Biology subjects

O'Shea, D. J.

Publications and source records attributed to O'Shea, D. J..

7 recordsLinked to original sources

bioRxiv2026

A predictive theory of experimental design for inferring neural population geometry in large-scale recordings

Ongoing technological advances will lead to recordings with progressively increasing numbers of neurons, while trial counts may only increase modestly. The analysis of such large-scale data increasingly relies on extracting collective neural population geometry. These combined recording and analysis trends raise the fundamental need for a predictive theory of experimental design that can tell us how accurately we will be able to infer such geometry in future larger scale recordings with more neurons and trials, by extrapolating from past smaller recordings. We derive such a theory for the simplest and most widely used method for extracting population geometry: principal component analysis. Our theory can predict how the dimensionality of neural data will grow with more neurons and trials and how accurate and reliable neuronal correlations and individual neural modes of the population geometry will be. Importantly, we find a blessing of dimensionality in which recording more neurons allows population geometry to be inferred accurately with fewer trials. The need for fewer trials in larger recordings will allow for the design of new experiments with more diverse trial types. Moreover, we derive scaling laws for the performance of neural prediction, setting the stage for the derivation of scaling laws for foundation models in neuroscience. We successfully test our theory across diverse species and recording modalities.

neuroscience↗

bioRxiv2023

Large-scale brain-wide neural recording in nonhuman primates

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmatically select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.

neuroscience↗

bioRxiv2022

Direct neural perturbations reveal a dynamical mechanism for robust computation

The rich repertoire of skilled mammalian behavior is the product of neural circuits that generate robust and flexible patterns of activity distributed across populations of neurons. Decades of associative studies have linked many behaviors to specific patterns of population activity, but association alone cannot reveal the dynamical mechanisms that shape those patterns. Are local neural circuits high-dimensional dynamical reservoirs able to generate arbitrary superpositions of patterns with appropriate excitation? Or might circuit dynamics be shaped in response to behavioral context so as to generate only the low-dimensional patterns needed for the task at hand? Here, we address these questions within primate motor cortex by delivering optogenetic and electrical microstimulation perturbations during reaching behavior. We develop a novel analytic approach that relates measured activity to theoretically tractable, dynamical models of excitatory and inhibitory neurons. This computational model captures the dynamical effects of these perturbations and demonstrates that motor cortical activity during reaching is shaped by a self-contained, low-dimensional dynamical system. The subspace containing task-relevant dynamics proves to be oriented so as to be robust to strong non-normal amplification within cortical circuits. This task dynamics space exhibits a privileged causal relationship with behavior, in that stimulation in motor cortex perturb reach kinematics only to the extent that it alters neural states within this subspace. Our results resolve long-standing questions about the dynamical structure of cortical activity associated with movement, and illuminate the dynamical perturbation experiments needed to understand how neural circuits throughout the brain generate complex behavior.

neuroscience↗

bioRxiv2020

Skill-specific changes in cortical preparatory activity during motor learning

Animals have a remarkable capacity to learn new motor skills, but it remains an open question as to how learning changes neural population dynamics underlying movement1. Specifically, we asked whether changes in neural population dynamics relate purely to newly learned movements or if additional patterns are generated that facilitate learning without matching motor output. We trained rhesus monkeys to learn a curl force field2 task that elicited new arm-movement kinetics for some but not all reach directions3,4. We found that along certain neural dimensions, preparatory activity in motor cortex reassociated existing activity patterns with new movements. These systematic changes were observed only for learning-altered reaches. Surprisingly, we also found prominent shifts of preparatory activity along a nearly orthogonal neural dimension. These changes in preparatory activity were observed uniformly for all reaches including those unaltered by learning. This uniform shift during learning implies formation of new neural activity patterns, which was not observed in other short-term learning contexts5-8. During a washout period when the curl field was removed, movement kinetics gradually reverted, but the learning-induced uniform shift of preparatory activity persisted and a second, orthogonal uniform shift occurred. This persistent shift may retain a motor memory of the learned field9-11, consistent with faster relearning of the same curl field observed behaviorally and neurally. When multiple different curl fields were learned sequentially, we found distinct uniform shifts, each reflecting the identity of the field applied and potentially separating the associated motor memories12,13. The neural geometry of these shifts in preparatory activity could serve to organize skill-specific changes in movement production, facilitating the acquisition and retention of a broad motor repertoire.

neuroscience↗

bioRxiv2019

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

Calcium imaging has rapidly developed into a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of new principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon (2P) imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon (2P) imaging of calcium signals from in macaques engaged in a motor task. By imaging apical dendrites, some of which originated from deep layer 5 neurons, as as well as superficial cell bodies, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement, which was stable across many weeks. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signals and successfully decoded movement direction online. By fusing 2P functional imaging with CLARITY volumetric imaging, we verify that an imaged dendrite, which contributed to oBCI decoding, originated from a putative Betz cell in motor cortical layer 5. This approach establishes new opportunities for studying motor control and designing BCIs.

neuroscience↗

bioRxiv2017

ERAASR: An algorithm for removing electrical stimulation artifacts from multielectrode array recordings

Electrical stimulation is a widely used and effective tool in systems neuroscience, neural prosthetics, and clinical neurostimulation. However, electrical artifacts evoked by stimulation significantly complicate the detection of spiking activity on nearby recording electrodes. Here, we present ERAASR: an algorithm for Estimation and Removal of Artifacts on Arrays via Sequential principal components Regression. This approach leverages the similar structure of artifact transients, but not spiking activity, across simultaneously recorded channels on the array, across pulses within a train, and across trials. The effectiveness of the algorithm is demonstrated in macaque dorsal premotor cortex using acute linear multielectrode array recordings and single electrode stimulation. Large electrical artifacts appeared on all channels during stimulation. After application of ERAASR, the cleaned signals were quiescent on channels with no spontaneous spiking activity, whereas spontaneously active channels exhibited evoked spikes which closely resembled spontaneously occurring spiking waveforms. The ERAASR algorithm requires no special hardware and comprises sequential application of straightforward linear methods with intuitive parameters. Enabling simultaneous electrical stimulation and multielectrode array recording can help elucidate the causal links between neural activity and cognitive functions and enable the design and implementation of novel sensory protheses.

neuroscience↗

bioRxiv2017

Inferring single-trial neural population dynamics using sequential auto-encoders

Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract de-noised single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural populations activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.

neuroscience↗