Artificial Neural Networks as Models of Robustness in Development and Regeneration: Stability of Memory During Morphological Remodeling

  • Jennifer Hammelman
  • Daniel Lobo
  • Michael Levin
Part of the Studies in Computational Intelligence book series (SCI, volume 628)


Artificial neural networks are both a well-established tool in machine learning and a mathematical model of distributed information processing. Developmental and regenerative biology is in desperate need of conceptual models to explain how some species retain memories despite drastic reorganization, remodeling, or regeneration of the brain. Here, we formalize a method of artificial neural network perturbation and quantitatively analyze memory persistence during different types of topology change. We introduce this system as a computational model of the complex information processing mechanisms that allow memories to persist during significant cellular and morphological turnover in the brain. We found that perturbations in artificial neural networks have a general negative effect on the preservation of memory, but that the removal of neurons with different firing patterns can effectively minimize this memory loss. The training algorithms employed and the difficulty of the pattern recognition problem tested are key factors determining the impact of perturbations. The results show that certain perturbations, such as neuron splitting and scaling, can achieve memory persistence by functional recovery of lost patterning information. The study of models integrating both growth and reduction, combined with distributed information processing is an essential first step for a computational theory of pattern formation, plasticity, and robustness.


Artificial Neural Network Hide Neuron Pattern Recognition Problem Tangent Sigmoid Transfer Function Connection Blocking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank the Levin lab, Francisco J. Vico, and many others in the community for helpful discussions at the intersection of neuroscience and developmental biology. This work was supported by NSF (subaward #CBET-0939511 via EBICS at MIT), the G. Harold and Leila Y. Mathers Charitable, and Templeton World Charity Foundations.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jennifer Hammelman
    • 1
  • Daniel Lobo
    • 2
  • Michael Levin
    • 1
  1. 1.Biology Department, School of Arts and ScienceTufts UniversityMedfordUSA
  2. 2.Department of Biological SciencesUniversity of Maryland, Baltimore CountyBaltimoreUSA

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