Bruno Apolloni, Simone Bassis, Lorenzo Valerio


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.


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Paper Citation

in Harvard Style

Apolloni B., Bassis S. and Valerio L. (2011). KINETIC MORPHOGENESIS OF A MULTILAYER PERCEPTRON . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 99-105. DOI: 10.5220/0003642800990105

in Bibtex Style

author={Bruno Apolloni and Simone Bassis and Lorenzo Valerio},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
SN - 978-989-8425-84-3
AU - Apolloni B.
AU - Bassis S.
AU - Valerio L.
PY - 2011
SP - 99
EP - 105
DO - 10.5220/0003642800990105