Neural cell as a primary fuzzy unit
Uziel Sandler, Lev E. Tsitolovsky · International Conference on Systems · 2008
Neurons in various brain regions change their activity correspondingly to a learning procedure and in special experiments learning can be artificially directed to a restricted population of neurons and even to a single neuron. Moreover, in exceptional cases, a single neuron has power over an entire behavior. Numerous literature's sources and our original experiments considered in this book, allow us to believe that capability of perceiving could take place already on a neuron level. Which does a neural cell uses for description of an environment and for decision-making? In this paper we argue that the subjective attitude of the brain to an expected event can be the reason for the advent of the brain's of perception. Mathematical basis for of was proposed by L. Zadeh almost half century ago and one was named as Fuzzy Logic. A real neuron is a complex dynamic system, so for description of its behavior we need an extension of computing of perceptions on evolutionary processes: evolution of perceptions. Such a theory called Fuzzy Dynamics has been developed and studied during the last decade. We show that the fuzzy dynamics approach leads to good compatibility with experimental observations and allows understanding some basic features of the neuron being. It is very likely that the inner logic of the neuron's behavior is close to fuzzy logic. This approach open the way for design of an animal or feeling robot, which is able to use a trial-and-error self-learning process based on artificial motivations and effectively adapt (like an animal) its behavior in suddenly changing environmental conditions.