A neural basis of choking under pressure.

Incentives tend to drive improvements in performance. But when incentives get too high, we can "choke under pressure" and underperform right when it matters most. What neural processes might lead to choking under pressure? We studied rhesus monkeys performing a challenging reaching task in which they underperformed when an unusually large "jackpot" reward was at stake, and we sought a neural mechanism that might result in that underperformance.

A posture subspace in primary motor cortex.

To generate movements, the brain must combine information about movement goal and body posture. Motor cortex (M1) is a key node for the convergence of these information streams. How are posture and goal information organized within M1's activity to permit the flexible generation of movement commands? To answer this question, we recorded M1 activity while monkeys performed a variety of tasks with the forearm in a range of postures.

A theory of brain-computer interface learning via low-dimensional control.

A remarkable demonstration of the flexibility of mammalian motor systems is primates' ability to learn to control brain-computer interfaces (BCIs). This constitutes a completely novel motor behavior, yet primates are capable of learning to control BCIs under a wide range of conditions. BCIs with carefully calibrated decoders, for example, can be learned with only minutes to hours of practice.

Learning leaves a memory trace in motor cortex.

How are we able to learn new behaviors without disrupting previously learned ones? To understand how the brain achieves this, we used a brain-computer interface (BCI) learning paradigm, which enables us to detect the presence of a memory of one behavior while performing another. We found that learning to use a new BCI map altered the neural activity that monkeys produced when they returned to using a familiar BCI map in a way that was specific to the learning experience. That is, learning left a "memory trace" in the primary motor cortex.

Dynamical constraints on neural population activity.

The manner in which neural activity unfolds over time is thought to be central to sensory, motor, and cognitive functions in the brain. Network models have long posited that the brain's computations involve time courses of activity that are shaped by the underlying network. A prediction from this view is that the activity time courses should be difficult to violate.

A neural basis of choking under pressure.

Unlabelled: Incentives tend to drive improvements in performance. But when incentives get too high, we can "choke under pressure" and underperform when it matters most. What neural processes might lead to choking under pressure? We studied Rhesus monkeys performing a challenging reaching task in which they underperform when an unusually large "jackpot" reward is at stake.

How learning unfolds in the brain: toward an optimization view.

How do changes in the brain lead to learning? To answer this question, consider an artificial neural network (ANN), where learning proceeds by optimizing a given objective or cost function. This "optimization framework" may provide new insights into how the brain learns, as many idiosyncratic features of neural activity can be recapitulated by an ANN trained to perform the same task. Nevertheless, there are key features of how neural population activity changes throughout learning that cannot be readily explained in terms of optimization and are not typically features of ANNs.

Learning is shaped by abrupt changes in neural engagement.

Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.

Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity.

The instability of neural recordings can render clinical brain-computer interfaces (BCIs) uncontrollable. Here, we show that the alignment of low-dimensional neural manifolds (low-dimensional spaces that describe specific correlation patterns between neurons) can be used to stabilize neural activity, thereby maintaining BCI performance in the presence of recording instabilities. We evaluated the stabilizer with non-human primates during online cursor control via intracortical BCIs in the presence of severe and abrupt recording instabilities.

New neural activity patterns emerge with long-term learning.

Learning has been associated with changes in the brain at every level of organization. However, it remains difficult to establish a causal link between specific changes in the brain and new behavioral abilities. We establish that new neural activity patterns emerge with learning.

Constraints on neural redundancy.

Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e.

Publisher Correction: Learning by neural reassociation.

In the version of this article initially published, equation (10) contained cos Θ instead of sin Θ as the bottom element of the right-hand vector. The error has been corrected in the HTML and PDF versions of the article.

Learning by neural reassociation.

Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task.

A Path to Understanding How Motor Cortex Influences Muscle Activity.

The influence of motor cortex on muscles during different behaviors is incompletely understood. In this issue of Neuron, Miri et al. (2017) show that the population activity patterns produced by motor cortex during different behaviors determine the selective routing of signals along different pathways between motor cortex and muscles.

Extracellular voltage threshold settings can be tuned for optimal encoding of movement and stimulus parameters.

Objective: A traditional goal of neural recording with extracellular electrodes is to isolate action potential waveforms of an individual neuron. Recently, in brain-computer interfaces (BCIs), it has been recognized that threshold crossing events of the voltage waveform also convey rich information. To date, the threshold for detecting threshold crossings has been selected to preserve single-neuron isolation.

Single-unit activity, threshold crossings, and local field potentials in motor cortex differentially encode reach kinematics.

A diversity of signals can be recorded with extracellular electrodes. It remains unclear whether different signal types convey similar or different information and whether they capture the same or different underlying neural phenomena. Some researchers focus on spiking activity, while others examine local field potentials, and still others posit that these are fundamentally the same signals.

Functional connectivity and tuning curves in populations of simultaneously recorded neurons.

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves.

Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions.

It is well known that discharge of neurons in the primary motor cortex (M1) depends on end-point force and limb posture. However, the details of these relations remain unresolved. With the development of brain-machine interfaces (BMIs), these issues have taken on practical as well as theoretical importance.

Intrafascicular stimulation of monkey arm nerves evokes coordinated grasp and sensory responses.

High-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss. In this study, we implanted Utah Slanted Electrode Arrays (USEAs) intrafascicularly at the elbow or shoulder in arm nerves of rhesus monkeys (n = 4) under isoflurane anesthesia. Input-output curves indicated that pulse-width-modulated single-electrode stimulation in each arm nerve could recruit single muscles with little or no recruitment of other muscles.

Restoration of grasp following paralysis through brain-controlled stimulation of muscles.

Patients with spinal cord injury lack the connections between brain and spinal cord circuits that are essential for voluntary movement. Clinical systems that achieve muscle contraction through functional electrical stimulation (FES) have proven to be effective in allowing patients with tetraplegia to regain control of hand movements and to achieve a greater measure of independence in daily activities. In existing clinical systems, the patient uses residual proximal limb movements to trigger pre-programmed stimulation that causes the paralysed muscles to contract, allowing use of one or two basic grasps.

Local field potentials allow accurate decoding of muscle activity.

Local field potentials (LFPs) in primary motor cortex include significant information about reach target location and upper limb movement kinematics. Some evidence suggests that they may be a more robust, longer-lasting signal than action potentials (spikes). Here we assess whether LFPs can also be used to decode upper limb muscle activity, a complex movement-related signal.

Electrical conduction block in large nerves: high-frequency current delivery in the nonhuman primate.

Recent studies have made significant progress toward the clinical implementation of high-frequency conduction block (HFB) of peripheral nerves. However, these studies were performed in small nerves, and questions remain regarding the nature of HFB in large-diameter nerves. This study in nonhuman primates shows reliable conduction block in large-diameter nerves (up to 4.

Nerve cuff stimulation and the effect of fascicular organization for hand grasp in nonhuman primates.

The overall goal of this work is to introduce nerve cuff electrodes into upper extremity hand grasp systems. The first challenge is to develop a nerve cuff electrode that can selectively activate multiple hand functions from common upper extremity peripheral nerves. The Flat Interface Nerve Electrode (FINE) has shown selective stimulation capability in animal trials.

Toward the restoration of hand use to a paralyzed monkey: brain-controlled functional electrical stimulation of forearm muscles.

Loss of hand use is considered by many spinal cord injury survivors to be the most devastating consequence of their injury. Functional electrical stimulation (FES) of forearm and hand muscles has been used to provide basic, voluntary hand grasp to hundreds of human patients. Current approaches typically grade pre-programmed patterns of muscle activation using simple control signals, such as those derived from residual movement or muscle activity.