Project Summary Any interaction between an organism and a static external world requires a motor behavior, from basic functions such as locomotion to the precise, highly-trained movements performed by athletes and musicians. As a critical node in the nervous system involved in voluntary motor control, the primary motor cortex (M1) provides the opportunity to investigate neuronal computations and their outputs at the final stage of cortical processing before movement. Studies of primate reaching that consider features of neuronal activity beyond population vectors of single-neuron tuning properties often emphasize the role of structured population dynamics in producing motor behavior. However, the dimensionality reduction techniques used to characterize these dynamics discard all information regarding the identity, role, and tuning properties of single neurons, thereby hindering efforts to understand the function of individual neurons within the context of the population. In order to integrate the neuron-centric and network perspectives on computations in motor cortex, encoding models must be developed that can predict fine-timing spike activity by accounting for movement kinematics, layer-specific properties, and population activity. Network science – the study of complex networks – provides a path toward this goal. Network activity will be summarized by functional networks (FNs) that maintain neuron- specific labels while simultaneously capturing all pairwise correlations between neurons. We will use FNs to investigate the role of network interactions conditioned on neuron-specific features, particularly cortical depth and whether it responds preferentially during particular movements (tuned) or not (untuned). I propose the use of unrestrained common marmosets to study single-neuron and network-based representations of naturalistic motor behaviors. I will model trajectory encoding of single neurons in deep and superficial layers, quantify functional connections between neurons within a cortical column and across the cortical sheet, and examine the role of tuned and untuned neurons in shaping the encoding properties of functionally connected units. By leveraging a model that combines temporally precise kinematic encoding with quantified network interactions, we can place single neuron properties in the context of population dynamics and gain insight into the activity patterns that produce natural motor behaviors. Furthermore, the study of unconstrained behavior will produce an improved model that can account for complex, naturalistic movements. This has implications for brain- machine interface control algorithms, where a deeper understanding of the encoding properties and network interactions that produce a rich motor repertoire may provide the foundation for algorithms that achieve dexterous movement in a long-term, unconstrained setting. I seek to accomplish these goals and gain valuable training in sound experimental design, computational ana...