Neural processing of auditory feedback during vocal practice in a songbird

Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers

Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.

A perspective on neuroethology: what the past teaches us about the future of neuroethology

For 100 years, the Journal of Comparative Physiology-A has significantly supported research in the field of neuroethology. The celebration of the journal's centennial is a great time point to appreciate the recent progress in neuroethology and to discuss possible avenues of the field. Animal behavior is the main source of inspiration for neuroethologists. This is illustrated by the huge diversity of investigated behaviors and species. To explain behavior at a mechanistic level, neuroethologists combine neuroscientific approaches with sophisticated behavioral analysis. The rapid technological progress in neuroscience makes neuroethology a highly dynamic and exciting field of research. To summarize the recent scientific progress in neuroethology, I went through all abstracts of the last six International Congresses for Neuroethology (ICNs 2010-2022) and categorized them based on the sensory modalities, experimental model species, and research topics. This highlights the diversity of neuroethology and gives us a perspective on the field's scientific future. At the end, I highlight three research topics that may, among others, influence the future of neuroethology. I hope that sharing my roots may inspire other scientists to follow neuroethological approaches.

Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations

In an ever-changing visual world, animals' survival depends on their ability to perceive and respond to rapidly changing motion cues. The primary visual cortex (V1) is at the forefront of this sensory processing, orchestrating neural responses to perturbations in visual flow. However, the underlying neural mechanisms that lead to distinct cortical responses to such perturbations remain enigmatic. In this study, our objective was to uncover the neural dynamics that govern V1 neurons' responses to visual flow perturbations using a biologically realistic computational model. By subjecting the model to sudden changes in visual input, we observed opposing cortical responses in excitatory layer 2/3 (L2/3) neurons, namely, depolarizing and hyperpolarizing responses. We found that this segregation was primarily driven by the competition between external visual input and recurrent inhibition, particularly within L2/3 and L4. This division was not observed in excitatory L5/6 neurons, suggesting a more prominent role for inhibitory mechanisms in the visual processing of the upper cortical layers. Our findings share similarities with recent experimental studies focusing on the opposing influence of top-down and bottom-up inputs in the mouse primary visual cortex during visual flow perturbations.

A neural circuit for vocal production responds to viscerosensory input in the songbird

Motor performance is monitored continuously by specialized brain circuits and used adaptively to modify behavior on a moment-to-moment basis and over longer time periods. During vocal behaviors, such as singing in songbirds, internal evaluation of motor performance relies on sensory input from the auditory and vocal-respiratory systems. Sensory input from the auditory system to the motor system, often referred to as auditory feedback, has been well studied in singing zebra finches (Taeniopygia guttata), but little is known about how and where nonauditory sensory feedback is evaluated. Here we show that brief perturbations in air sac pressure cause short-latency neural responses in the higher-order song control nucleus HVC (used as proper name), an area necessary for song learning and song production. Air sacs were briefly pressurized through a cannula in anesthetized or sedated adult male zebra finches, and neural responses were recorded in both nucleus parambigualis (PAm), a brainstem inspiratory center, and HVC, a cortical premotor nucleus. These findings show that song control nuclei in the avian song system are sensitive to perturbations directly targeted to vocal-respiratory, or viscerosensory, afferents and support a role for multimodal sensory feedback integration in modifying and controlling vocal control circuits.NEW & NOTEWORTHY This study presents the first evidence of sensory input from the vocal-respiratory periphery directly activating neurons in a motor circuit for vocal production in songbirds. It was previously thought that this circuit relies exclusively on sensory input from the auditory system, but we provide groundbreaking evidence for nonauditory sensory input reaching the higher-order premotor nucleus HVC, expanding our understanding of what sensory feedback may be available for vocal control.

Sequential maturation of stimulus-specific adaptation in the mouse lemniscal auditory system

Stimulus-specific adaptation (SSA), the reduction of neural activity to a common stimulus that does not generalize to other, rare stimuli, is an essential property of our brain. Although well characterized in adults, it is still unknown how it develops during adolescence and what neuronal circuits are involved. Using in vivo electrophysiology and optogenetics in the lemniscal pathway of the mouse auditory system, we observed SSA to be stable from postnatal day 20 (P20) in the inferior colliculus, to develop until P30 in the auditory thalamus and even later in the primary auditory cortex (A1). We found this maturation process to be experience-dependent in A1 but not in thalamus and to be related to alterations in deep but not input layers of A1. We also identified corticothalamic projections to be implicated in thalamic SSA development. Together, our results reveal different circuits underlying the sequential SSA maturation and provide a unique perspective to understand predictive coding and surprise across sensory systems.

Stimulus-Specific Prediction Error Neurons in Mouse Auditory Cortex

Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors (PEs), mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through nonpredictive mechanisms, such as the movement-based facilitation of a neuron's inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice of either sex to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal. Together, these findings reveal that cortical predictions about self-generated sounds have specificity in multiple simultaneous dimensions and that cortical prediction error neurons encode specific violations from expectation.SIGNIFICANCE STATEMENT Audette et. al record neural activity in the auditory cortex while mice perform a sound-generating forelimb movement and measure neural responses to sounds that violate an animal's expectation in different ways. They find that predictions about self-generated sounds are highly specific across multiple stimulus dimensions and that a population of typically nonsound-responsive neurons respond to sounds that violate an animal's expectation in a specific way. These results identify specific prediction error (PE) signals in the mouse auditory cortex and suggest that errors may be calculated early in sensory processing.

Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses

Predictive processing postulates the existence of prediction error neurons in cortex. Neurons with both negative and positive prediction error response properties have been identified in layer 2/3 of visual cortex, but whether they correspond to transcriptionally defined subpopulations is unclear. Here we used the activity-dependent, photoconvertible marker CaMPARI2 to tag neurons in layer 2/3 of mouse visual cortex during stimuli and behaviors designed to evoke prediction errors. We performed single-cell RNA-sequencing on these populations and found that previously annotated Adamts2 and Rrad layer 2/3 transcriptional cell types were enriched when photolabeling during stimuli that drive negative or positive prediction error responses, respectively. Finally, we validated these results functionally by designing artificial promoters for use in AAV vectors to express genetically encoded calcium indicators. Thus, transcriptionally distinct cell types in layer 2/3 that can be targeted using AAV vectors exhibit distinguishable negative and positive prediction error responses.

Spontaneous vocal coordination of vocalizations to water noise in rooks (Corvus frugilegus): An exploratory study

The ability to control one's vocal production is a major advantage in acoustic communication. Yet, not all species have the same level of control over their vocal output. Several bird species can interrupt their song upon hearing an external stimulus, but there is no evidence how flexible this behavior is. Most research on corvids focuses on their cognitive abilities, but few studies explore their vocal aptitudes. Recent research shows that crows can be experimentally trained to vocalize in response to a brief visual stimulus. Our study investigated vocal control abilities with a more ecologically embedded approach in rooks. We show that two rooks could spontaneously coordinate their vocalizations to a long-lasting stimulus (the sound of their small bathing pool being filled with a water hose), one of them adjusting roughly (in the second range) its vocalizations as the stimuli began and stopped. This exploratory study adds to the literature showing that corvids, a group of species capable of cognitive prowess, are indeed able to display good vocal control abilities.

Visual categories and concepts in the avian brain

Birds are excellent model organisms to study perceptual categorization and concept formation. The renewed focus on avian neuroscience has sparked an explosion of new data in the field. At the same time, our understanding of sensory and particularly visual structures in the avian brain has shifted fundamentally. These recent discoveries have revealed how categorization is mediated in the avian brain and has generated a theoretical framework that goes beyond the realm of birds. We review the contribution of avian categorization research-at the methodical, behavioral, and neurobiological levels. To this end, we first introduce avian categorization from a behavioral perspective and the common elements model of categorization. Second, we describe the functional and structural organization of the avian visual system, followed by an overview of recent anatomical discoveries and the new perspective on the avian 'visual cortex'. Third, we focus on the neurocomputational basis of perceptual categorization in the bird's visual system. Fourth, an overview of the avian prefrontal cortex and the prefrontal contribution to perceptual categorization is provided. The fifth section outlines how asymmetries of the visual system contribute to categorization. Finally, we present a mechanistic view of the neural principles of avian visual categorization and its putative extension to concept learning.

Where is the error? Hierarchical predictive coding through dendritic error computation

Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. We then discuss the correspondence of this model to experimentally observed connectivity patterns, plasticity, and dynamics in cortex.

Auditory processing neurons influence song evaluation and strength of mate preference in female songbirds

Animals use a variety of complex signaling mechanisms to convey an array of information that can be detected by conspecifics and heterospecifics. Receivers of those signals perceive that information and use it to direct their subsequent actions. Thus, communication such as that which occurs between senders and receivers of vocal communication signals can be a powerful model in which to investigate the neural basis of sensory perception and action initiation that underlie decision-making. In this study, we investigated how female songbirds perceive the quality of acoustic signals (songs) performed by males and use that information to express preference for one song among many possible alternatives. We use behavioral measurement of song preference before and after lesion-induced alteration of activity in an auditory processing area (caudal nidopallium, NC) for which we have previously described its interconnections with other auditory areas and downstream reward pathways. Our findings reveal that inactivating NC does not change a female's ability or willingness to perform behavioral indicators of mate choice, nor does it change their ability to identify the songs of individual males. However, lesioning NC does induce a decrease in the strength of song preference for specific males more than others. That decrease does not result in a complete elimination of preference, as female preferences for specific males are still evident but not as strongly expressed after lesioning of NC. Taken together, these data indicate that NC plays a role in a female's strength of preference in song evaluation and mate choice, and activity in NC is an important facet of mate choice.

A brain atlas of the carrion crow (Corvus corone)

Corvidae, passerine songbirds such as jays, crows, and ravens known as corvids, have become model systems for the study of avian cognition. The superior cognitive capabilities of corvids mainly emerge from a disproportionally large telencephalon found in these species. However, a systematic mapping of the neuroanatomy of the corvid brain, and the telencephalon in particular, is lacking so far. Here, we present a brain atlas of the carrion crow, Corvus corone, with special emphasis on the telencephalic pallium. We applied four staining techniques to brain slices (Nissl, myelin, combination of Nissl and myelin, and tyrosine hydroxylase targeting catecholaminergic neurons). This allowed us to identify brain nuclei throughout the brain and delineate the known pallial subdivisions termed hyperpallium, entopallium, mesopallium, nidopallium, arcopallium, and hippocampal complex. The extent of these subdivisions and brain nuclei are described according to stereotaxic coordinates. In addition, 3D depictions of pallial regions were reconstructed from these slices. While the overall organization of the carrion crow's brain matches other songbird brains, the relative proportions and expansions of associative pallial areas differ considerably in agreement with enhanced cognitive skills found in corvids. The presented global organization of the crow brain in stereotaxic coordinates will help to guide future neurobiological studies in corvids.

Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications

An influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare them with predictions thereof. A core element of this comparison is prediction-error neurons, the activity of which only changes upon mismatches between actual and predicted sensory stimuli. While it has been shown that these prediction-error neurons come in different variants, it is largely unresolved how they are simultaneously formed and shaped by highly interconnected neural networks. By using a computational model, we study the circuit-level mechanisms that give rise to different variants of prediction-error neurons. Our results shed light on the formation, refinement, and robustness of prediction-error circuits, an important step toward a better understanding of predictive processing.

Predictable sensory stimuli do not evoke significant responses in a subset of cortical excitatory neurons. Some of those neurons, however, change their activity upon mismatches between actual and predicted stimuli. Different variants of these prediction-error neurons exist, and they differ in their responses to unexpected sensory stimuli. However, it is unclear how these variants can develop and coexist in the same recurrent network and how they are simultaneously shaped by the astonishing diversity of inhibitory interneurons. Here, we study these questions in a computational network model with three types of inhibitory interneurons. We find that balancing excitation and inhibition in multiple pathways gives rise to heterogeneous prediction-error circuits. Dependent on the network’s initial connectivity and distribution of actual and predicted sensory inputs, these circuits can form different variants of prediction-error neurons that are robust to network perturbations and generalize to stimuli not seen during learning. These variants can be learned simultaneously via homeostatic inhibitory plasticity with low baseline firing rates. Finally, we demonstrate that prediction-error neurons can support biased perception, we illustrate a number of functional implications, and we discuss testable predictions.

Functional and ultrastructural analysis of reafferent mechanosensation in larval zebrafish

All animals need to differentiate between exafferent stimuli, which are caused by the environment, and reafferent stimuli, which are caused by their own movement. In the case of mechanosensation in aquatic animals, the exafferent inputs are water vibrations in the animal's proximity, which need to be distinguishable from the reafferent inputs arising from fluid drag due to locomotion. Both of these inputs are detected by the lateral line, a collection of mechanosensory organs distributed along the surface of the body. In this study, we characterize in detail how hair cells-the receptor cells of the lateral line-in zebrafish larvae discriminate between such reafferent and exafferent signals. Using dye labeling of the lateral line nerve, we visualize two parallel descending inputs that can influence lateral line sensitivity. We combine functional imaging with ultra-structural EM circuit reconstruction to show that cholinergic signals originating from the hindbrain transmit efference copies (copies of the motor command that cancel out self-generated reafferent stimulation during locomotion) and that dopaminergic signals from the hypothalamus may have a role in threshold modulation, both in response to locomotion and salient stimuli. We further gain direct mechanistic insight into the core components of this circuit by loss-of-function perturbations using targeted ablations and gene knockouts. We propose that this simple circuit is the core implementation of mechanosensory reafferent suppression in these young animals and that it might form the first instantiation of state-dependent modulation found at later stages in development.

Vocal Learning and Behaviors in Birds and Human Bilinguals: Parallels, Divergences and Directions for Research

Comparisons between the communication systems of humans and animals are instrumental in contextualizing speech and language into an evolutionary and biological framework and for illuminating mechanisms of human communication. As a complement to previous work that compares developmental vocal learning and use among humans and songbirds, in this article we highlight phenomena associated with vocal learning subsequent to the development of primary vocalizations (i.e., the primary language (L1) in humans and the primary song (S1) in songbirds). By framing avian “second-song” (S2) learning and use within the human second-language (L2) context, we lay the groundwork for a scientifically-rich dialogue between disciplines. We begin by summarizing basic birdsong research, focusing on how songs are learned and on constraints on learning. We then consider commonalities in vocal learning across humans and birds, in particular the timing and neural mechanisms of learning, variability of input, and variability of outcomes. For S2 and L2 learning outcomes, we address the respective roles of age, entrenchment, and social interactions. We proceed to orient current and future birdsong inquiry around foundational features of human bilingualism: L1 effects on the L2, L1 attrition, and L1<–>L2 switching. Throughout, we highlight characteristics that are shared across species as well as the need for caution in interpreting birdsong research. Thus, from multiple instructive perspectives, our interdisciplinary dialogue sheds light on biological and experiential principles of L2 acquisition that are informed by birdsong research, and leverages well-studied characteristics of bilingualism in order to clarify, contextualize, and further explore S2 learning and use in songbirds.

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution

Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by 'subjective valuation'-a deep inference about the precision or 'predictability' of the self-evidencing ('fitness-promoting') outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of 'selflessness'; in particular the 'totally selfless' states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.

Neurogenomic insights into the behavioral and vocal development of the zebra finch

The zebra finch (Taeniopygia guttata) is a socially monogamous and colonial opportunistic breeder with pronounced sexual differences in singing and plumage coloration. Its natural history has led to it becoming a model species for research into sex differences in vocal communication, as well as behavioral, neural and genomic studies of imitative auditory learning. As scientists tap into the genetic and behavioral diversity of both wild and captive lineages, the zebra finch will continue to inform research into culture, learning, and social bonding, as well as adaptability to a changing climate.

Effect of locomotion on the auditory steady state response of head-fixed mice

OBJECTIVES

Electroencephalographic (EEG) examinations of the auditory steady-state response (ASSR) can non-invasively probe cortical function to generate the gamma-band (40 Hz) oscillation, which is increasingly applied to the neurophysiological studies on the rodent models of psychiatric disorders. Though, it has been well established that the brain activities are significantly modulated by the behavioural state (such as locomotion), how the ASSR is affected remains unclear.

METHODS

We investigated the effect of locomotion by recording local field potential (LFP) evoked by 40-Hz click-train from multiple brain areas: auditory cortex (AC), medial geniculate body (MGB), hippocampus (HP) and prefrontal cortex (PFC), in head-fixed mice free to run on a treadmill. Comparisons were conducted on the LFPs during spontaneous movement and stationary conditions.

RESULTS

We found that in both the auditory (AC and MGB) and non-auditory areas (HP and PFC), locomotion reduced the initial negative deflection of LFP (early response during 0-100 ms from stimulus onset), and had no significant effect on the ASSR phase-locking to the late stimulus (100-500 ms).

CONCLUSIONS

Our results suggest that different neural mechanisms contribute to the early response and ASSR, and the ASSR is a more robust biomarker to investigate the pathogenesis of neuropsychiatric disorders.

Learning Speech Production and Perception through Sensorimotor Interactions

Action and perception are closely linked in many behaviors necessitating a close coordination between sensory and motor neural processes so as to achieve a well-integrated smoothly evolving task performance. To investigate the detailed nature of these sensorimotor interactions, and their role in learning and executing the skilled motor task of speaking, we analyzed ECoG recordings of responses in the high-γ band (70-150 Hz) in human subjects while they listened to, spoke, or silently articulated speech. We found elaborate spectrotemporally modulated neural activity projecting in both "forward" (motor-to-sensory) and "inverse" directions between the higher-auditory and motor cortical regions engaged during speaking. Furthermore, mathematical simulations demonstrate a key role for the forward projection in "learning" to control the vocal tract, beyond its commonly postulated predictive role during execution. These results therefore offer a broader view of the functional role of the ubiquitous forward projection as an important ingredient in learning, rather than just control, of skilled sensorimotor tasks.

An avian cortical circuit for chunking tutor song syllables into simple vocal-motor units

How are brain circuits constructed to achieve complex goals? The brains of young songbirds develop motor circuits that achieve the goal of imitating a specific tutor song to which they are exposed. Here, we set out to examine how song-generating circuits may be influenced early in song learning by a cortical region (NIf) at the interface between auditory and motor systems. Single-unit recordings reveal that, during juvenile babbling, NIf neurons burst at syllable onsets, with some neurons exhibiting selectivity for particular emerging syllable types. When juvenile birds listen to their tutor, NIf neurons are also activated at tutor syllable onsets, and are often selective for particular syllable types. We examine a simple computational model in which tutor exposure imprints the correct number of syllable patterns as ensembles in an interconnected NIf network. These ensembles are then reactivated during singing to train a set of syllable sequences in the motor network.

Young songbirds learn to imitate their parents’ songs. Here, the authors find that, in baby birds, neurons in a brain region at the interface of auditory and motor circuits signal the onsets of song syllables during both tutoring and babbling, suggesting a specific neural mechanism for vocal imitation.

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.

Learning prediction error neurons in a canonical interneuron circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

Neurotelemetry Reveals Putative Predictive Activity in HVC during Call-Based Vocal Communications in Zebra Finches

Premotor predictions facilitate vocal interactions. Here, we study such mechanisms in the forebrain nucleus HVC (proper name), a cortex-like sensorimotor area of songbirds, otherwise known for being essential for singing in zebra finches. To study the role of the HVC in calling interactions between male and female mates, we used wireless telemetric systems for simultaneous measurement of neuronal activity of male zebra finches and vocalizations of males and females that freely interact with each other. In a non-social context, male HVC neurons displayed stereotypic premotor activity in relation to active calling and showed auditory-evoked activity to hearing of played-back female calls. In a social context, HVC neurons displayed auditory-evoked activity to hearing of female calls only if that neuron showed activity preceding the upcoming female calls. We hypothesize that this activity preceding the auditory-evoked activity in the male HVC represents a neural correlate of behavioral anticipation, predictive activity that helps to coordinate vocal communication between social partners.SIGNIFICANCE STATEMENT Most social-living vertebrates produce large numbers of calls per day, and the calls have prominent roles in social interactions. Here, we show neuronal mechanisms that are active during call-based vocal communication of zebra finches, a highly social songbird species. HVC, a forebrain nucleus known for its importance in control of learned vocalizations of songbirds, displays predictive activity that may enable the male to adjust his own calling pattern to produce very fast sequences of male female call exchanges.

Optimal spectral templates for triggered feedback experiments

In the field of songbird neuroscience, researchers have used playback of aversive noise bursts to drive changes in song behavior for specific syllables within a bird’s song. Typically, a short (~5–10 msec) slice of the syllable is selected for targeting and the average spectrum of the slice is used as a template. Sounds that are sufficiently close to the template are considered a match. If other syllables have portions that are spectrally similar to the target, false positive errors will weaken the operant contingency. We present a gradient descent method for template optimization that increases the separation in distance between target and distractors slices, greatly improving targeting accuracy. Applied to songs from five adult Bengalese finches, the fractional reduction in errors for sub-syllabic slices was 51.54±22.92%. At the level of song syllables, we use an error metric that controls for the vastly greater number of distractors vs. target syllables. Setting 5% average error (misses + false positives) as a minimal performance criterion, the number of targetable syllables increased from 3 to 16 out of 61 syllables. At 10% error, targetable syllables increased from 11 to 26. By using simple and robust linear discriminant methods, the algorithm reaches near asymptotic performance when using 10 songs as training data, and the error increases by <2.3% on average when using only a single song for training. Targeting is temporally precise, with average jitter of 3.33 msec for the 16 accurately targeted syllables. Because the algorithm is concerned only with the problem of template selection, it can be used as a simple and robust front end for existing hardware and software implementations for triggered feedback.

Listening to music while running alters ground reaction forces: a study of acute exposure to varying speed and loudness levels in young women and men

PURPOSE

Music listening while running enhances physiological and psychological features, resulting in a more enjoyable experience. The possible influence of music on ground reaction forces (GRF) during running, however, is unknown. Considering the 'distracting' role of music on runner's attention, we hypothesized that music would cover foot impacts against the ground. This study verified such hypothesis by testing the effects of different music volumes while running at different velocities.

METHODS

Fifty fit volunteers (F:M = 22:8; 23 ± 2 years) performed 2-min running stints over 3 random conditions (80-dB, 85-dB music; 'no music'), at 3 velocities (8, 10, 12 km/h). Participants ran on a sensorized treadmill that recorded GRF during all experiments.

RESULTS

Listening to 85-dB music resulted in greater GRF at 8 (p = 0.0005) and 10 km/h (p = 0.04) but not 12 km/h (p = 0.35) and not with 80-dB volume. Gender-based analyses revealed significant Condition × gender interactions only for 85-dB music vs. 'no music'. Bonferroni-adjusted comparisons revealed significant music-induced increases in GRF only in men at 8 km/h (+ 4.1 kg/cm², p < 0.0005; women: + 0.8 kg/cm², p = 0.47) and 10 km/h (+ 3.3 kg/cm², p = 0.004; women: + 0.8 kg/cm², p = 0.51) but not at 12 km/h.

CONCLUSION

In active men, listening to loud music while running results in increased GRF, whereas no effect was observed in women. The lack of music effect in women may be related to structural factors, such as larger hip width-to-femoral length ratio, possibly resulting in different loading patterns. The present preliminary findings introduce high-volume music listening as a new potential risk factor for injury in young runners.

In vivo assessment of the neural substrate linked with vocal imitation accuracy

Human speech and bird song are acoustically complex communication signals that are learned by imitation during a sensitive period early in life. Although the brain areas indispensable for speech and song learning are known, the neural circuits important for enhanced or reduced vocal performance remain unclear. By combining in vivo structural Magnetic Resonance Imaging with song analyses in juvenile male zebra finches during song learning and beyond, we reveal that song imitation accuracy correlates with the structural architecture of four distinct brain areas, none of which pertain to the song control system. Furthermore, the structural properties of a secondary auditory area in the left hemisphere, are capable to predict future song copying accuracy, already at the earliest stages of learning, before initiating vocal practicing. These findings appoint novel brain regions important for song learning outcome and inform that ultimate performance in part depends on factors experienced before vocal practicing.

Memory circuits for vocal imitation

Many complex behaviors exhibited by social species are first learned by imitating the behavior of other more experienced individuals. Speech and language are the most widely appreciated behaviors learned in this way. Vocal imitation in songbirds is perhaps the best studied socially transmitted behavior, and research over the past few years has begun to crack the circuit mechanisms for how songbirds learn from vocal models. Studies in zebra finches are revealing an unexpected and essential role for premotor cortical circuits in forming the behavioral-goal memories used to guide song imitation, challenging the view that song memories used for imitation are stored in auditory circuits. Here, we provide a summary of this recent progress focusing on the What, Where, and How of tutor song memory, and propose a circuit hypothesis for song learning based on these recent findings.

Discrete Evaluative and Premotor Circuits Enable Vocal Learning in Songbirds

Virtuosic motor performance requires the ability to evaluate and modify individual gestures within a complex motor sequence. Where and how the evaluative and premotor circuits operate within the brain to enable such temporally precise learning is poorly understood. Songbirds can learn to modify individual syllables within their complex vocal sequences, providing a system for elucidating the underlying evaluative and premotor circuits. We combined behavioral and optogenetic methods to identify 2 afferents to the ventral tegmental area (VTA) that serve evaluative roles in syllable-specific learning and to establish that downstream cortico-basal ganglia circuits serve a learning role that is only premotor. Furthermore, song performance-contingent optogenetic stimulation of either VTA afferent was sufficient to drive syllable-specific learning, and these learning effects were of opposite valence. Finally, functional, anatomical, and molecular studies support the idea that these evaluative afferents bidirectionally modulate VTA dopamine neurons to enable temporally precise vocal learning.

Auditory-Motor Matching in Vocal Recognition and Imitative Learning

Songbirds possess mirror neurons (MNs) activating during the perception and execution of specific features of songs. These neurons are located in high vocal center (HVC), a premotor nucleus implicated in song perception, production and learning, making worth to inquire their properties and functions in vocal recognition and imitative learning. By integrating a body of brain and behavioral data, we discuss neurophysiology, anatomical, computational properties and possible functions of songbird MNs. We state that the neurophysiological properties of songbird MNs depends on sensorimotor regions that are outside the auditory neural system. Interestingly, songbirds MNs can be the result of the specific type of song representation possessed by some songbird species. At the functional level, we discuss whether songbird MNs are involved in others' song recognition, by dissecting the function of recognition in various different but possible overlapping processes: action-oriented perception, discriminative-oriented perception and identification of the signaler. We conclude that songbird MNs may be involved in recognizing other singer's vocalizations, while their role in imitative learning still require to solve how auditory feedback are used to correct own vocal performance to match the tutor song. Finally, we compare songbird and human mirror responses, hypothesizing a case of convergent evolution, and proposing new experimental directions.

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.

First Error-Based Supervised Learning Algorithm for Spiking Neural Networks

Neural circuits respond to multiple sensory stimuli by firing precisely timed spikes. Inspired by this phenomenon, the spike timing-based spiking neural networks (SNNs) are proposed to process and memorize the spatiotemporal spike patterns. However, the response speed and accuracy of the existing learning algorithms of SNNs are still lacking compared to the human brain. To further improve the performance of learning precisely timed spikes, we propose a new weight updating mechanism which always adjusts the synaptic weights at the first wrong output spike time. The proposed learning algorithm can accurately adjust the synaptic weights that contribute to the membrane potential of desired and non-desired firing time. Experimental results demonstrate that the proposed algorithm shows higher accuracy, better robustness, and less computational resources compared with the remote supervised method (ReSuMe) and the spike pattern association neuron (SPAN), which are classic sequence learning algorithms. In addition, the SNN-based computational model equipped with the proposed learning method achieves better recognition results in speech recognition task compared with other bio-inspired baseline systems.

Songbird Ventral Pallidum Sends Diverse Performance Error Signals to Dopaminergic Midbrain

Motor skills improve with practice, requiring outcomes to be evaluated against ever-changing performance benchmarks, yet it remains unclear how performance error signals are computed. Here, we show that the songbird ventral pallidum (VP) is required for song learning and sends diverse song timing and performance error signals to the ventral tegmental area (VTA). Viral tracing revealed inputs to VP from auditory and vocal motor thalamus, auditory and vocal motor cortex, and VTA. Our findings show that VP circuits, commonly associated with hedonic functions, signal performance error during motor sequence learning.

Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types

GABAergic interneurons play an important role in shaping the activity of excitatory pyramidal cells (PCs). How the various inhibitory cell types contribute to neuronal information processing, however, is not resolved. Here, we propose a functional role for a widespread network motif consisting of parvalbumin- (PV), somatostatin- (SOM) and vasoactive intestinal peptide (VIP)-expressing interneurons. Following the idea that PV and SOM interneurons control the distribution of somatic and dendritic inhibition onto PCs, we suggest that mutual inhibition between VIP and SOM cells translates weak inputs to VIP interneurons into large changes of somato-dendritic inhibition of PCs. Using a computational model, we show that the neuronal and synaptic properties of the circuit support this hypothesis. Moreover, we demonstrate that the SOM-VIP motif allows transient inputs to persistently switch the circuit between two processing modes, in which top-down inputs onto apical dendrites of PCs are either integrated or cancelled.

Neurons in the brain can be classified as excitatory or inhibitory based on whether they activate or deactivate the cells to whom they send signals. Compared to their excitatory counterpart, inhibitory neurons present themselves as a wild diversity of cell classes. It is broadly believed that these classes serve different purposes, but as of now, those are poorly understood. In this article, we suggest how an intricate interplay of three inhibitory cell classes can control whether internal signals—such as predictions, memory signals or motor commands—are taken into account when sensory signals are interpreted. Using a mathematical model and computer simulations, we show that such internal signals can be shut down by regulating which inhibitory cell types are active, and that the interaction of different cell classes allows weak control signals to do so.

Differential developmental changes in cortical representations of auditory-vocal stimuli in songbirds

Procedural skill learning requires iterative comparisons between feedback of self-generated motor output and a goal sensorimotor pattern. In juvenile songbirds, neural representations of both self-generated behaviors (each bird's own immature song) and the goal motor pattern (each bird's adult tutor song) are essential for vocal learning, yet little is known about how these behaviorally relevant stimuli are encoded. We made extracellular recordings during song playback in anesthetized juvenile and adult zebra finches ( Taeniopygia guttata) in adjacent cortical regions RA (robust nucleus of the arcopallium), AId (dorsal intermediate arcopallium), and RA cup, each of which is well situated to integrate auditory-vocal information: RA is a motor cortical region that drives vocal output, AId is an adjoining cortical region whose projections converge with basal ganglia loops for song learning in the dorsal thalamus, and RA cup surrounds RA and receives inputs from primary and secondary auditory cortex. We found strong developmental differences in neural selectivity within RA, but not in AId or RA cup. Juvenile RA neurons were broadly responsive to multiple songs but preferred juvenile over adult vocal sounds; in addition, spiking responses lacked consistent temporal patterning. By adulthood, RA neurons responded most strongly to each bird's own song with precisely timed spiking activity. In contrast, we observed a complete lack of song responsivity in both juvenile and adult AId, even though this region receives song-responsive inputs. A surprisingly large proportion of sites in RA cup of both juveniles and adults did not respond to song playback, and responsive sites showed little evidence of song selectivity. NEW & NOTEWORTHY Motor skill learning entails changes in selectivity for behaviorally relevant stimuli across cortical regions, yet the neural representation of these stimuli remains understudied. We investigated how information important for vocal learning in zebra finches is represented in regions analogous to infragranular layers of motor and auditory cortices during vs. after the developmentally regulated learning period. The results provide insight into how neurons in higher level stages of cortical processing represent stimuli important for motor skill learning.

Predictive Processing: A Canonical Cortical Computation

This perspective describes predictive processing as a computational framework for understanding cortical function in the context of emerging evidence, with a focus on sensory processing. We discuss how the predictive processing framework may be implemented at the level of cortical circuits and how its implementation could be falsified experimentally. Lastly, we summarize the general implications of predictive processing on cortical function in healthy and diseased states.

A cortical filter that learns to suppress the acoustic consequences of movement

Sounds can arise from the environment and also predictably from many of our own movements, such as vocalizing, walking, or playing music. The capacity to anticipate these movement-related (reafferent) sounds and distinguish them from environmental sounds is essential for normal hearing^1,2, but the neural circuits that learn to anticipate the often arbitrary and changeable sounds that result from our movements remain largely unknown. Here we developed an acoustic virtual reality (aVR) system in which a mouse learned to associate a novel sound with its locomotor movements, allowing us to identify the neural circuit mechanisms that learn to suppress reafferent sounds and to probe the behavioural consequences of this predictable sensorimotor experience. We found that aVR experience gradually and selectively suppressed auditory cortical responses to the reafferent frequency, in part by strengthening motor cortical activation of auditory cortical inhibitory neurons that respond to the reafferent tone. This plasticity is behaviourally adaptive, as aVR-experienced mice showed an enhanced ability to detect non-reafferent tones during movement. Together, these findings describe a dynamic sensory filter that involves motor cortical inputs to the auditory cortex that can be shaped by experience to selectively suppress the predictable acoustic consequences of movement.

How Do Expectations Shape Perception?

Perception and perceptual decision-making are strongly facilitated by prior knowledge about the probabilistic structure of the world. While the computational benefits of using prior expectation in perception are clear, there are myriad ways in which this computation can be realized. We review here recent advances in our understanding of the neural sources and targets of expectations in perception. Furthermore, we discuss Bayesian theories of perception that prescribe how an agent should integrate prior knowledge and sensory information, and investigate how current and future empirical data can inform and constrain computational frameworks that implement such probabilistic integration in perception.

Quantifying syringeal dynamics in vitro using electroglottography

The complex and elaborate vocalizations uttered by many of the 10,000 extant bird species are considered a major driver in their evolutionary success, warranting study of the underlying mechanisms of vocal production. Additionally, birdsong has developed into a highly productive model system for vocal imitation learning and motor control, where, in contrast to humans, we have experimental access to the entire neuromechanical control loop. In human voice production, complex laryngeal geometry, vocal fold tissue properties, airflow and laryngeal musculature all interact to ultimately control vocal fold kinematics. Quantifying vocal fold kinematics is thus critical to understanding neuromechanical control of voiced sound production, but in vivo imaging of vocal fold kinematics in birds is experimentally challenging. Here, we adapted and tested electroglottography (EGG) as a novel tool for examining vocal fold kinematics in the avian vocal organ, the syrinx. We furthermore imaged and quantified syringeal kinematics in the pigeon (Columba livia) syrinx with unprecedented detail. Our results show that EGG signals predict (1) the relative amount of contact between the avian equivalent of vocal folds and (2) essential parameters describing vibratory kinematics, such as fundamental frequency, and timing of syringeal opening and closing events. As such, EGG provides novel opportunities for measuring syringeal vibratory kinematic parameters in vivo Furthermore, the opportunity for imaging syringeal vibratory kinematics from multiple planar views (horizontal and coronal) simultaneously promotes birds as an excellent model system for studying kinematics and control of voiced sound production in general, including in humans and other mammals.

How Movement Modulates Hearing

Hearing is often viewed as a passive process: Sound enters the ear, triggers a cascade of activity through the auditory system, and culminates in an auditory percept. In contrast to a passive process, motor-related signals strongly modulate the auditory system from the eardrum to the cortex. The motor modulation of auditory activity is most well documented during speech and other vocalizations but also can be detected during a wide variety of other sound-generating behaviors. An influential idea is that these motor-related signals suppress neural responses to predictable movement-generated sounds, thereby enhancing sensitivity to environmental sounds during movement while helping to detect errors in learned acoustic behaviors, including speech and musicianship. Findings in humans, monkeys, songbirds, and mice provide new insights into the circuits that convey motor-related signals to the auditory system, while lending support to the idea that these signals function predictively to facilitate hearing and vocal learning.

Contextual signals in visual cortex

Vision is an active process. What we perceive strongly depends on our actions, intentions and expectations. During visual processing, these internal signals therefore need to be integrated with the visual information from the retina. The mechanisms of how this is achieved by the visual system are still poorly understood. Advances in recording and manipulating neuronal activity in specific cell types and axonal projections together with tools for circuit tracing are beginning to shed light on the neuronal circuit mechanisms of how internal, contextual signals shape sensory representations. Here we review recent work, primarily in mice, that has advanced our understanding of these processes, focusing on contextual signals related to locomotion, behavioural relevance and predictions.

The function and failure of sensory predictions

Humans and other primates are equipped with neural mechanisms that allow them to automatically make predictions about future events, facilitating processing of expected sensations and actions. Prediction-driven control and monitoring of perceptual and motor acts are vital to normal cognitive functioning. This review provides an overview of corollary discharge mechanisms involved in predictions across sensory modalities and discusses consequences of predictive coding for cognition and behavior. Converging evidence now links impairments in corollary discharge mechanisms to neuropsychiatric symptoms such as hallucinations and delusions. We review studies supporting a prediction-failure hypothesis of perceptual and cognitive disturbances. We also outline neural correlates underlying prediction function and failure, highlighting similarities across the visual, auditory, and somatosensory systems. In linking basic psychophysical and psychophysiological evidence of visual, auditory, and somatosensory prediction failures to neuropsychiatric symptoms, our review furthers our understanding of disease mechanisms.

Perception as a Route for Motor Skill Learning: Perspectives from Neuroscience

Learning a motor skill requires physical practice that engages neural networks involved in movement. These networks have also been found to be engaged during perception of sensory signals associated with actions. Nonetheless, despite extensive evidence for the existence of such sensory-evoked neural activity in motor pathways, much less is known about their contribution to learning and actual changes in behavior. Primate studies usually involve an overlearned task while studies in humans have largely focused on characterizing activity of the action observation network (AON) in the context of action understanding, theory of mind, and social interactions. Relatively few studies examined neural plasticity induced by perception and its role in transfer of motor knowledge. Here, we review this body of literature and point to future directions for the development of alternative, physiologically grounded ways in which sensory signals could be harnessed to improve motor skills.

The Potential Effect of Forbrain as an Altered Auditory Feedback Device

PURPOSE

The purpose of this study was to run a proof of concept on a new commercially available device, Forbrain® (Sound For Life Ltd/Soundev, Luxemburg, model UN38.3), to test whether it can modulate the speech of its users.

METHOD

Participants were instructed to read aloud a text of their choice during 3 experimental phases: baseline, test, and posttest, while wearing a Forbrain® headset. Critically, for half of the participants (Forbrain group), the device was turned on during the test phase, whereas for the other half (control group), the device was kept off. Voice recordings were analyzed to derive 6 quantitative measures of voice quality over each of the phases of the experiment.

RESULTS

A significant Group × Phase interaction was obtained for the smoothed cepstral peak prominence, a measure of voice harmony, and for the trendline of the long-term average spectrum, a measure of voice robustness, this latter surviving Bonferroni correction for multiple comparisons.

CONCLUSIONS

The results of this study indicate the effectiveness of Forbrain® in modifying the speech of its users. It is suggested that Forbrain® works as an altered auditory feedback device. It may hence be used as a clinical device in speech therapy clinics, yet further studies are warranted to test its usefulness in clinical groups.

A Basal Ganglia Circuit Sufficient to Guide Birdsong Learning

Learning vocal behaviors, like speech and birdsong, is thought to rely on continued performance evaluation. Whether candidate performance evaluation circuits in the brain are sufficient to guide vocal learning is not known. Here, we test the sufficiency of VTA projections to the vocal basal ganglia in singing zebra finches, a songbird species that learns to produce a complex and stereotyped multi-syllabic courtship song during development. We optogenetically manipulate VTA axon terminals in singing birds contingent on how the pitch of an individual song syllable is naturally performed. We find that optical inhibition and excitation of VTA terminals are each sufficient to reliably guide learned changes in song. Inhibition and excitation have opponent effects on future performances of targeted song syllables, consistent with positive and negative reinforcement of performance outcomes. These findings define a central role for reinforcement mechanisms in learning vocalizations and demonstrate minimal circuit elements for learning vocal behaviors. VIDEO ABSTRACT.