent plasticity.
- Analysis of evolutionary steps from reactive to proactive behavior.
- Identification of necessary conditions for predictive and inductive learning.
Main Results:
- Neural networks can evolve from reactive to proactive strategies.
- Four specific conditions are necessary for this evolution.
- Spike-timing dependent plasticity is crucial for developing predictive abilities.
Conclusions:
- Embodied neural networks can evolve predictive and inductive capabilities.
- Evolutionary steps are identifiable for transitioning from reactive to proactive behavior.
- The study provides a framework for understanding the evolution of intelligence in artificial systems.
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