Stochastic optimization of a biologically plausible spino-neuromuscular system model   [SO]

by

Gotshall, S., Browder, K., Sampson, J., Soule, T. and Wells, R.

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Info: Genetic Programming and Evolvable Machines (Journal), 2007, p. 355-380
Keywords:genetic algorithms, Biological neural networks, Particle swarm optimisers, PSO, Breeding swarm optimisers
Abstract:
Simulations and modelling techniques are becoming increasingly important in understanding the behaviour 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 behaviour, biological simulations [BS] often include hundreds of parameters that correspond to biological components and characteristics. This paper demonstrates the effectiveness of genetic algorithms (GA) [GA] [GAG] and particle swarm [PS] optimizer (PSO) based techniques in training biologically plausible behaviour 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 behaviours 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 behaviours. These are all fundamental characteristics of biological motor control and emerge without direct selection for these behaviours. This is the first time that all of these characteristic behaviours emerge in a model of this detail without direct selective pressure. [SP]
Notes:
special issue on medical applications of Genetic and Evolutionary Computation See Erratum cite{Gotshall:2011:GPEM}
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BibTex:
@Article{Gotshall:2007:GPEM,
  author =       "Stanley Gotshall and Kathy Browder and Jessica Sampson
                 and Terence Soule and Richard Wells",
  title =        "Stochastic optimization of a biologically plausible
                 spino-neuromuscular system model",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2007",
  volume =       "8",
  number =       "4",
  pages =        "355--380",
  month =        dec,
  note =         "special issue on medical applications of Genetic and
                 Evolutionary Computation",
  keywords =     "genetic algorithms, Biological neural networks,
                 Particle swarm optimisers, PSO, Breeding swarm
                 optimisers",
  ISSN =         "1389-2576",
  doi =          "doi:10.1007/s10710-007-9044-8",
  abstract =     "Simulations and modelling techniques are becoming
                 increasingly important in understanding the behaviour
                 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 behaviour, 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 behaviour
                 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 behaviours 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 behaviours. These are all
                 fundamental characteristics of biological motor control
                 and emerge without direct selection for these
                 behaviours. This is the first time that all of these
                 characteristic behaviours emerge in a model of this
                 detail without direct selective pressure.",
  notes =        "See Erratum cite{Gotshall:2011:GPEM}",
}