Dopamine Depletion Affects Vocal Acoustics and Disrupts Sensorimotor Adaptation in Songbirds

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.

Mapping the vocal circuitry of Alston's singing mouse with pseudorabies virus

Vocalizations are often elaborate, rhythmically structured behaviors. Vocal motor patterns require close coordination of neural circuits governing the muscles of the larynx, jaw, and respiratory system. In the elaborate vocalization of Alston's singing mouse (Scotinomys teguina) each note of its rapid, frequency-modulated trill is accompanied by equally rapid modulation of breath and gape. To elucidate the neural circuitry underlying this behavior, we introduced the polysynaptic retrograde neuronal tracer pseudorabies virus (PRV) into the cricothyroid and digastricus muscles, which control frequency modulation and jaw opening, respectively. Each virus singly labels ipsilateral motoneurons (nucleus ambiguus for cricothyroid, and motor trigeminal nucleus for digastricus). We find that the two isogenic viruses heavily and bilaterally colabel neurons in the gigantocellular reticular formation, a putative central pattern generator. The viruses also show strong colabeling in compartments of the midbrain including the ventrolateral periaqueductal gray and the parabrachial nucleus, two structures strongly implicated in vocalizations. In the forebrain, regions important to social cognition and energy balance both exhibit extensive colabeling. This includes the paraventricular and arcuate nuclei of the hypothalamus, the lateral hypothalamus, preoptic area, extended amygdala, central amygdala, and the bed nucleus of the stria terminalis. Finally, we find doubly labeled neurons in M1 motor cortex previously described as laryngeal, as well as in the prelimbic cortex, which indicate these cortical regions play a role in vocal production. The progress of both viruses is broadly consistent with vertebrate-general patterns of vocal circuitry, as well as with circuit models derived from primate literature.

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, 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.

Deafening-induced rapid changes to spine synaptic connectivity in the adult avian vocal basal ganglia

The basal ganglia have been implicated in auditory-dependent vocal learning and plasticity in human and songbirds, but the underlying neural phenotype remains to be clarified. Here, using confocal imaging and three-dimensional electron microscopy, we investigated striatal structural plasticity in response to hearing loss in Area X, the avian vocal basal ganglia, in adult male zebra finch (Taeniopygia guttata). We observed a rapid elongation of dendritic spines, by approximately 13%, by day 3 after deafening, and a considerable increase in spine synapse density, by approximately 61%, by day 14 after deafening, compared with the controls with an intact cochlea. These findings reveal structural sensitivity of Area X to auditory deprivation and suggest that this striatal plasticity might contribute to deafening-induced changes to learned vocal behavior.

What Is the Role of Thalamostriatal Circuits in Learning Vocal Sequences?

Basal ganglia (BG) circuits integrate sensory and motor-related information from the cortex, thalamus, and midbrain to guide learning and production of motor sequences. Birdsong, like speech, is comprised of precisely sequenced vocal elements. Learning song sequences during development relies on Area X, a vocalization related region in the medial striatum of the songbird BG. Area X receives inputs from cortical-like pallial song circuits and midbrain dopaminergic circuits and sends projections to the thalamus. It has recently been shown that thalamic circuits also send substantial projections back to Area X. Here, we outline a gated-reinforcement learning model for how Area X may use signals conveyed by thalamostriatal inputs to direct song learning. Integrating conceptual advances from recent mammalian and songbird literature, we hypothesize that thalamostriatal pathways convey signals linked to song syllable onsets and offsets and influence striatal circuit plasticity via regulation of cholinergic interneurons (ChIs). We suggest that syllable sequence associated vocal-motor information from the thalamus drive precisely timed pauses in ChIs activity in Area X. When integrated with concurrent corticostriatal and dopaminergic input, this circuit helps regulate plasticity on medium spiny neurons (MSNs) and the learning of syllable sequences. We discuss new approaches that can be applied to test core ideas of this model and how associated insights may provide a framework for understanding the function of BG circuits in learning motor sequences.

Unilateral vocal nerve resection alters neurogenesis in the avian song system in a region-specific manner

New neurons born in the adult brain undergo a critical period soon after migration to their site of incorporation. During this time, the behavior of the animal may influence the survival or culling of these cells. In the songbird song system, earlier work suggested that adult-born neurons may be retained in the song motor pathway nucleus HVC with respect to motor progression toward a target song during juvenile song learning, seasonal song restructuring, and experimentally manipulated song variability. However, it is not known whether the quality of song per se, without progressive improvement, may also influence new neuron survival. To test this idea, we experimentally altered song acoustic structure by unilateral denervation of the syrinx, causing a poor quality song. We found no effect of aberrant song on numbers of new neurons in HVC, suggesting that song quality does not influence new neuron culling in this region. However, aberrant song resulted in the loss of left-side dominance in new neurons in the auditory region caudomedial nidopallium (NCM), and a bilateral decrease in new neurons in the basal ganglia nucleus Area X. Thus new neuron culling may be influenced by behavioral feedback in accordance with the function of new neurons within that region. We propose that studying the effects of singing behaviors on new neurons across multiple brain regions that differentially subserve singing may give rise to general rules underlying the regulation of new neuron survival across taxa and brain regions more broadly.

New roles for dopamine in motor skill acquisition: lessons from primates, rodents, and songbirds

Motor learning is a core aspect of human life and appears to be ubiquitous throughout the animal kingdom. Dopamine, a neuromodulator with a multifaceted role in synaptic plasticity, may be a key signaling molecule for motor skill learning. Though typically studied in the context of reward-based associative learning, dopamine appears to be necessary for some types of motor learning. Mesencephalic dopamine structures are highly conserved among vertebrates, as are some of their primary targets within the basal ganglia, a subcortical circuit important for motor learning and motor control. With a focus on the benefits of cross-species comparisons, this review examines how "model-free" and "model-based" computational frameworks for understanding dopamine's role in associative learning may be applied to motor learning. The hypotheses that dopamine could drive motor learning either by functioning as a reward prediction error, through passive facilitating of normal basal ganglia activity, or through other mechanisms are examined in light of new studies using humans, rodents, and songbirds. Additionally, new paradigms that could enhance our understanding of dopamine's role in motor learning by bridging the gap between the theoretical literature on motor learning in humans and other species are discussed.

Actor-critic reinforcement learning in the songbird

It feels rewarding to ace your opponent on match point. Here, we propose common mechanisms underlie reward and performance learning. First, when a singing bird unexpectedly hits the right note, its dopamine (DA) neurons are activated as when a thirsty monkey receives an unexpected juice reward. Second, these DA signals reinforce vocal variations much as they reinforce stimulus-response associations. Third, limbic inputs to DA neurons signal the predicted quality of song syllables much like they signal the predicted reward value of a place or a stimulus during foraging. Finally, songbirds may solve difficult problems in reinforcement learning - such as credit assignment and catastrophic forgetting - with node perturbation and consolidation of reinforced vocal patterns in motor cortical circuits. Consolidation occurs downstream of a canonical 'actor-critic' circuit motif that learns to maximize performance quality in essentially the same way it learns to maximize reward: by computing and learning from prediction errors.

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).

Network dynamics underlie learning and performance of birdsong

Understanding the sensorimotor control of the endless variety of human speech patterns stands as one of the apex problems in neuroscience. The capacity to learn - through imitation - to rapidly sequence vocal sounds in meaningful patterns is clearly one of the most derived of human behavioral traits. Selection pressure produced an analogous capacity in numerous species of vocal-learning birds, and due to an increasing appreciation for the cognitive and computational flexibility of avian cortex and basal ganglia, a general understanding of the forebrain network that supports the learning and production of birdsong is beginning to emerge. Here, we review recent advances in experimental studies of the zebra finch (Taeniopygia guttata), which offer new insights into the network dynamics that support this surprising analogue of human speech learning and production.