Authors:
Bruno Apolloni
;
Simone Bassis
and
Lorenzo Valerio
Affiliation:
University of Milan, Italy
Keyword(s):
Neural network morphogenesis, Mobile neurons, Deep multilayer perceptrons, Eulerian dynamics.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Architectures and Mechanisms
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
We introduce a morphogenesis paradigm for a neural network where neurons are allowed to move autonomously in a topological space to reach suitable reciprocal positions under an informative perspective. To this end, a neuron is attracted by the mates which are most informative and repelled by those which are most similar to it. We manage the neuron motion with a Newtonian dynamics in a subspace of a framework where topological coordinates match with those reckoning the neuron connection weights. As a result, we have a synergistic plasticity of the network which is ruled by an extended Lagrangian where physics components merge with the common error terms. With the focus on a multilayer perceptron, this plasticity is operated by an extension of the standard back-propagation algorithm which proves robust even in the case of deep architectures. We use two classic benchmarks to gain some insights on the morphology and plasticity we are proposing.