Correlation-based model of artificially induced plasticity in motor cortex by a bidirectional brain-computer interface.

Experiments show that spike-triggered stimulation performed with Bidirectional Brain-Computer-Interfaces (BBCI) can artificially strengthen connections between separate neural sites in motor cortex (MC). When spikes from a neuron recorded at one MC site trigger stimuli at a second target site after a fixed delay, the connections between sites eventually strengthen. It was also found that effective spike-stimulus delays are consistent with experimentally derived spike-timing-dependent plasticity (STDP) rules, suggesting that STDP is key to drive these changes. However, the impact of STDP at the level of circuits, and the mechanisms governing its modification with neural implants remain poorly understood. The present work describes a recurrent neural network model with probabilistic spiking mechanisms and plastic synapses capable of capturing both neural and synaptic activity statistics relevant to BBCI conditioning protocols. Our model successfully reproduces key experimental results, both established and new, and offers mechanistic insights into spike-triggered conditioning. Using analytical calculations and numerical simulations, we derive optimal operational regimes for BBCIs, and formulate predictions concerning the efficacy of spike-triggered conditioning in different regimes of cortical activity.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields.

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Colored context cues can facilitate the ability to learn and to switch between multiple dynamical force fields.

We tested the efficacy of color context cues during adaptation to dynamic force fields. Four groups of human subjects performed elbow flexion/extension movements to move a cursor between targets on a monitor while encountering a resistive (Vr) or assistive (Va) viscous force field. They performed two training sets of 256 trials daily, for 10 days. The monitor background color changed (red, green) every four successful trials but provided different degrees of force field context information to each group. For the irrelevant-cue groups, the color changed every four trials, but one group encountered only the Va field and the other only the Vr field. For the reliable-cue group, the force field alternated between Va and Vr each time the monitor changed color (Vr, red; Va, green). For the unreliable-cue group, the force field changed between Va and Vr pseudorandomly at each color change. All subjects made increasingly stereotyped movements over 10 training days. Reliable-cue subjects typically learned the association between color cues and fields and began to make predictive changes in motor output at each color change during the first day. Their performance continued to improve over the remaining days. Unreliable-cue subjects also improved their performance across training days but developed a strategy of probing the nature of the field at each color change by emitting a default motor response and then adjusting their motor output in subsequent trials. These findings show that subjects can extract explicit and implicit information from color context cues during force field adaptation.

Sequential activation of motor cortical neurons contributes to intralimb coordination during reaching in the cat by modulating muscle synergies.

We examined the contribution of the motor cortex to the control of intralimb coordination during reaching in the standing cat. We recorded the activity of 151 pyramidal tract neurons (PTNs) in the forelimb representation of three cats during a task in which the cat reached forward from a standing position to press a lever. We simultaneously recorded the activity of muscles in the contralateral forelimb acting around each of the major joints. Cell activity was recorded with and without the presence of an obstacle requiring a modification of limb trajectory. The majority of the PTNs (134/151, 89%) modulated their discharge activity at some period of the reach while 84/151 (56%) exhibited a significant peak or trough of activity as the limb was transported from its initial position to the lever. These phasic changes of activity were distributed sequentially throughout the transport phase. A cluster analysis of muscle activity in two of the cats showed the presence of five muscle synergies during this transport period. One of the synergies was related to the lift of the paw from the support surface, two to flexion of the limb and dorsiflexion of the paw, one to preparation for contact with the lever, and one to the transport of the entire limb forward; a sixth synergy was activated during the lever press. An analysis of the phase of cell activity with respect to the phase of activity of muscles selected to represent each of these synergies showed that different populations of PTNs were activated sequentially and coincidentally with each synergy. We suggest that this sequential activation of populations of PTNs is compatible with a contribution to the initiation and modulation of functionally distinct groups of synergistic muscles and ultimately serves to ensure the appropriate multiarticular, intralimb coordination of the limb during reaching.

Muscle synergies during locomotion in the cat: a model for motor cortex control.

It is well established that the motor cortex makes an important contribution to the control of visually guided gait modifications, such as those required to step over an obstacle. However, it is less clear how the descending cortical signal interacts with the interneuronal networks in the spinal cord to ensure that precise changes in limb trajectory are appropriately incorporated into the base locomotor rhythm. Here we suggest that subpopulations of motor cortical neurones, active sequentially during the step cycle, may regulate the activity of small groups of synergistic muscles, likewise active sequentially throughout the step cycle. These synergies, identified by a novel associative cluster analysis, are defined by periods of muscle activity that are coextensive with respect to the onset and offset of the EMG activity. Moreover, the synergies are sparse and are frequently composed of muscles acting around more than one joint. During gait modifications, we suggest that subpopulations of motor cortical neurones may modify the magnitude and phase of the EMG activity of all muscles contained within a given synergy. Different limb trajectories would be produced by differentially modifying the activity in each synergy thus providing a flexible substrate for the control of intralimb coordination during locomotion.

From Squid to Mammals with the HH Model through the Nav Channels' Half-Activation-Voltage Parameter.

The model family analyzed in this work stems from the classical Hodgkin-Huxley model (HHM). for a single-compartment (space-clamp) and continuous variation of the voltage-gated sodium channels (Nav) half-activation-voltage parameter ΔV1/2, which controls the window of sodium-influx currents. Unlike the baseline HHM, its parametric extension exhibits a richer multitude of dynamic regimes, such as multiple fixed points (FP's), bi- and multi-stability (coexistence of FP's and/or periodic orbits). Such diversity correlates with a number of functional properties of excitable neural tissue, such as the capacity or not to evoke an action potential (AP) from the resting state, by applying a minimal absolute rheobase current amplitude. The utility of the HHM rooted in the giant squid for the descriptions of the mammalian nervous system is of topical interest. We conclude that the model's fundamental principles are still valid (up to using appropriate parameter values) for warmer-blooded species, without a pressing need for a substantial revision of the mathematical formulation. We demonstrate clearly that the continuous variation of the ΔV1/2 parameter comes close to being equivalent with recent HHM 'optimizations'. The neural dynamics phenomena described here are nontrivial. The model family analyzed in this work contains the classical HHM as a special case. The validity and applicability of the HHM to mammalian neurons can be achieved by picking the appropriate ΔV1/2 parameter in a significantly broad range of values. For such large variations, in contrast to the classical HHM, the h and n gates' dynamics may be uncoupled--i.e. the n gates may no longer be considered in mere linear correspondence to the h gates. ΔV1/2 variation leads to a multitude of dynamic regimes--e.g. models with either 1 fixed point (FP) or with 3 FP's. These may also coexist with stable and/or unstable periodic orbits. Hence, depending on the initial conditions, the system may behave as either purely excitable or as an oscillator. ΔV1/2 variation leads to significant changes in the metabolic efficiency of an action potential (AP). Lower ΔV1/2 values yield a larger range of AP response frequencies, and hence provide for more flexible neural coding. Such lower values also contribute to faster AP conduction velocities along neural fibers of otherwise comparable-diameter. The 3 FP case brings about an absolute rheobase current. In comparison in the classical HHM the rheobase current is only relative--i.e. excitability is lost after a finite amount of elapsed stimulation time. Lower ΔV1/2 values translate in lower threshold currents from the resting state.

Energy-optimal electrical-stimulation pulses shaped by the Least-Action Principle.

Electrical stimulation (ES) devices interact with excitable neural tissue toward eliciting action potentials (AP's) by specific current patterns. Low-energy ES prevents tissue damage and loss of specificity. Hence to identify optimal stimulation-current waveforms is a relevant problem, whose solution may have significant impact on the related medical (e.g. minimized side-effects) and engineering (e.g. maximized battery-life) efficiency. This has typically been addressed by simulation (of a given excitable-tissue model) and iterative numerical optimization with hard discontinuous constraints--e.g. AP's are all-or-none phenomena. Such approach is computationally expensive, while the solution is uncertain--e.g. may converge to local-only energy-minima and be model-specific. We exploit the Least-Action Principle (LAP). First, we derive in closed form the general template of the membrane-potential's temporal trajectory, which minimizes the ES energy integral over time and over any space-clamp ionic current model. From the given model we then obtain the specific energy-efficient current waveform, which is demonstrated to be globally optimal. The solution is model-independent by construction. We illustrate the approach by a broad set of example situations with some of the most popular ionic current models from the literature. The proposed approach may result in the significant improvement of solution efficiency: cumbersome and uncertain iteration is replaced by a single quadrature of a system of ordinary differential equations. The approach is further validated by enabling a general comparison to the conventional simulation and optimization results from the literature, including one of our own, based on finite-horizon optimal control. Applying the LAP also resulted in a number of general ES optimality principles. One such succinct observation is that ES with long pulse durations is much more sensitive to the pulse's shape whereas a rectangular pulse is most frequently optimal for short pulse durations.

Motor cortical regulation of sparse synergies provides a framework for the flexible control of precision walking.

We have previously described a modular organization of the locomotor step cycle in the cat in which a number of sparse synergies are activated sequentially during the swing phase of the step cycle (Krouchev et al., 2006). Here, we address how these synergies are modified during voluntary gait modifications. Data were analysed from 27 bursts of muscle activity (recorded from 18 muscles) recorded in the forelimb of the cat during locomotion. These were grouped into 10 clusters, or synergies, during unobstructed locomotion. Each synergy was comprised of only a small number of muscles bursts (sparse synergies), some of which included both proximal and distal muscles. Eight (8/10) of these synergies were active during the swing phase of locomotion. Synergies observed during the gait modifications were very similar to those observed during unobstructed locomotion. Constraining these synergies to be identical in both the lead (first forelimb to pass over the obstacle) and the trail (second limb) conditions allowed us to compare the changes in phase and magnitude of the synergies required to modify gait. In the lead condition, changes were observed particularly in those synergies responsible for transport of the limb and preparation for landing. During the trail condition, changes were particularly evident in those synergies responsible for lifting the limb from the ground at the onset of the swing phase. These changes in phase and magnitude were adapted to the size and shape of the obstacle over which the cat stepped. These results demonstrate that by modifying the phase and magnitude of a finite number of muscle synergies, each comprised of a small number of simultaneously active muscles, descending control signals could produce very specific modifications in limb trajectory during locomotion. We discuss the possibility that these changes in phase and magnitude could be produced by changes in the activity of neurones in the motor cortex.

Principal component analysis of M1 neurophysiology data suggests a motor-control system-architecture template.

Stereotyped reaching tasks are used to study how primate subjects learn and recall motor skills required to compensate for different external forces during arm movements. To unveil mechanisms accounting for skilled performance under a wide range of rapidly switching task dynamics conditions, we recorded neural data from the primary motor-cortex (M1). Here we present a systematic analysis of changes in the M1 activity of a monkey with extensive practice compensating for five different dynamic fields in an elbow flexion/extension task. We show how they reflect differences in task kinematics and dynamics. Making extensive use of principal component analysis (PCA) and in preparation for computational modeling (see the companion paper) we demonstrate how M1 activity can be related functionally to the dynamics of feed-forward (FF), fast- and slow- feedback (FB) loops of the adaptive controller implemented by the brain to guide skilled motor behavior.

A functional approach to modeling M1 single-unit activity recorded in three primate motor control studies.

When monkeys make movements with or without external force perturbations, or generate isometric forces in different directions from different workspace positions, primary motor cortex (M1) cell activity shows systematic changes in directional tuning and in force-generation gains as a function of arm posture. However, it may be simplistic to assume most control intelligence is in the cortex while the brainstem and especially the spinal cord do little more than passively implement pontifical descending commands. More recent studies like [1-4] do suggest a different perspective. Furthermore, systematic changes in directionality of M1 cell and limb muscle EMG activity may stem partly from the feedback (aka reflex) loops, physical properties of limb biomechanics, muscle anisotropy and force production nonlinearities, and their interplay with task conditions, and not only due to predictive feedforward central commands.

Sequential activation of muscle synergies during locomotion in the intact cat as revealed by cluster analysis and direct decomposition.

During goal-directed locomotion, descending signals from supraspinal structures act through spinal interneuron pathways to effect modifications of muscle activity that are appropriate to the task requirements. Recent studies using decomposition methods suggest that this control might be facilitated by activating synergies organized at the level of the spinal cord. However, it is difficult to directly relate these mathematically defined synergies to the patterns of electromyographic activity observed in the original recordings. To address this issue, we have used a novel cluster analysis to make a detailed study of the organization of the synergistic patterns of muscle activity observed in the fore- and hindlimb during treadmill locomotion. The results show that the activity of a large number of forelimb muscles (26 bursts of activity from 18 muscles) can be grouped into 11 clusters on the basis of synchronous co-activation. Nine (9/11) of these clusters defined muscle activity during the swing phase of locomotion; these clusters were distributed in a sequential manner and were related to discrete behavioral events. A comparison with the synergies identified by linear decomposition methods showed some striking similarities between the synergies identified by the different methods. In the hindlimb, a simpler organization was observed, and a sequential activation of muscles similar to that observed in the forelimb during swing was less clear. We suggest that this organization of synergistic muscles provides a means by which descending signals could provide the detailed control of different muscle groups that is necessary for the flexible control of multi-articular movements.

Context-dependent anticipation of different task dynamics: rapid recall of appropriate motor skills using visual cues.

Recent studies have reported that human subjects show varying degrees of ability to use contextual cues to recall the motor skills required to compensate for different dynamic external force fields during arm movements. In particular, the subjects showed little or no ability to use color cues to adjust motor outputs in anticipation of the perturbing fields after limited periods of training that were sufficient to learn to compensate for the fields themselves. This is unexpected since humans and monkeys can use color cues to perform a wide range of visuomotor tasks. Here we show that a monkey with extensive practice compensating for viscous fields in an elbow-movement task can use associated color cues to adjust motor output in anticipation of an impending field before physically encountering it.