Stochastic optimization of a biologically plausible spino-neuromuscular system model

Abstract

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.

Evolving stable behavior in a spino-neuromuscular system model

“…Rather, that it produces a stable system that generalizes its behavior for a variety of tasks. The spino-neuromuscular system (SNMS) model used in these and previous [3] experiments consists of two groups of muscle fibers corresponding to the biceps and triceps ( Figure 1). Each muscle fiber is controlled by a relatively small neural network that processes information from the muscle fibers and other feedback pathways.…”