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Miller, L. E.

Publications and source records attributed to Miller, L. E..

53 records · Page 3Linked to original sources
bioRxiv2021

Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots. Author SummaryIt is well established that proprioception is essential for effective for motor control, yet our understanding of proprioceptive coding in somatosensory cortex is far behind that of more established sensory modalities such as vision and touch. Here, we use unsupervised learning of a deep neural network imposed with biological constraints to reproduce coding properties of proprioceptive neurons in area 2 of primary somatosensory cortex. With this model, we demonstrate that the tendency for area 2 neurons in close physical proximity to share similar directional tuning can be explained by local topographic organisation driven by Mexican hat lateral effects, a phenomenon that is otherwise unobservable with the spatial resolution of available electrode microarray recordings. This provides, to the best of our knowledge, the first evidence of local topographic organisation in area 2 proprioceptive neurons. We also predict that the structure of proprioceptive receptive fields are typically multi-joint rather than single-joint in nature and provide, to the best of our knowledge, and find that our model best reproduces the tuning properties of neural data when training on natural kinematic recordings, rather than those from a stereotyped task, underlining the importance of training on data that reflect the natural distribution of sensory stimuli.

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bioRxiv2021

Estimating muscle activation from EMG using deep learning-based dynamical systems models

ObjectiveTo study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded electromyographic (EMG) signals. Common approaches estimate muscle activation independently for each channel or require manual tuning of model hyperparameters to optimally preserve behaviorally-relevant features. ApproachHere, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks (RNNs) to model the spatial and temporal regularities that underlie multi-muscle activation. Main ResultsWe first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion, and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also tested the generality of the approach by applying AutoLFADS to monkey forearm muscle activity from an isometric task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force compared to low-pass or Bayesian filtering. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than other tested approaches. SignificanceUltimately, this method leverages both dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles that can be used for further studies of multi-muscle coordination and its control by upstream brain areas.

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bioRxiv2021

Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array

ObjectivePersons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. If ICMS can mimic naturally occurring neural activity, afferent interfaces may be more informative and easier to learn than interfaces that evoke unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS. ApproachUsing a Utah multi-electrode array, we recorded activity evoked by single pulses, and short (~0.2 s) and long (~4 s) trains of ICMS at a wide range of amplitudes and frequencies. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings. With these techniques, we could record ~0.7 ms after stimulation offset even on the stimulated electrode. Main resultsWe recorded likely transsynaptically-evoked activity as early as ~0.7 ms after single pulses of stimulation that was immediately followed by suppressed neural activity lasting 10-150 ms. Instead of this long-lasting inhibition, neurons increased their firing rates for ~100 ms after trains. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels. SignificanceThe detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.

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bioRxiv2021

Alpha oscillations are involved in localizing touch on hand-held tools

The sense of touch is not restricted to the body but can also extend to external objects. When we use a hand-held tool to contact an object, we feel the touch on the tool and not in the hand holding the tool. The ability to perceive touch on a tool actually extends along its entire surface, allowing the user to accurately localize where it is touched similarly as they would on their body. While the neural mechanisms underlying the ability to localize touch on the body have been largely investigated, those allowing to localize touch on a tool are still unknown. We aimed to fill this gap by recording the EEG signal of participants while they localized tactile stimuli on a hand-held rod. We focused on oscillatory activity in the alpha (7-14 Hz) and beta (15-30 Hz) range, as they have been previously linked to distinct spatial codes used to localize touch on the body. Beta activity reflects the mapping of touch in skin-based coordinates, whereas alpha activity reflects the mapping of touch in external space. We found that alpha activity was solely modulated by the location of tactile stimuli applied on a hand-held rod. Source reconstruction suggested that this alpha power modulation was localized in a network of fronto-parietal regions previously implicated in higher-order tactile and spatial processing. These findings are the first to implicate alpha oscillations in tool-extended sensing and suggest an important role for processing touch in external space when localizing touch on a tool.

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bioRxiv2021

Small, correlated changes in synaptic connectivity may facilitate rapid motor learning

Animals can rapidly adapt their movements to external perturbations. This adaptation is paralleled by changes in single neuron activity in the motor cortices. Behavioural and neural recording studies suggest that when animals learn to counteract a visuomotor perturbation, these changes originate from altered inputs to the motor cortices rather than from changes in local connectivity, as neural covariance is largely preserved during adaptation. Since measuring synaptic changes in vivo remains very challenging, we used a modular recurrent network model to compare the expected neural activity changes following learning through altered inputs (Hinput) and learning through local connectivity changes (Hlocal). Learning under Hinput produced small changes in neural activity and largely preserved the neural covariance, in good agreement with neural recordings in monkeys. Surprisingly given the presumed dependence of stable neural covariance on preserved circuit connectivity, Hlocal led to only slightly larger changes in neural activity and covariance compared to Hinput. This similarity is due to Hlocal only requiring small, correlated connectivity changes to counteract the perturbation, which provided the network with significant robustness against simulated synaptic noise. Simulations of tasks that impose increasingly larger behavioural changes revealed a growing difference between Hinput and Hlocal, which could be exploited when designing future experiments.

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bioRxiv2021

Stoney vs. Histed: Quantifying the Spatial Effects of Intracortical Microstimulation

BackgroundIntracortical microstimulation (ICMS) is used to map neural circuits and restore lost sensory modalities such as vision, hearing, and somatosensation. The spatial effects of ICMS remain controversial: Stoney and colleagues proposed that the volume of somatic activation increased with stimulation intensity, while Histed et al. suggested activation density, but not somatic activation volume, increases with stimulation intensity. ObjectiveWe used computational modeling to quantify the spatial effects of ICMS intensity and unify the apparently paradoxical findings of Histed and Stoney. MethodsWe implemented a biophysically-based computational model of a cortical column comprising neurons with realistic morphology and representative synapses. We quantified the spatial effects of single pulse ICMS, including the radial distance to activated neurons and the density of activated neurons as a function of stimulation intensity. ResultsAt all amplitudes, the dominant mode of somatic activation was by antidromic propagation to the soma following axonal activation, rather than via trans-synaptic activation. There were no occurrences of direct activation of somata or dendrites. The volume over which antidromic action potentials were initiated grew with stimulation amplitude, while the volume of somatic activations did not. However, the density of somatic activation within the activated volume increased with stimulation amplitude. ConclusionsThe results resolve the apparent paradox between Stoney and Histeds results by demonstrating that the volume over which action potentials are initiated grows with ICMS amplitude, consistent with Stoney. However, the volume occupied by the activated somata remains approximately constant, while the density of activated neurons within that volume increase, consistent with Histed. HIGHLIGHTSO_LIImplemented a biophysically-based computational model of cortical column comprising cortical neurons with realistic morphology and representative synapses. C_LIO_LIQuantified the spatial patterns of neural activation by intracortical microstimulation to resolve the paradoxical findings of Stoney et al., 1968 and Histed et al., 2009. C_LIO_LIThe dominant mode of neural activation near the electrode was direct (i.e., via antidromic propagation from direct activation of the axon) and not trans-synaptic. C_LIO_LIThe dominant effect of increased ICMS intensity was to increase the density of activated neurons but not the volume of activation. C_LI

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bioRxiv2021

Sensory computations in the cuneate nucleus of macaques

AO_SCPLOWBSTRACTC_SCPLOWTactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures - the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, CN plays a key role in processing tactile information. SO_SCPLOWIGNIFICANCEC_SCPLOWPerception is the outcome of the sequential processing of sensory signals at multiple stages along the neuraxis. The conventional view is that tactile signals are processed predominantly in the cerebral cortex. We tested this view by investigating the response properties of neurons in the cuneate nucleus (CN), the first potential stage of processing along the primary touch neuraxis. We found that CN responses more nearly resemble those of cortical neurons than they do those of nerve fibers: CN neurons have spatially complex receptive fields reflecting convergent input from multiple classes of nerve fibers and exhibit a selectivity for object features, absent in the nerve. We conclude that CN plays a key, early role in the processing of tactile information.

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bioRxiv2021

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the "latent dynamics". These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings.

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bioRxiv2021

ENCODING OF LIMB STATE BY SINGLE NEURONS IN THE CUNEATE NUCLEUS OF AWAKE MONKEYS

The cuneate nucleus (CN) is among the first sites along the neuraxis where proprioceptive signals can be integrated, transformed, and modulated. The objective of the study was to characterize the proprioceptive representations in CN. To this end, we recorded from single CN neurons in three monkeys during active reaching and passive limb perturbation. We found that many neurons exhibited responses that were tuned approximately sinusoidally to limb movement direction, as has been found for other sensorimotor neurons. The distribution of their preferred directions (PDs) was highly non-uniform and resembled that of muscle spindles within individual muscles, suggesting that CN neurons typically receive inputs from only a single muscle. We also found that the responses of proprioceptive CN neurons tended to be modestly amplified during active reaching movements compared to passive limb perturbations, in contrast to cutaneous CN neurons whose responses were not systematically different in the active and passive conditions. Somatosensory signals thus seem to be subject to a "spotlighting" of relevant sensory information rather than uniform suppression as has been suggested previously.

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bioRxiv2021

A large-scale neural network training framework for generalized estimation of single-trial population dynamics

Recent technical advances have enabled recording of increasingly large populations of neural activity, even during natural, unstructured behavior. Deep sequential autoencoders are the current state-of-the-art for uncovering dynamics from these datasets. However, these highly complex models include many non-trainable hyperparameters (HPs) that are typically hand tuned with reference to supervisory information (e.g., behavioral data). This process is cumbersome and time consuming and biases model selection toward models with good representations of individual supervisory variables. Additionally, it cannot be applied to cognitive areas or unstructured tasks for which supervisory information is unavailable. Here we demonstrate AutoLFADS, an automated model-tuning framework that can characterize dynamics using only neural data, without the need for supervisory information. This enables inference of dynamics out-of-the-box in diverse brain areas and behaviors, which we demonstrate on several datasets: motor cortex during free-paced reaching, somatosensory cortex during reaching with perturbations, and dorsomedial frontal cortex during cognitive timing tasks. We also provide a cloud software package and comprehensive tutorials that enable new users to apply the method without dedicated computing resources.

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bioRxiv2020

Estimating the dimensionality of the manifold underlying multi-electrode neural recordings

It is generally accepted that the number of neurons in a given brain area far exceeds the information that area encodes. For example, motor areas of the human brain contain tens of millions of neurons that control the activation of tens or at most hundreds of muscles. This massive redundancy implies the covariation of many neurons, which constrains the population activity to a low-dimensional manifold within the space of all possible patterns of neural activity. To gain a conceptual understanding of the complexity of the neural activity within a manifold, it is useful to estimate its dimensionality, which quantifies the number of degrees of freedom required to describe the observed population activity without significant information loss. While there are many algorithms for dimensionality estimation, we do not know which are well suited for analyzing neural activity. The objective of this study was to evaluate the efficacy of several representative algorithms for estimating linearly and nonlinearly embedded data. We generated synthetic neural recordings with known intrinsic dimensionality and used them to test the algorithms accuracy and robustness. We emulated some of the important challenges associated with experimental data by adding noise, altering the nature of the embedding from the low-dimensional manifold to the high-dimensional recordings, varying the dimensionality of the manifold, and limiting the amount of available data. We demonstrated that linear algorithms overestimate the dimensionality of nonlinear, noise-free data. In cases of high noise, most algorithms overestimated dimensionality. We thus developed a denoising algorithm based on deep learning, the "Joint Autoencoder", which significantly improved subsequent dimensionality estimation. Critically, we found that all algorithms failed when the dimensionality was high (above 20) or when the amount of data used for estimation was low. Based on the challenges we observed, we formulated a pipeline for estimating the dimensionality of experimental neural data. Author SummaryThe number of neurons that we can record from has increased exponentially for decades; today we can simultaneously record from thousands of neurons. However, the individual firing rates are highly redundant. One approach to identifying important features from redundant data is to estimate the dimensionality of the neural recordings, which represents the number of degrees of freedom required to describe the data without significant information loss. Better understanding of dimensionality may also uncover the mechanisms of computation within a neural circuit. Circuits carrying out complex computations might be higher-dimensional than those carrying out simpler computations. Typically, studies have quantified neural dimensionality using one of several available methods despite a lack of consensus on which method would be most appropriate for neural data. In this work, we used several methods to investigate the accuracy of simulated neural data with properties mimicking those of actual neural recordings. Based on these results, we devised an analysis pipeline to estimate the dimensionality of neural recordings. Our work will allow scientists to extract informative features from a large number of highly redundant neurons, as well as quantify the complexity of information encoded by these neurons.

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bioRxiv2019

Area 2 of primary somatosensory cortex encodes kinematics of the whole arm

AO_SCPLOWBSTRACTC_SCPLOWProprioception, the sense of body position, movement, and associated forces, remains poorly understood, despite its critical role in movement. Most studies of area 2, a proprioceptive area of somatosensory cortex, have simply compared neurons activities to the movement of the hand through space. By using motion tracking, we sought to elaborate this relationship by characterizing how area 2 activity relates to whole arm movements. We found that a whole-arm model, unlike classic models, successfully predicted how features of neural activity changed as monkeys reached to targets in two workspaces. However, when we then evaluated this whole-arm model across active and passive movements, we found that many neurons did not consistently represent the whole arm over both conditions. These results suggest that 1) neural activity in area 2 includes representation of the whole arm during reaching and 2) many of these neurons represented limb state differently during active and passive movements.

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bioRxiv2018

A stable, long-term cortical signature underlying consistent behavior

Animals readily execute learned motor behaviors in a consistent manner over long periods of time, yet similarly stable neural correlates remained elusive up to now. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which capture the dominant co-variation patterns within the neural population, are preserved across time. We recorded from populations of neurons in premotor, primary motor, and somatosensory cortices for up to two years as monkeys performed a reaching task. Intriguingly, despite steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. Such stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based on the recorded neural activity degraded substantially. We posit that latent cortical dynamics within the manifold are the fundamental and stable building blocks underlying consistent behavioral execution.

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bioRxiv2018

Linear-Nonlinear-Time-Warp-Poisson models of neural activity

Prominent models of spike trains assume only one source of variability - stochastic (Poisson) spiking - when stimuli and behavior are fixed. However, spike trains may also reflect variability due to internal processes such as planning. For example, we can plan a movement at one point in time and execute it at some arbitrary later time. Neurons involved in planning may thus share an underlying time-course that is not precisely locked to the actual movement. Here we combine the standard Linear-Nonlinear-Poisson (LNP) model with Dynamic Time Warping (DTW) to account for shared temporal variability. When applied to recordings from macaque premotor cortex, we find that time warping considerably improves predictions of neural activity. We suggest that such temporal variability is a widespread phenomenon in the brain which should be modeled.

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bioRxiv2017

Multiple tasks viewed from the neural manifold: Stable control of varied behavior

How do populations of cortical neurons have the flexibility to perform different functions? We investigated this question in primary motor cortex (M1), where populations of neurons are able to generate a rich repertoire of motor behaviors. We recorded neural activity while monkeys performed a variety of wrist and reach-to-grasp motor tasks, each requiring a different pattern of neural activity. We characterized the flexibility of M1 movement control by comparing the \"neural modes\" that capture covariation across neurons, believed to arise from network connectivity. We found large similarities in the structure of the neural modes across tasks, as well as striking similarities in their temporal activation dynamics. These similarities were only apparent at the population level. Moreover, a subset of these well-preserved modes captured a task-independent mapping onto muscle commands. We hypothesize that this system of flexibly combined, stable neural modes gives M1 the flexibility to generate our wide-ranging behavioral repertoire.

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bioRxiv2017

Population Coding Of Conditional Probability Distributions In Dorsal Premotor Cortex

Our bodies and the environment constrain our movements. For example, when our arm is fully outstretched, we cannot extend it further. More generally, the distribution of possible movements is conditioned on the state of our bodies in the environment, which is constantly changing. However, little is known about how the brain represents such distributions, and uses them in movement planning. Here, we recorded from dorsal premotor cortex (PMd) and primary motor cortex (M1) while monkeys reached to randomly placed targets. The hands position within the workspace created probability distributions of possible upcoming targets, which affected movement trajectories and latencies. PMd, but not M1, neurons had increased activity when the monkeys hand position made it likely the upcoming movement would be in the neurons preferred directions. Across the population, PMd activity represented probability distributions of individual upcoming reaches, which depended on rapidly changing information about the bodys state in the environment.

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