Stanley Phillips Gotshall

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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,(More)
This paper presents a biologically realistic model of the spino-neuromuscular system (SNMS). The model uses a pulsecoded recurrent neural network to control a simulated humanlike arm. We use a genetic algorithm to train the network based on a target behavior for the arm. Our goal is to create a useful model for studying the function and behavior of neural(More)
A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems that processes sensory information in a way similar to humans. Artificial neural networks have been an increasingly popular option for thisbecause they consist of processing units(More)
This paper demonstrates the effectiveness of genetic algorithms in training stable behavior in a model of the spino-neuromuscular system (SNMS). In particular, we test the stability of trained instances of the model with respect to unfamiliar control signals and untrained forearm weights. The results show that small changes to the input frequency and(More)
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