Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity

Neuronal responses characterized by regular tuning curves are typically assumed to arise from structured synaptic connectivity. However, many responses exhibit both regular and irregular components. To address the relationship between tuning curve properties and underlying circuitry, we analyzed neuronal activity recorded from primary motor cortex (M1) of monkeys performing a 3D arm posture control task and compared the results with a neural network model. Posture control is well suited for examining M1 neuronal tuning because it avoids the dynamic complexity of time-varying movements. As a function of hand position, the neuronal responses have a linear component, as has previously been described, as well as heterogeneous and highly irregular nonlinearities. These nonlinear components involve high spatial frequencies and therefore do not support explicit encoding of movement parameters. Yet both the linear and nonlinear components contribute to the decoding of EMG of major muscles used in the task. Remarkably, despite the presence of a strong linear component, a feedforward neural network model with entirely random connectivity can replicate the data, including both the mean and distributions of the linear and nonlinear components as well as several other features of the neuronal responses. This result shows that smoothness provided by the regularity in the inputs to M1 can impose apparent structure on neural responses, in this case a strong linear (also known as cosine) tuning component, even in the absence of ordered synaptic connectivity.
Author Summary Relationships between the activity of single neurons and experimental parameters are often characterized by functions called tuning curves. Regular tuning-curve shapes are typically assumed to arise from structure in the synaptic inputs to each neuron. We found that the activities of neurons in primary motor cortex during an arm posture task exhibit both a regular component that fits a well-known tuning curve description, and heterogeneous irregular components that do not. Such complex components are often assumed to reflect residual noise. However, both the regular and irregular components are needed to optimally decode the commands that guide the muscles used in the task. We then asked what type of input structure was needed to generate neuronal responses with both regular and irregular elements. We constructed and analyzed a mathematical model, based on known physiology of the relevant brain regions that replicates the full spectrum of recorded neuronal responses. Surprisingly, the synaptic connectivity in this model is completely random.