ニュース & イベント
会場：ひびきのキャンパス 講義室２
題目：「不要・冗長の削減原理：自然脳と人工脳の抽象化・一般化能力 （Principle of Redundancy Reduction (PRR): Abstraction-Generalization Ability）」
講師： Dr. Syozo Yasui (Kyushu Institute of Technology)
The brain of new-born babies has random connections for its neuronal system. Every neuron is connected with any other neurons. There is no obvious synaptic rule. Such randomness keeps increasing until the brain owner becomes several years old.
The randomness (synapse density) eventually reaches a peak. Thereafter, it begins to decrease and finally settle on a plateau.
The interpretation follows. Initially, connections are prepared as many as possible. However, through everyday life and experiences, it will be learned that not all such connections are necessary. Unnecessary and redundant ones are eliminated. The remaining ones are selected neural pathways that form a pruned slimy network.
In this talk, I will try to explain that such “skeleton brain” has a high ability of abstraction and generalization for cognitive science. This is called “Principle of Redundancy Reduction (PRR). Furthermore, applications of PRR to artificial intelligence will be described, where a neural ?network algorithm automatically leans PRR adaptively, so as to deal with changing environments.
