Topic: muscle co-activations

Motor control is a complex, high-dimensional task. It has been suggested that the brain may reduce the dimensionality of this control problem by using a set of motor primitives (or muscle synergies) that encode frequently used patterns of muscle co-activations in the time and/or weight-domain (Bizzi et al., 1991; Kutch and Valero-Cuevas, 2012; Bizzi and Cheung, 2013; Alessandro et al., 2013). In this project we investigate how motor primitives may affect the learning of movement in motor cortex. We model motor cortex as a recurrent neural network (RNN) and compare two potential output scenario