Genetic Programming and Evolvable Machines

, Volume 8, Issue 4, pp 355–380 | Cite as

Stochastic optimization of a biologically plausible spino-neuromuscular system model

A comparison with human subjects
  • Stanley Gotshall
  • Kathy Browder
  • Jessica Sampson
  • Terence Soule
  • Richard Wells
Original Paper


Simulations and modeling techniques are becoming increasingly important in understanding the behavior of biological systems. Detailed models help researchers answer questions in diverse areas such as the behavior of bacteria and viruses and aiding in the diagnosis and treatment of injuries and diseases. However, to yield meaningful biological behavior, biological simulations often include hundreds of parameters that correspond to biological components and characteristics. This paper demonstrates the effectiveness of genetic algorithms (GA) and particle swarm optimizer (PSO) based techniques in training biologically plausible behavior in a neuromuscular simulation of a biceps/triceps pair. The results are compared to human subjects during flexion/extension movements to show that these algorithms are effective in training biologically plausible behaviors on both neural and gross anatomical levels. Specific behaviors of interest that emerge include tonic tensions in both muscles during resting periods, biceps/triceps coactivation patterns, and recruitment-like behaviors. These are all fundamental characteristics of biological motor control and emerge without direct selection for these behaviors. This is the first time that all of these characteristic behaviors emerge in a model of this detail without direct selective pressure.


Biological neural networks Genetic algorithms Particle swarm optimizers Breeding swarm optimizers 


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Stanley Gotshall
    • 1
  • Kathy Browder
    • 2
  • Jessica Sampson
    • 3
  • Terence Soule
    • 1
  • Richard Wells
    • 4
  1. 1.Department of Computer ScienceUniversity of IdahoMoscowUSA
  2. 2.Department of Health, Physical Education, Recreation, and DanceUniversity of IdahoMoscowUSA
  3. 3.Department of Mechanical EngineeringUniversity of IdahoMoscowUSA
  4. 4.Department of Electrical and Computer EngineeringUniversity of IdahoMoscowUSA

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