A flexible carbon nanotube electrode array for acute in vivo EMG recordings.

Executing complex behaviors requires precise control of muscle activity. Our understanding of how the nervous system learns and controls motor skills relies on recording electromyographic (EMG) signals from multiple muscles that are engaged in the motor task. Despite recent advances in tools for monitoring and manipulating neural activity, methods for recording in situ spiking activity in muscle fibers have changed little in recent decades. Here, we introduce a novel experimental approach to recording high-resolution EMG signals using parylene-coated carbon nanotube fibers (CNTFs). These fibers are fabricated via a wet spinning process and twisted together to create a bipolar electrode. Single CNTFs are strong, extremely flexible, small in diameter (14-24 µm), and have low interface impedance. We present two designs to build bipolar electrode arrays that, due to the small size of CNTF, lead to high spatial resolution EMG recordings. To test the EMG arrays, we recorded the activity of small (4 mm length) vocal muscles in songbirds in an acute setting. CNTF arrays were more flexible and yielded multiunit/bulk EMG recordings with higher SNR compared with stainless steel wire electrodes. Furthermore, we were able to record single-unit recordings not previously reported in these small muscles. CNTF electrodes are therefore well-suited for high-resolution EMG recording in acute settings, and we present both opportunities and challenges for their application in long-term chronic recordings.NEW & NOTEWORTHY We introduce a novel approach to record high-resolution EMG signals in small muscles using extremely strong and flexible carbon nanotube fibers (CNTFs). We test their functionality in songbird vocal muscles. Acute EMG recordings successfully yielded multiunit recordings with high SNR. Furthermore, they successfully isolated single-unit spike trains from CNTF recordings. CNTF electrodes have great potential for chronic EMG studies of small, deep muscles that demand high electrode flexibility and strength.

Chance, long tails, and inference in 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 large abrupt (but not gradually introduced) errors lead to weak learning. Here, we propose a theory of sensorimotor learning that explains these observations. The theory posits that the animal controls an entire probability distribution of motor commands rather than trying to produce 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.

Motor control by precisely timed spike patterns.

A fundamental problem in neuroscience is understanding how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories assume that this information is conveyed by the total number of spikes fired within a specified time interval (spike rate), recent studies have shown that additional information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information in spike timing actually plays a role in brain function. By examining the activity of individual motor units (the muscle fibers innervated by a single motor neuron) and manipulating patterns of activation of these neurons, we provide both correlative and causal evidence that the nervous system uses millisecond-scale variations in the timing of spikes within multispike patterns to control a vertebrate behavior-namely, respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision.

Dopaminergic Contributions to Vocal Learning.

Although the brain relies on auditory information to calibrate vocal behavior, the neural substrates of vocal learning remain unclear. Here we demonstrate that lesions of the dopaminergic inputs to a basal ganglia nucleus in a songbird species (Bengalese finches, Lonchura striata var. domestica) greatly reduced the magnitude of vocal learning driven by disruptive auditory feedback in a negative reinforcement task. These lesions produced no measureable effects on the quality of vocal performance or the amount of song produced. Our results suggest that dopaminergic inputs to the basal ganglia selectively mediate reinforcement-driven vocal plasticity. In contrast, dopaminergic lesions produced no measurable effects on the birds' ability to restore song acoustics to baseline following the cessation of reinforcement training, suggesting that different forms of vocal plasticity may use different neural mechanisms. SIGNIFICANCE STATEMENT: During skill learning, the brain relies on sensory feedback to improve motor performance. However, the neural basis of sensorimotor learning is poorly understood. Here, we investigate the role of the neurotransmitter dopamine in regulating vocal learning in the Bengalese finch, a songbird with an extremely precise singing behavior that can nevertheless be reshaped dramatically by auditory feedback. Our findings show that reduction of dopamine inputs to a region of the songbird basal ganglia greatly impairs vocal learning but has no detectable effect on vocal performance. These results suggest a specific role for dopamine in regulating vocal plasticity.

Millisecond-scale motor encoding in a cortical vocal area.

Studies of motor control have almost universally examined firing rates to investigate how the brain shapes behavior. In principle, however, neurons could encode information through the precise temporal patterning of their spike trains as well as (or instead of) through their firing rates. Although the importance of spike timing has been demonstrated in sensory systems, it is largely unknown whether timing differences in motor areas could affect behavior. We tested the hypothesis that significant information about trial-by-trial variations in behavior is represented by spike timing in the songbird vocal motor system. We found that neurons in motor cortex convey information via spike timing far more often than via spike rate and that the amount of information conveyed at the millisecond timescale greatly exceeds the information available from spike counts. These results demonstrate that information can be represented by spike timing in motor circuits and suggest that timing variations evoke differences in behavior.

Multifunctional and Context-Dependent Control of Vocal Acoustics by Individual Muscles.

The relationship between muscle activity and behavioral output determines how the brain controls and modifies complex skills. In vocal control, ensembles of muscles are used to precisely tune single acoustic parameters such as fundamental frequency and sound amplitude. If individual vocal muscles were dedicated to the control of single parameters, then the brain could control each parameter independently by modulating the appropriate muscle or muscles. Alternatively, if each muscle influenced multiple parameters, a more complex control strategy would be required to selectively modulate a single parameter. Additionally, it is unknown whether the function of single muscles is fixed or varies across different vocal gestures. A fixed relationship would allow the brain to use the same changes in muscle activation to, for example, increase the fundamental frequency of different vocal gestures, whereas a context-dependent scheme would require the brain to calculate different motor modifications in each case. We tested the hypothesis that single muscles control multiple acoustic parameters and that the function of single muscles varies across gestures using three complementary approaches. First, we recorded electromyographic data from vocal muscles in singing Bengalese finches. Second, we electrically perturbed the activity of single muscles during song. Third, we developed an ex vivo technique to analyze the biomechanical and acoustic consequences of single-muscle perturbations. We found that single muscles drive changes in multiple parameters and that the function of single muscles differs across vocal gestures, suggesting that the brain uses a complex, gesture-dependent control scheme to regulate vocal output.

Vocal generalization depends on gesture identity and sequence.

Generalization, the brain's ability to transfer motor learning from one context to another, occurs in a wide range of complex behaviors. However, the rules of generalization in vocal behavior are poorly understood, and it is unknown how vocal learning generalizes across an animal's entire repertoire of natural vocalizations and sequences. Here, we asked whether generalization occurs in a nonhuman vocal learner and quantified its properties. We hypothesized that adaptive error correction of a vocal gesture produced in one sequence would generalize to the same gesture produced in other sequences. To test our hypothesis, we manipulated the fundamental frequency (pitch) of auditory feedback in Bengalese finches (Lonchura striata var. domestica) to create sensory errors during vocal gestures (song syllables) produced in particular sequences. As hypothesized, error-corrective learning on pitch-shifted vocal gestures generalized to the same gestures produced in other sequential contexts. Surprisingly, generalization magnitude depended strongly on sequential distance from the pitch-shifted syllables, with greater adaptation for gestures produced near to the pitch-shifted syllable. A further unexpected result was that nonshifted syllables changed their pitch in the direction opposite from the shifted syllables. This apparently antiadaptive pattern of generalization could not be explained by correlations between generalization and the acoustic similarity to the pitch-shifted syllable. These findings therefore suggest that generalization depends on the type of vocal gesture and its sequential context relative to other gestures and may reflect an advantageous strategy for vocal learning and maintenance.

Vocal learning is constrained by the statistics of sensorimotor experience.

The brain uses sensory feedback to correct behavioral errors. Larger errors by definition require greater corrections, and many models of learning assume that larger sensory feedback errors drive larger motor changes. However, an alternative perspective is that larger errors drive learning less effectively because such errors fall outside the range of errors normally experienced and are therefore unlikely to reflect accurate feedback. This is especially crucial in vocal control because auditory feedback can be contaminated by environmental noise or sensory processing errors. A successful control strategy must therefore rely on feedback to correct errors while disregarding aberrant auditory signals that would lead to maladaptive vocal corrections. We hypothesized that these constraints result in compensation that is greatest for smaller imposed errors and least for larger errors. To test this hypothesis, we manipulated the pitch of auditory feedback in singing Bengalese finches. We found that learning driven by larger sensory errors was both slower than that resulting from smaller errors and showed less complete compensation for the imposed error. Additionally, we found that a simple principle could account for these data: the amount of compensation was proportional to the overlap between the baseline distribution of pitch production and the distribution experienced during the shift. Correspondingly, the fraction of compensation approached zero when pitch was shifted outside of the song's baseline pitch distribution. Our data demonstrate that sensory errors drive learning best when they fall within the range of production variability, suggesting that learning is constrained by the statistics of sensorimotor experience.

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 conditions. 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 sequenced 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. This shift is minimally affected by cerebellar perturbation and significantly larger than the load 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.

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.

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.

Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries.

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 protein's function based on its sequence, we still do not understand how to accurately predict an organism's behavior based on neural activity. Here, we introduce the unsupervised Bayesian Ising Approximation (uBIA) 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. In data recorded during singing from neurons in a vocal control region, 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 comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, we used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. 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.

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 how non-auditory information drives vocal plasticity. Here, we first used a reinforcement learning paradigm to establish that somatosensory feedback (cutaneous electrical stimulation) 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 critical for guiding adaptive vocal changes based on both auditory and somatosensory signals. The ability of this circuit to use both auditory and somatosensory information to guide vocal learning may reflect a general principle for the neural systems that support vocal plasticity across species.

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 ([Formula: see text]). The charge storage capacity was measured to be 34.2 mC/cm2, indicating suitability of the electrodes for stimulation applications as well.

Linked control of syllable sequence and phonology in birdsong.

The control of sequenced behaviors, including human speech, requires that the brain coordinate the production of discrete motor elements with their concatenation into complex patterns. In birdsong, another sequential vocal behavior, the acoustic structure (phonology) of individual song elements, or "syllables," must be coordinated with the sequencing of syllables into a song. However, it is unknown whether syllable phonology is independent of the sequence in which a syllable is produced. We quantified interactions between phonology and sequence in Bengalese finch song by examining both convergent syllables, which can be preceded by at least two different syllables and divergent syllables, which can be followed by at least two different syllables. Phonology differed significantly based on the identity of the preceding syllable for 97% of convergent syllables and differed significantly with the identity of the upcoming syllable for 92% of divergent syllables. Furthermore, sequence-dependent phonological differences extended at least two syllables away from the convergent or divergent syllable. To determine whether these phenomena reflect differences in central control, we analyzed premotor neural activity in the robust nucleus of the arcopallium (RA). Activity associated with a syllable varied significantly depending on the sequence in which the syllable was produced, suggesting that sequence-dependent variations in premotor activity contribute to sequence-dependent differences in phonology. Moreover, these data indicate that RA activity could contribute to the sequencing of syllables. Together, these results suggest that, rather than being controlled independently, the sequence and phonology of birdsong are intimately related, as is the case for human speech.

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

A 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., Student's 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).

Central contributions to acoustic variation in birdsong.

Birdsong is a learned behavior remarkable for its high degree of stereotypy. Nevertheless, adult birds display substantial rendition-by-rendition variation in the structure of individual song elements or "syllables." Previous work suggests that some of this variation is actively generated by the avian basal ganglia circuitry for purposes of motor exploration. However, it is unknown whether and how natural variations in premotor activity drive variations in syllable structure. Here, we recorded from the premotor nucleus robust nucleus of the arcopallium (RA) in Bengalese finches and measured whether neural activity covaried with syllable structure across multiple renditions of individual syllables. We found that variations in premotor activity were significantly correlated with variations in the acoustic features (pitch, amplitude, and spectral entropy) of syllables in approximately a quarter of all cases. In these cases, individual neural recordings predicted 8.5 +/- 0.3% (mean +/- SE) of the behavioral variation, and in some cases accounted for 25% or more of trial-by-trial variations in acoustic output. The prevalence and strength of neuron-behavior correlations indicate that each acoustic feature is controlled by a large ensemble of neurons that vary their activity in a coordinated manner. Additionally, we found that correlations with pitch (but not other features) were predominantly positive in sign, supporting a model of pitch production based on the anatomy and physiology of the vocal motor apparatus. Collectively, our results indicate that trial-by-trial variations in spectral structure are indeed under central neural control at the level of RA, consistent with the idea that such variation reflects motor exploration.

Flexible strategies for sensory integration during motor planning.

When planning target-directed reaching movements, human subjects combine visual and proprioceptive feedback to form two estimates of the arm's position: one to plan the reach direction, and another to convert that direction into a motor command. These position estimates are based on the same sensory signals but rely on different combinations of visual and proprioceptive input, suggesting that the brain weights sensory inputs differently depending on the computation being performed. Here we show that the relative weighting of vision and proprioception depends both on the sensory modality of the target and on the information content of the visual feedback, and that these factors affect the two stages of planning independently. The observed diversity of weightings demonstrates the flexibility of sensory integration and suggests a unifying principle by which the brain chooses sensory inputs so as to minimize errors arising from the transformation of sensory signals between coordinate frames.

Dopamine Depletion Affects Vocal Acoustics and Disrupts Sensorimotor Adaptation in Songbirds.

Dopamine is hypothesized to convey error information in reinforcement learning tasks with explicit appetitive or aversive cues. However, during motor skill learning feedback signals arise from an animal's evaluation of sensory feedback resulting 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 pars compacta (VTA/SNc) complex is necessary for vocal learning when songbirds modify their vocalizations to avoid hearing distorted auditory feedback (playbacks of white noise). However, it remains unclear whether dopaminergic signaling underlies vocal learning in response to more naturalistic errors (pitch-shifted feedback delivered via headphones). We used male Bengalese finches (Lonchura striata var. domestica) to test the hypothesis that the necessity of dopamine signaling is shared between the two types of learning. We combined 6-hydroxydopamine (6-OHDA) lesions of dopaminergic terminals within Area X, a basal ganglia nucleus critical for song learning, with a headphones learning paradigm that shifted the pitch of auditory feedback and compared their learning to that of unlesioned controls. We found that 6-OHDA lesions affected song behavior in two ways. First, over a period of days lesioned birds systematically lowered their pitch regardless of the presence or absence of auditory errors. Second, 6-OHDA lesioned birds also displayed severe deficits in sensorimotor learning in response to pitch-shifted feedback. Our results suggest roles for dopamine in both motor production and auditory error processing, and a shared mechanism underlying vocal learning in response to both distorted and pitch-shifted auditory feedback.

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 characterize 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.