Abstract
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.
Highlights
The dependence of neuronal responses on stimulus- or movement-related parameters is often characterized by tuning curves
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
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
Summary
The dependence of neuronal responses on stimulus- or movement-related parameters is often characterized by tuning curves. A natural assumption is that a smooth, regular tuning curve reflects structured, orderly input to a neuron. Neuronal responses inevitably involve a degree of irregularity, even when responses are averaged across trials. Do such irregularities reflect noise in the inputs, or might they suggest something more complex such as unstructured input? We address this question using data recorded from primary motor cortex (M1) during an arm posture task, augmented by a neural network model of M1 neurons and their inputs. It is well suited to address the nature of neuronal tuning curves and their relationship to input. To reveal fine-scale tuning curve structure, we employed a task with 54 different arm postures, consisting of 27 target positions with two forearm rotation angles
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