---
title: "On Multi-Layered Connectionist Models: Adding Layers vs. Increasing Width"
authors:
  - "Chung-Jen Ho"
doi: ""
url: "https://ijcai.org/proceedings/1989/28"
pdf: "https://ijcai.org/proceedings/1989/0028.pdf"
venue: "Proceedings of the Eleventh International Joint Conference on Artificial Intelligence"
year: 1989
pages: "176-178"
track: "Parallel and Distributed Processing"
keywords:
---

# On Multi-Layered Connectionist Models: Adding Layers vs. Increasing Width

Chung-Jen Ho

*Proceedings of the Eleventh International Joint Conference on Artificial Intelligence*, pages 176-178.

## Abstract

In this paper, we explore the computational potential and limitations of the multi-layered connectionist models [Minsky and Papert, 1968]. We found that the number of layers and the width are two crucial parameters for the multilayered connectionist models. If each layer has the same size nand we increment the number of layers by 1, then the number of problems solved will increase O(nn 3 ) times. On the other hand, suppos e the number of layers is equal to 2. If we increment the width by 1, then the number of problem solved will increase O(nn 2 ) times, where n is the input size. Hence, we can extend a 2-layered connectionist model by adding layers or increasing width. Our conclusion is that increasing width is better than adding layers.