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.

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.

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.

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.

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.