人工神経回路網ハイパーコラムモデルにおける組合せ学習ならびに連想学習 Combinatorial Learning and Associative Learning in Hyper-Column Model
Hyper-Column Model (HCM) is a self-organized, competitive and hierarchical multilayer neural network. It is derived from the Neocognitron by replacing each <i>S</i> cell and <i>C</i> cell with a two layer Hierarchical Self-Organizing Map (HSOM). HCM can recognize images with variant object size, position, orientation and spatial resolution. In this paper, we propose two new learning methods; "Combinatorial Learning, " and "Associative Learning". The former enables HCM to learn a pattern of winner neurons which are activated in each HSOM with excitatory lateral connections. HCM is expanded to a supervised learnable model by the latter learning algorithm.
- 日本神経回路学会誌 = The Brain & neural networks
日本神経回路学会誌 = The Brain & neural networks 13(4), 129-136, 2006-12-05