From Biological Synapses to Intelligent Robots

TL;DR

This review discusses how biologically inspired Hebbian synaptic learning models can enhance adaptive, unsupervised control and sensing in intelligent robots, drawing from neural mechanisms observed in biological systems.

Contribution

It highlights the potential of Hebbian learning as a foundational model for developing adaptive, self-organizing robotic control systems inspired by biological neural networks.

Findings

Hebbian learning supports unsupervised adaptation in robots.

Biological neural mechanisms inform robust control architectures.

Synaptic plasticity enables functional complexity in robotic systems.

Abstract

This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival or task relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for intelligent robotics and sensor systems. Keywords: Hebbian learning, synaptic plasticity, neural networks, self organization, brain, reinforcement, sensory processing, robot control