Showing papers by "Byron M. Yu published in 2020"

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

Abstract: 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. The stabilized BCIs recovered proficient control under different instability conditions and across multiple days. The stabilizer does not require knowledge of user intent and can outperform supervised recalibration. It stabilized BCIs even when neural activity contained little information about the direction of cursor movement. The stabilizer may be applicable to other neural interfaces and may improve the clinical viability of BCIs. Neural activity residing in a low-dimensional space that reflects specific correlation patterns among neurons can be used to maintain the performance of brain–computer interfaces in the presence of recording instabilities.

Learning is shaped by abrupt changes in neural engagement

Abstract: Internal states such as arousal, attention, and motivation are known to modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain must modify the neural activity it produces to improve behavioral performance. How do internal states affect the evolution of this learning process? Using a brain-computer interface (BCI) learning paradigm in non-human primates, we identified large fluctuations in neural population activity in motor cortex (M1) indicative of arousal-like internal state changes. These fluctuations drove population activity along dimensions we term neural engagement axes. Neural engagement increased abruptly at the start of learning, and then gradually retreated. In a BCI, the causal relationship between neural activity and behavior is known. This allowed us to understand how these changes impacted behavioral performance for different task goals. We found that neural engagement interacted with learning, helping to explain why animals learned some task goals more quickly than others.

Slow drift of neural activity as a signature of impulsivity in macaque visual and prefrontal cortex

Abstract: An animal’s decision depends not only on incoming sensory evidence but also on its fluctuating internal state. This internal state is a product of cognitive factors, such as fatigue, motivation, and arousal, but it is unclear how these factors influence the neural processes that encode the sensory stimulus and form a decision. We discovered that, over the timescale of tens of minutes during a perceptual decision-making task, animals slowly shifted their likelihood of reporting stimulus changes. They did this unprompted by task conditions. We recorded neural population activity from visual area V4 as well as prefrontal cortex, and found that the activity of both areas slowly drifted together with the behavioral fluctuations. We reasoned that such slow fluctuations in behavior could either be due to slow changes in how the sensory stimulus is processed or due to a process that acts independently of sensory processing. By analyzing the recorded activity in conjunction with models of perceptual decision-making, we found evidence for the slow drift in neural activity acting as an impulsivity signal, overriding sensory evidence to dictate the final decision. Overall, this work uncovers an internal state embedded in the population activity across multiple brain areas, hidden from typical trial-averaged analyses and revealed only when considering the passage of time within each experimental session. Knowledge of this cognitive factor was critical in elucidating how sensory signals and the internal state together contribute to the decision-making process.

Bridging neuronal correlations and dimensionality reduction

Abstract: Two commonly used approaches to study interactions among neurons are spike count correlation, which describes pairs of neurons, and dimensionality reduction, applied to a population of neurons. While both approaches have been used to study trial-to-trial correlated neuronal variability, they are often used in isolation and have not been directly related. We first established concrete mathematical and empirical relationships between pairwise correlation and metrics of population-wide covariability based on dimensionality reduction. Applying these insights to macaque V4 population recordings, we found that the previously reported decrease in mean pairwise correlation associated with attention stemmed from three distinct changes in population-wide covariability. Overall, our work builds the intuition and formalism to bridge between pairwise correlation and population-wide covariability and presents a cautionary tale about the inferences one can make about population activity by using a single statistic, whether it be mean pairwise correlation or dimensionality.

Intracortical Brain–Machine Interfaces

Abstract: A brain–machine interface, or BMI, directly connects the brain to the external world, bypassing damaged biological pathways. It replaces the impaired parts of the nervous system with hardware and software that translate a user’s internal motor commands into action. In this chapter, we will discuss the four basic components of an intracortical BMI: an intracortical neural recording, a decoding algorithm, an output device, and sensory feedback. In Sect. 5.2 we will discuss intracortical signals, the electrodes used to record them, and where in the brain to record them. The salient features of the neural signal useful for control are extracted with a decoding algorithm. This algorithm translates the neural signal into an intended action which is executed by an output device, such as a robotic limb, the person’s own muscles, or a computer interface. In Sect. 5.3 we will discuss classification decoders and how they can be implemented in a BMI for communication. In Sect. 5.4 we will discuss continuous decoders for moment-by-moment control of a computer cursor or robotic arm. In Sect. 5.5, we will discuss a BMI that controls electrical stimulation to directly activate a patient’s own paralyzed muscles and reanimate their arm. Finally, in Sect. 5.6, we will discuss ongoing work toward expanding sensory feedback with the goal of making intracortical BMIs a clinically viable option for treating paralysis, as well as other research trends.