Biology subjects

Sober, S. J.

Publications and source records attributed to Sober, S. J..

16 recordsLinked to original sources

bioRxiv2026

High performance sorting of motor unit action potentials with EMUsort

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5% and 49.9% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

neuroscience↗

bioRxiv2025

JiSuJi, a virtual muscle for small animal simulations, accurately predicts force from naturalistic spike trains

Physics-based simulators for neuromechanical control of virtual animals have the potential to significantly enhance our understanding of intricate structure-function relationships in neuromuscular systems, their neural activity and motor control. However, a key challenge is the accurate prediction of the forces that muscle fibers produce based on their complex patterns of electrical activity ("spike trains") while preserving model simplicity for broader applicability. In this study, we present a chemomechanical, three-dimensional finite-element muscle model - JiSuJi (pronounced ji su j[i], meaning "ultrafast muscle" in Chinese) - that efficiently and accurately predicts muscle forces from naturalistic spike trains. The models performance is validated against songbird vocal muscles, a particularly fast and therefore challenging muscle type. Our results demonstrate that JiSuJi accurately predicts both isometric and non-isometric muscle forces across a variety of naturalistic neural activity patterns. JiSuJi furthermore outperforms state-of-the-art muscle simulators for accuracy, while maintaining computational efficiency. Simulating muscle behavior offers a promising approach for investigating the underlying mechanisms of neuro-muscular interactions and precise motor control, especially in the fast-contracting muscles of animal model systems.

neuroscience↗

bioRxiv2024

Motor unit mechanisms of speed control in mouse locomotion

During locomotion, the coordinated activity of dozens of muscles shapes the kinematic features of each stride, including systematic changes in limb movement across walking speed. Motor units, each of which consists of a single motor neuron and the muscle fibers it innervates, contribute to the total activation of each muscle through their recruitment and firing rate when active. However, it remains unknown how the nervous system controls locomotor speed by changing the firing of individual motor units. To address this, we combined quantitative behavioral analysis of mouse locomotion with single motor unit recordings from the lateral and long heads of the triceps brachii, which drive monoarticular extension of the elbow and biarticular movements of the elbow and shoulder, respectively. In contrast to prior studies employing bulk EMG to examine muscle activity, our recordings revealed the diversity of spike patterning across motor units as well as systematic differences in motor unit activity across muscles and locomotor speeds. First, motor unit activity differed significantly across the lateral and long heads, suggesting differential control of these two closely apposed elbow extensor muscles. Second, we found that individual units were recruited probabilistically (during only a subset of strides), showing that the highly repeatable bulk EMG signals observed across strides in fact reflect varying subsets of individual motor units. Finally, although recruitment probability and firing rate both increased at faster walking speeds, increases in recruitment were proportionally larger than rate changes, and recruitment of individual units accompanied changes in limb kinematics. Together, these results reveal how the firing of individual motor units varies systematically across muscles and walking speeds to produce flexible locomotor behavior.

neuroscience↗

bioRxiv2024

Millisecond-scale motor control precedes sensorimotor learning in Bengalese finches

The changes in neural activity that underlie motor skill acquisition during development are unknown. Juvenile songbirds learn their songs by an imitative process, and after learning adults use a millisecond-precise spike timing code to control vocal acoustics. Current theories suggest that developmental changes in neural firing rates, rather than precisely timed spike patterns, underlie the emergence of learning. Here we tested the hypothesis that songbirds transition from a rate-based to a spike-timing-based motor code during the process of vocal development. To do so, we recorded vocal motor neurons across development as individual Bengalese finches learned their songs. Contrary to our hypothesis, we found that despite dramatic changes in firing statistics during development, millisecond-scale spike pattern codes for vocal acoustics are present throughout all stages of vocal development. Furthermore, firing rate fluctuations are no more predictive of song output in young learners than in expert adults. The dramatic changes in spiking statistics observed during song learning therefore do not reflect a developmental change in the timescale of motor coding, but instead signals the selection of a particular subset of precisely timed spike patterns. We speculate that such patterns are favored because they most reliably modulate behavior.

neuroscience↗

bioRxiv2024

Opto-Myomatrix: μLED integrated microelectrode arrays for optogenetic activation and electrical recording in muscle tissue

ObjectiveOptogenetics is a valuable and widely-used technique that allows precise perturbations of selected groups of cells with high temporal and spatial resolution by using optical systems and genetic engineering technologies. This study aims to develop Opto-Myomatrix, a novel optogenetic tool for precise muscle fiber control and high-resolution electrophysiological recording. MethodBased on a flexible and biocompatible polymer substrate, the device incorporates an integrated {micro}LED that delivers light at 465 nm for optogenetic stimulation and 32 PEDOT:PSS-coated electrodes for electromyography (EMG) recording. We also added a reflector to improve optical power output. ResultsThe fabricated Opto-Myomatrix device achieves an optical output intensity as high as 129.46 mW/mm2 in the direction of interest, which is enhanced by nearly 100%. The PEDOT:PSS-coated electrodes exhibit 85% lower impedance than uncoated contacts, enabling high signal-to-noise EMG acquisition. We investigated heat dissipation characteristics of the {micro}LED through measurements and finite element simulations, confirming that temperature changes remain within safe limits. The Opto-Myomatrix device was implanted in transgenetic mice and successfully stimulated targeted jaw muscles, inducing movement while simultaneously capturing EMG signals. ConclusionThe Opto-Myomatrix effectively combines precise optical stimulation and high-quality EMG recording in a flexible and biocompatible device, focusing on optogenetic applications in muscle. SignificanceWhile optogenetic tools are well-established for brain and central nervous system (CNS) research, the development of Opto-Myomatrix addresses a critical gap by enabling precise muscle fiber control and high-resolution recording for advancing neuromuscular studies.

bioengineering↗

bioRxiv2024

Flexible EMG arrays with integrated electronics for scalable electrode density

Recent developments in electrode technology have demonstrated the power of flexible microelectrode arrays (FMEAs) for measuring muscle activity at high resolution. We recently introduced the Myomatrix array, an FMEA optimized for measuring the activity of individual motor units (the collection of muscle fibers innervated by a single motor neuron) [1] in freely behaving animals. Although FMEAs are fundamentally changing the way electromyography (EMG) is acquired, the number of recording channels is limited by the size of the plug that interfaces with the signal processing hardware and the density of electrode connections on the array. Increasing EMG channel count and supporting electrophysiological studies in smaller animals depends on two seemingly incompatible goals: reducing device size while increasing the number of recording channels. The solution to these goals is to increase the channel count per wire output. Current off-the-shelf designs require a separate headstage and FMEA to be used simultaneously. In our prior devices [1], each FMEA had a dedicated wire output for every electrode input, creating a channel density of 1:1. To improve this channel density, we have developed an FMEA with an integrated digital amplifier (Bare-Die RHD2216, INTAN INC, USA). The design of the FMEA reduces the footprint of the devices back end by 74% and relocates the Intan bare die from the headstage to the FMEA itself, creating a channel density of 1:3.2. Our methodology combines standard FMEA microfabrication with wire-bonding and surface-mounted components, enabling direct integration into a Serial Peripheral Interface (SPI) connection into the device itself, without any separate headstage. With this initial device there is a 1 : 3.2 channel density; however, our method allows using other bare die amplifiers (Intan, Inc., USA) for a channel density of 1:12.8. Our findings present a robust technique for chip embedding in custom FMEAs applicable to in-vivo electrophysiology

neuroscience↗

bioRxiv2024

Myo-optogenetics: optogenetic stimulation and electrical recording in skeletal muscles

Complex movements involve highly coordinated control of local muscle elements. Highly controlled perturbations of motor outputs can reveal insights into the neural control of movements. Here we introduce an optogenetic method, compatible with electromyography (EMG) recordings, to perturb muscles in transgenic mice. By expressing channelrhodopsin in muscle fibers, we achieved noninvasive, focal activation of orofacial muscles, enabling detailed examination of the mechanical properties of optogenetically evoked jaw muscle contractions. We demonstrated simultaneous EMG recording and optical stimulation, revealing the electrophysiological characteristics of optogenetically triggered muscle activity. Additionally, we applied optogenetic activation of muscles in physiologically and behaviorally relevant settings, mapping precise muscle actions and perturbing active behaviors. Our findings highlight the potential of muscle optogenetics to precisely manipulate muscle activity, offering a powerful tool for probing neuromuscular control systems and advancing our understanding of motor control.

neuroscience↗

bioRxiv2023

An output-null signature of inertial load in motor cortex

Coordinated movement requires the nervous system to continuously compensate for changes in mechanical load across different contexts. For voluntary movements like reaching, the motor cortex is a critical hub that generates commands to move the limbs and counteract loads. How does cortex contribute to load compensation when rhythmic movements are clocked by a spinal pattern generator? Here, we address this question by manipulating the mass of the forelimb in unrestrained mice during locomotion. While load produces changes in motor output that are robust to inactivation of motor cortex, it also induces a profound shift in cortical dynamics, which is minimally affected by cerebellar perturbation and significantly larger than the response in the spinal motoneuron population. This latent representation may enable motor cortex to generate appropriate commands when a voluntary movement must be integrated with an ongoing, spinally-generated rhythm.

neuroscience↗

bioRxiv2022

High-performance Flexible Microelectrode Array with PEDOT:PSS Coated 3D Micro-cones for Electromyographic Recording

High signal-to-noise ratio (SNR) electromyography (EMG) recordings are essential for identifying and analyzing single motor unit activity. While high-density electrodes allow for greater spatial resolution, the smaller electrode area translates to a higher impedance and lower SNR. In this study, we developed an implantable and flexible 3D microelectrode array (MEA) with low impedance that enables high-quality EMG recording. With polyimide micro-cones realized by standard photolithography process and PEDOT:PSS coating, this design can increase effective surface area by up to 250% and significantly improve electrical performance for electrode sites with various geometric surface areas, where the electrode impedance is at most improved by 99.3%. Acute EMG activity from mice was recorded by implanting the electrodes in vivo, and we were able to detect multiple individual motor units simultaneously and with high resolution (SNR >> 100). The charge storage capacity was measured to be 34.2 mC/cm2, indicating suitability of the electrodes for stimulation applications as well.

bioengineering↗

bioRxiv2021

Shared mechanisms of auditory and non-auditory vocal learning in the songbird brain

Songbirds and humans share the ability to adaptively modify their vocalizations based on sensory feedback. Prior studies have focused primarily on the role that auditory feedback plays in shaping vocal output throughout life. In contrast, it is unclear whether and how non-auditory information drives vocal plasticity. Here, we first used a reinforcement learning paradigm to establish that non-auditory feedback can drive vocal learning in adult songbirds. We then assessed the role of a songbird basal ganglia-thalamocortical pathway critical to auditory vocal learning in this novel form of vocal plasticity. We found that both this circuit and its dopaminergic inputs are necessary for non-auditory vocal learning, demonstrating that this pathway is not specialized exclusively for auditory-driven vocal learning. The ability of this circuit to use both auditory and non-auditory information to guide vocal learning may reflect a general principle for the neural systems that support vocal plasticity across species.

neuroscience↗

bioRxiv2019

Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds

The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a proteins function based on its sequence, we still do not understand how to accurately predict an organisms behavior based on neural activity. Here we derive a method for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. Our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such dictionaries of important spike patterns - rather than merely identifying the timescale on which such patterns exist, as in some prior approaches - opens the door for understanding fine motor control and the neural bases of sensorimotor learning in animals. For example, for the first time, we identify differences in encoding motor exploration versus typical behavior. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.

biophysics↗

bioRxiv2019

Application of the hierarchical bootstrap to multi-level data in neuroscience

AO_SCPLOWBSTRACTC_SCPLOWA common feature in many neuroscience datasets is the presence of hierarchical data structures, most commonly recording the activity of multiple neurons in multiple animals across multiple trials. Accordingly, the measurements constituting the dataset are not independent, even though the traditional statistical analyses often applied in such cases (e.g. Students t-test) treat them as such. The hierarchical bootstrap has been shown to be an effective tool to accurately analyze such data and while it has been used extensively in the statistical literature, its use is not widespread in neuroscience - despite the ubiquity of hierarchical datasets. In this paper, we illustrate the intuitiveness and utility of this approach to analyze hierarchically nested datasets. We use simulated neural data to show that traditional statistical tests can result in a false positive rate of over 45%, even if the Type-I error rate is set at 5%. While summarizing data across non-independent points (or lower levels) can potentially fix this problem, this approach greatly reduces the statistical power of the analysis. The hierarchical bootstrap, when applied sequentially over the levels of the hierarchical structure, keeps the Type-I error rate within the intended bound and retains more statistical power than summarizing methods. We conclude by demonstrating the effectiveness of the method in two real-world examples, first analyzing singing data in male Bengalese finches (Lonchura striata var. domestica) and second quantifying changes in behavior under optogenetic control in flies (Drosophila melanogaster).

neuroscience↗

bioRxiv2019

Dopamine depletion affects vocal acoustics and disrupts sensorimotor adaptation in songbirds

Dopamine is hypothesized to convey important error information in reinforcement learning tasks with explicit appetitive or aversive cues. However, during motor skill learning the only available feedback signal is typically an animals evaluation of the sensory feedback arising from its own behavior, rather than any external reward or punishment. It has previously been shown that intact dopaminergic signaling from the ventral tegmental area - substantia nigra compacta complex (VTA/SNc) is necessary for vocal learning in response to an external aversive auditory cue in songbirds. However, the role of dopamine in learning in the absence of explicit external cues is still unclear. Here we used male Bengalese finches (Lonchura striata var. domestica) to test the hypothesis that dopamine signaling is necessary for self-evaluation driven sensorimotor learning. We combined 6-hydroxydopamine (6-OHDA) lesions of dopaminergic terminals within Area X, a songbird basal ganglia nucleus critical for vocal learning, with a headphones learning paradigm that shifted the birds auditory feedback and compared their learning to birds without lesions. We found that 6-OHDA lesions affected song behavior in two ways. First, over a period of days lesioned birds systemically lowered their pitch regardless of the presence or absence of auditory errors. Second, 6-OHDA lesioned birds also displayed severe deficits in sensorimotor learning as measured by their adaptive change in pitch in response to the pitch-shifted auditory error. Our results suggest roles for dopamine both in motor production and in auditory error processing during vocal learning.\n\nSignificance StatementDopamine has been hypothesized to convey a reward prediction error signal in learning tasks involving external reinforcement. However the role dopamine plays in tasks involving self-guided error correction in the absence of external reinforcement is much less clear. To address this question, we studied the role of dopamine in sensorimotor adaptation using male Bengalese finches, which spontaneously produce a complex motor behavior (song) and are capable of modulating their behavioral output in response to induced auditory errors. Our results reveal that in addition to conveying errors in motor performance, dopamine may also have a role in modulating effort and in choosing a corrective response to the auditory error.

neuroscience↗

bioRxiv2018

Transitioning between preparatory and precisely sequenced neuronal activity in production of a skilled behavior

Precise neural sequences are associated with the production of well-learned skilled behaviors. Yet, how neural sequences arise in the brain remains unclear. In songbirds, premotor projection neurons in the cortical song nucleus HVC are necessary for producing learned song and exhibit precise sequential activity during singing. Using cell-type specific calcium imaging we identify populations of HVC premotor neurons associated with the beginning and ending of singing-related neural sequences. We discovered neurons that bookend singing-related sequences and neuronal populations that transition from sparse preparatory activity prior to song to precise neural sequences during singing. Recordings from downstream premotor neurons or the respiratory system suggest that pre-song activity may be involved in motor preparation to sing. These findings reveal population mechanisms associated with moving from non-vocal to vocal behavioral states and suggest that precise neural sequences begin and end as part of orchestrated activity across functionally diverse populations of cortical premotor neurons.

neuroscience↗

bioRxiv2017

Thalamostriatal and cerebellothalamic pathways in a songbird, the Bengalese finch

The thalamostriatal system is a major network in the mammalian brain, originating principally from the intralaminar nuclei of thalamus. Functions of the thalamostriatal system remain unclear, but a subset of these projections provides a pathway through which the cerebellum communicates with the basal ganglia. Both the cerebellum and basal ganglia play crucial roles in motor control. Although songbirds have yielded key insights into the neural basis of vocal learning, it is unknown whether a thalamostriatal system exists in the songbird brain. Thalamic nucleus DLM is an important part of the song system, the network of nuclei required for learning and producing song. DLM receives output from song system basal ganglia nucleus Area X and sits within dorsal thalamus, the proposed avian homolog of the mammalian intralaminar nuclei that also receives projections from the cerebellar nuclei. Using a viral vector that specifically labels presynaptic axon segments, we show in Bengalese finches that dorsal thalamus projects to Area X, the basal ganglia nucleus of the song system, and to surrounding medial striatum. To identify the sources of thalamic input to Area X, we map DLM and cerebellar-recipient dorsal thalamus (DTCbN). Surprisingly, we find both DLM and immediately-adjacent subregions of DTCbN project to Area X. In contrast, a medial-ventral subregion of DTCbN projects to medial striatum outside Area X. Our results suggest the basal ganglia in the song system, like the mammalian basal ganglia, integrate feedback from the thalamic region to which they project as well as thalamic regions that receive cerebellar output.

neuroscience↗

bioRxiv2017

Chance, long tails, and inference: a non-Gaussian, Bayesian theory of vocal learning in songbirds

Traditional theories of sensorimotor learning posit that animals use sensory error signals to find the optimal motor command in the face of Gaussian sensory and motor noise. However, most such theories cannot explain common behavioral observations, for example that smaller sensory errors are more readily corrected than larger errors and that large abrupt (but not gradually introduced) errors lead to weak learning. Here we propose a new theory of sensorimotor learning that explains these observations. The theory posits that the animal learns an entire probability distribution of motor commands rather than trying to arrive at a single optimal command, and that learning arises via Bayesian inference when new sensory information becomes available. We test this theory using data from a songbird, the Bengalese finch, that is adapting the pitch (fundamental frequency) of its song following perturbations of auditory feedback using miniature headphones. We observe the distribution of the sung pitches to have long, non-Gaussian tails, which, within our theory, explains the observed dynamics of learning. Further, the theory makes surprising predictions about the dynamics of the shape of the pitch distribution, which we confirm experimentally.

animal behavior and cognition↗