Author
Byron M. Yu
Other affiliations: University College London, University of California, Berkeley, Stanford University
Bio: Byron M. Yu is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Population & Dimensionality reduction. The author has an hindex of 36, co-authored 98 publications receiving 7703 citations. Previous affiliations of Byron M. Yu include University College London & University of California, Berkeley.
Papers published on a yearly basis
Papers
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Stanford University1, University College London2, Howard Hughes Medical Institute3, University of Chicago4, University of Pittsburgh5, Yeshiva University6, New York University7, Washington University in St. Louis8, University of California, San Francisco9, University of Texas at Austin10, Northwestern University11
TL;DR: In this article, the authors measured neural variability in 13 extracellularly recorded datasets and one intra-cellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats and found that stimulus onset caused a decline in neural variability.
Abstract: Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.
1,033 citations
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TL;DR: This review examines three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets, and practical advice about selecting methods and interpreting their outputs.
Abstract: Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.
883 citations
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TL;DR: The design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported are presented, indicating that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible.
Abstract: Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.
677 citations
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TL;DR: The results suggest that the existing structure of a network can shape learning, and offer a network-level explanation for the observation that the authors are more readily able to learn new skills when they are related to the skills that they already possess.
Abstract: Learning, whether motor, sensory or cognitive, requires networks of neurons to generate new activity patterns. As some behaviours are easier to learn than others, we asked if some neural activity patterns are easier to generate than others. Here we investigate whether an existing network constrains the patterns that a subset of its neurons is capable of exhibiting, and if so, what principles define this constraint. We employed a closed-loop intracortical brain-computer interface learning paradigm in which Rhesus macaques (Macaca mulatta) controlled a computer cursor by modulating neural activity patterns in the primary motor cortex. Using the brain-computer interface paradigm, we could specify and alter how neural activity mapped to cursor velocity. At the start of each session, we observed the characteristic activity patterns of the recorded neural population. The activity of a neural population can be represented in a high-dimensional space (termed the neural space), wherein each dimension corresponds to the activity of one neuron. These characteristic activity patterns comprise a low-dimensional subspace (termed the intrinsic manifold) within the neural space. The intrinsic manifold presumably reflects constraints imposed by the underlying neural circuitry. Here we show that the animals could readily learn to proficiently control the cursor using neural activity patterns that were within the intrinsic manifold. However, animals were less able to learn to proficiently control the cursor using activity patterns that were outside of the intrinsic manifold. These results suggest that the existing structure of a network can shape learning. On a timescale of hours, it seems to be difficult to learn to generate neural activity patterns that are not consistent with the existing network structure. These findings offer a network-level explanation for the observation that we are more readily able to learn new skills when they are related to the skills that we already possess.
536 citations
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08 Dec 2008TL;DR: In this article, Gaussian process factor analysis (GPFA) was proposed to combine smoothing and dimensionality reduction operations in a common probabilistic framework, and applied to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices.
Abstract: We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from tens to hundreds of neurons on individual experimental trials. Current methods for extracting neural trajectories involve a two-stage process: the data are first "denoised" by smoothing over time, then a static dimensionality reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way, and account for spiking variability that may vary both across neurons and across time. We then present a novel method for extracting neural trajectories, Gaussian-process factor analysis (GPFA), which unifies the smoothing and dimensionality reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that GPFA provided a better characterization of the population activity than the two-stage methods.
528 citations
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TL;DR: The results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
Abstract: Two people with long-standing tetraplegia use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. John Donoghue and colleagues have previously demonstrated that people with tetraplegia can learn to use neural signals from the motor cortex to control a computer cursor. Work from another lab has also shown that monkeys can learn to use such signals to feed themselves with a robotic arm. Now, Donoghue and colleagues have advanced the technology to a level at which two people with long-standing paralysis — a 58-year-old woman and a 66-year-old man — are able to use a neural interface to direct a robotic arm to reach for and grasp objects. One subject was able to learn to pick up and drink from a bottle using a device implanted 5 years earlier, demonstrating not only that subjects can use the brain–machine interface, but also that it has potential longevity. Paralysis following spinal cord injury, brainstem stroke, amyotrophic lateral sclerosis and other disorders can disconnect the brain from the body, eliminating the ability to perform volitional movements. A neural interface system1,2,3,4,5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with long-standing tetraplegia can use a neural interface system to move and click a computer cursor and to control physical devices6,7,8. Able-bodied monkeys have used a neural interface system to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here we demonstrate the ability of two people with long-standing tetraplegia to use neural interface system-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm and hand over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor 5 years earlier, also used a robotic arm to drink coffee from a bottle. Although robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after injury to the central nervous system, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals.
2,181 citations
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TL;DR: A system that permits embodied prosthetic control is described and monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task, and this demonstration of multi-degree-of-freedom embodied prosthetics control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.
Abstract: Brain-machine interfaces have mostly been used previously to move cursors on computer displays. Now experiments on macaque monkeys show that brain activity signals can control a multi-jointed prosthetic device in real-time. The monkeys used motor cortical activity to control a human-like prosthetic arm in a self-feeding task, with a greater sophistication of control than previously possible. This work could be important for the development of more practical neuro-prosthetic devices in the future. A system where monkeys use their motor cortical activity to control a robotic arm in a real-time self-feeding task, showing a significantly greater sophisitication of control than in previous studies, is demonstrated. This work could be important for the development of more practical neuro-prosthetic devices in the future. Arm movement is well represented in populations of neurons recorded from the motor cortex1,2,3,4,5,6,7. Cortical activity patterns have been used in the new field of brain–machine interfaces8,9,10,11 to show how cursors on computer displays can be moved in two- and three-dimensional space12,13,14,15,16,17,18,19,20,21,22. Although the ability to move a cursor can be useful in its own right, this technology could be applied to restore arm and hand function for amputees and paralysed persons. However, the use of cortical signals to control a multi-jointed prosthetic device for direct real-time interaction with the physical environment (‘embodiment’) has not been demonstrated. Here we describe a system that permits embodied prosthetic control; we show how monkeys (Macaca mulatta) use their motor cortical activity to control a mechanized arm replica in a self-feeding task. In addition to the three dimensions of movement, the subjects’ cortical signals also proportionally controlled a gripper on the end of the arm. Owing to the physical interaction between the monkey, the robotic arm and objects in the workspace, this new task presented a higher level of difficulty than previous virtual (cursor-control) experiments. Apart from an example of simple one-dimensional control23, previous experiments have lacked physical interaction even in cases where a robotic arm16,19,24 or hand20 was included in the control loop, because the subjects did not use it to interact with physical objects—an interaction that cannot be fully simulated. This demonstration of multi-degree-of-freedom embodied prosthetic control paves the way towards the development of dexterous prosthetic devices that could ultimately achieve arm and hand function at a near-natural level.
1,579 citations
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TL;DR: The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals and opens a path towards recording of brain-wide neural activity during behaviour.
Abstract: New silicon probes known as Neuropixels are shown to record from hundreds of neurons simultaneously in awake and freely moving rodents. Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions. Existing technologies reliably measure activity from a relatively small number of neurons with high spatial and temporal resolution, or from a large volume of neurons with low resolution. Timothy Harris and colleagues describe the design, fabrication and performance of Neuropixels, a silicon probe that can measure well-isolated neural activity from hundreds of neurons. They integrated these probes into a lightweight system that could record activity simultaneously and with high fidelity from hundreds of neurons in awake and freely moving rodents. Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures1,2. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging3,4,5 offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN6 sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
1,443 citations
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TL;DR: This work studies prefrontal cortex activity in macaque monkeys trained to flexibly select and integrate noisy sensory inputs towards a choice, and finds that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of the population.
Abstract: Prefrontal cortex is thought to have a fundamental role in flexible, context-dependent behaviour, but the exact nature of the computations underlying this role remains largely unknown. In particular, individual prefrontal neurons often generate remarkably complex responses that defy deep understanding of their contribution to behaviour. Here we study prefrontal cortex activity in macaque monkeys trained to flexibly select and integrate noisy sensory inputs towards a choice. We find that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of the population. The population dynamics can be reproduced by a trained recurrent neural network, which suggests a previously unknown mechanism for selection and integration of task-relevant inputs. This mechanism indicates that selection and integration are two aspects of a single dynamical process unfolding within the same prefrontal circuits, and potentially provides a novel, general framework for understanding context-dependent computations.
1,416 citations
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TL;DR: It is found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour.
Abstract: Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.
1,290 citations