Artificial neuron for artificial intelligence system, has multiple dendrites with power series of weights having associated counts for associated powers.

Associative neuron for artificial neural network - forms sum of received auxiliary input signals weighted by coefficients, and applies non-linear function to weighted sum to generate non-linear signal.

Abstract: We present a generalization of conventional artificial neural networks that allows for a functional equivalence to multi-expert systems. The new model provides an architectural freedom going beyond existing multi-expert models and an integrative formalism to compare and combine various techniques of learning. (We consider gradient, EM, reinforcement, and unsupervised learning.) Its uniform representation aims at a simple genetic encoding and evolutionary structure optimization of multi-expert systems. This paper contains a detailed description of the model and learning rules, empirically validates its functionality, and discusses future perspectives.

"Signal transmission method for classification system, involves extracting functional and membership components from input signal "