Extrinsic and intrinsic dynamics in movement intermittency.

What determines how we move in the world? Motor neuroscience often focusses either on intrinsic rhythmical properties of motor circuits or extrinsic sensorimotor feedback loops. Here we show that the interplay of both intrinsic and extrinsic dynamics is required to explain the intermittency observed in continuous tracking movements. Using spatiotemporal perturbations in humans, we demonstrate that apparently discrete submovements made 2-3 times per second reflect constructive interference between motor errors and continuous feedback corrections that are filtered by intrinsic circuitry in the motor system. Local field potentials in monkey motor cortex revealed characteristic signatures of a Kalman filter, giving rise to both low-frequency cortical cycles during movement, and delta oscillations during sleep. We interpret these results within the framework of optimal feedback control, and suggest that the intrinsic rhythmicity of motor cortical networks reflects an internal model of external dynamics, which is used for state estimation during feedback-guided movement. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Decoding Local Field Potentials for Neural Interfaces.

The stability and frequency content of local field potentials (LFPs) offer key advantages for long-term, low-power neural interfaces. However, interpreting LFPs may require new signal processing techniques which should be informed by a scientific understanding of how these recordings arise from the coordinated activity of underlying neuronal populations. We review current approaches to decoding LFPs for brain-machine interface (BMI) applications, and suggest several directions for future research. To facilitate an improved understanding of the relationship between LFPs and spike activity, we share a dataset of multielectrode recordings from monkey motor cortex, and describe two unsupervised analysis methods we have explored for extracting a low-dimensional feature space that is amenable to biomimetic decoding and biofeedback training.

Real-time estimation and biofeedback of single-neuron firing rates using local field potentials.

The long-term stability and low-frequency composition of local field potentials (LFPs) offer important advantages for robust and efficient neuroprostheses. However, cortical LFPs recorded by multi-electrode arrays are often assumed to contain only redundant information arising from the activity of large neuronal populations. Here we show that multichannel LFPs in monkey motor cortex each contain a slightly different mixture of distinctive slow potentials that accompany neuronal firing. As a result, the firing rates of individual neurons can be estimated with surprising accuracy. We implemented this method in a real-time biofeedback brain-machine interface, and found that monkeys could learn to modulate the activity of arbitrary neurons using feedback derived solely from LFPs. These findings provide a principled method for monitoring individual neurons without long-term recording of action potentials.

A common structure underlies low-frequency cortical dynamics in movement, sleep, and sedation.

Upper-limb movements are often composed of regular submovements, and neural correlates of submovement frequencies between 1 and 4 Hz have been found in the motor cortex. The temporal profile of movements is usually assumed to be determined by extrinsic factors such as limb biomechanics and feedback delays, but another possibility is that an intrinsic rhythmicity contributes to low frequencies in behavior. We used multielectrode recordings in monkeys performing an isometric movement task to reveal cyclic activity in primary motor cortex locked to submovements, and a distinct oscillation in premotor cortex. During ketamine sedation and natural sleep, cortical activity traversed similar cycles and became synchronized across areas. Because the same cortical dynamics are coupled to submovements and also observed in the absence of behavior, we conclude that the motor networks controlling the upper limb exhibit an intrinsic periodicity at submovement frequencies that is reflected in the speed profile of movements.

Generalizability of EMG decoding using local field potentials.

Motor cortical local field potentials (LFPs) have been successfully used to decode both kinematics and kinetics of arm movement. For future clinically viable prostheses, however, brain activity decoders will have to generalize well under a wide spectrum of behavioral conditions. This property has not yet been demonstrated clearly. Here, we provide evidence for the first time, that an LFP-based electromyogram (EMG) decoder can generalize reasonably well across two different types of behavior. We implanted intracortical microelectrode arrays in the primary motor (M1) and ventral pre-motor (PMv) cortices of a rhesus macaque, and recorded LFP and EMG activity from arm and hand muscles of the contralateral forelimb during a two-dimensional (2-D) centre-out isometric wrist torque task (TT), and during free reach and grasp behavior (FB). Selected temporal and spectral features of the LFP signals were used to train EMG decoders using data from both types of behavior separately. We assessed the decoding performance for both within- and across-task cases. The average achieved generalization score was 65 ± 20%, while in many cases individual scores reached 100%.

Plasticity of temporal pattern codes for vocalization stimuli in primary auditory cortex.

It has been suggested that "call-selective" neurons may play an important role in the encoding of vocalizations in primary auditory cortex (A1). For example, marmoset A1 neurons often respond more vigorously to natural than to time-reversed twitter calls, although the spectral energy distribution in the natural and time-reversed signals is the same. Neurons recorded in cat A1, in contrast, showed no such selectivity for natural marmoset calls. To investigate whether call selectivity in A1 can arise purely as a result of auditory experience, we recorded responses to marmoset calls in A1 of naive ferrets, as well as in ferrets that had been trained to recognize these natural marmoset calls. We found that training did not induce call selectivity for the trained vocalizations in A1. However, although ferret A1 neurons were not call selective, they efficiently represented the vocalizations through temporal pattern codes, and trained animals recognized marmoset twitters with a high degree of accuracy. These temporal patterns needed to be analyzed at timescales of 10-50 ms to ensure efficient decoding. Training led to a substantial increase in the amount of information transmitted by these temporal discharge patterns, but the fundamental nature of the temporal pattern code remained unaltered. These results emphasize the importance of temporal discharge patterns and cast doubt on the functional significance of call-selective neurons in the processing of animal communication sounds at the level of A1.