A GPU-Accelerated Deep Reinforcement Learning Pipeline for Simulating Animal Behavior
Charles Zhang, Elliott Abe, Jason Foat, Bing Brunton, Talmo Pereira, Bence Olveczky, Emil Warnberg · COSYNE 2025
To study how motor circuits control bodies in robust, adaptive, and flexible ways, we often break down the complex control system into individual components to be studied in isolation. However, it is increasingly recognized that we need to consider biomechanically realistic models when interpreting the neural computations underlying movement and motor learning. Training an artificial neural network (ANN) to control a biomechanical model and have it reproduce the behavior of real animals allows the probing of neural control of movement in a fully transparent and configurable system. To make thi
ePoster · Neuroscience