Geometric Neural Networks

The representation of the external world in biological creatures appears to be deened in terms of geometry. This suggests that researchers should look for suitable mathematical systems with powerful geometric and algebraic characteristics. In such mathematical context the design and implementation of neural networks will be certainly more advantageous. This paper presents the generalization of feedfor-ward neural networks in the Cliiord or geometric algebra framework. The eeciency of the geometric neural nets indicate a step forward in the design of algorithms for multidimensional artiicial learning.