Cortical Processing of Conspecific Vocalizations in Zebra Finches Depends on the Early Acoustical Environment.

Sensory experience during development has lasting effects on perception and neural processing. Exposing animals to artificial stimuli early in life influences the tuning and functional organization of the auditory cortex, but less is known about how the rich acoustical environments experienced by vocal communicators affect the processing of complex vocalizations. Here, we show that in zebra finches (Taeniopygia guttata), a colonial-breeding songbird species, exposure to a naturalistic social-acoustical environment during development has a profound impact on cortical-level auditory responses to conspecific song. Compared to birds raised by pairs in acoustic isolation, birds raised in a breeding colony had higher average firing rates, selectivity, and discriminability, especially in the narrow-spiking, putatively inhibitory neurons of a higher-order auditory area, the caudomedial nidopallium (NCM). Neurons in colony-reared birds were also less correlated in their tuning and more efficient at encoding the spectrotemporal structure of conspecific song. These results suggest that the auditory cortex adapts to noisy, complex acoustical environments by strengthening inhibitory circuitry, functionally decoupling excitatory neurons while maintaining overall excitatory-inhibitory balance.

Rapid, Activity-Dependent Intrinsic Plasticity in the Developing Zebra Finch Auditory Cortex.

The acoustic environment an animal experiences early in life shapes the structure and function of its auditory system. This process of experience-dependent development is thought to be primarily orchestrated by potentiation and depression of synapses, but plasticity of intrinsic voltage dynamics may also contribute. Here, we show that in juvenile male and female zebra finches, neurons in a cortical-level auditory area, the caudal mesopallium (CM), can rapidly change their firing dynamics. This plasticity was only observed in birds that were reared in a complex acoustic and social environment, which also caused increased expression of the low-threshold potassium channel Kv1.1 in the plasma membrane and endoplasmic reticulum (ER). Intrinsic plasticity depended on activity, was reversed by blocking low-threshold potassium currents, and was prevented by blocking intracellular calcium signaling. Taken together, these results suggest that Kv1.1 is rapidly mobilized to the plasma membrane by activity-dependent elevation of intracellular calcium. This produces a shift in the excitability and temporal integration of CM neurons that may be permissive for auditory learning in complex acoustic environments during a crucial period for the development of vocal perception and production.SIGNIFICANCE STATEMENT Neurons can change not only the strength of their connections to other neurons, but also how they integrate synaptic currents to produce patterns of action potentials. In contrast to synaptic plasticity, the mechanisms and functional roles of intrinisic plasticity remain poorly understood. We found that neurons in the zebra finch auditory cortex can rapidly shift their spiking dynamics within a few minutes in response to intracellular stimulation. This plasticity involves increased conductance of a low-threshold potassium current associated with the Kv1.1 channel, but it only occurs in birds reared in a rich acoustic environment. Thus, auditory experience regulates a mechanism of neural plasticity that allows neurons to rapidly adapt their firing dynamics to stimulation.

Nonlinear effects of intrinsic dynamics on temporal encoding in a model of avian auditory cortex.

Neurons exhibit diverse intrinsic dynamics, which govern how they integrate synaptic inputs to produce spikes. Intrinsic dynamics are often plastic during development and learning, but the effects of these changes on stimulus encoding properties are not well known. To examine this relationship, we simulated auditory responses to zebra finch song using a linear-dynamical cascade model, which combines a linear spectrotemporal receptive field with a dynamical, conductance-based neuron model, then used generalized linear models to estimate encoding properties from the resulting spike trains. We focused on the effects of a low-threshold potassium current (KLT) that is present in a subset of cells in the zebra finch caudal mesopallium and is affected by early auditory experience. We found that KLT affects both spike adaptation and the temporal filtering properties of the receptive field. The direction of the effects depended on the temporal modulation tuning of the linear (input) stage of the cascade model, indicating a strongly nonlinear relationship. These results suggest that small changes in intrinsic dynamics in tandem with differences in synaptic connectivity can have dramatic effects on the tuning of auditory neurons.

Experience- and Sex-Dependent Intrinsic Plasticity in the Zebra Finch Auditory Cortex during Song Memorization.

For vocal communicators like humans and songbirds, survival and reproduction depend on highly developed auditory processing systems that can detect and differentiate nuanced differences in vocalizations, even amid noisy environments. Early auditory experience is critical to the development of these systems. In zebra finches and other songbirds, there is a sensitive period when young birds memorize a song that will serve as a model for their own vocal production. In addition to learning a specific tutor's song, the auditory system may also undergo critical developmental processes that support auditory perception of vocalizations more generally. Here, we investigate changes in intrinsic spiking dynamics among neurons in the caudal mesopallium, a cortical-level auditory area implicated in discriminating and learning species-specific vocalizations. A subset of neurons in this area only fire transiently at the onset of current injections (i.e., phasic firing), a dynamical property that can enhance the reliability and selectivity of neural responses to complex acoustic stimuli. At the beginning of the sensitive period, just after zebra finches have fledged from the nest, there is an increase in the proportion of caudal mesopallium neurons with phasic excitability, and in the proportion of neurons expressing Kv1.1, a low-threshold channel that facilitates phasic firing. This plasticity requires exposure to a complex, noisy environment and is greater in males, the only sex that sings in this species. This shift to more phasic dynamics is therefore an experience-dependent adaptation that could facilitate auditory processing in noisy, acoustically complex conditions during a key stage of vocal development.SIGNIFICANCE STATEMENT Auditory experience early in life shapes how humans and songbirds perceive the vocal communication sounds produced by their species. However, the changes that occur in the brain as this learning takes place are poorly understood. In this study, we show that in young zebra finches that are just beginning to learn the structure of their species' song, neurons in a key cortical area adapt their intrinsic firing patterns in response to the acoustic environment. In the complex, cocktail-party-like environment of a colony, more neurons adopt transient firing dynamics, which can facilitate neural coding of songs amid such challenging conditions.

A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex.

Birdsong is a complex vocal communication signal, and like humans, birds need to discriminate between similar sequences of sound with different meanings. The caudal mesopallium (CM) is a cortical-level auditory area implicated in song discrimination. CM neurons respond sparsely to conspecific song and are tolerant of production variability. Intracellular recordings in CM have identified a diversity of intrinsic membrane dynamics, which could contribute to the emergence of these higher-order functional properties. We investigated this hypothesis using a novel linear-dynamical cascade model that incorporated detailed biophysical dynamics to simulate auditory responses to birdsong. Neuron models that included a low-threshold potassium current present in a subset of CM neurons showed increased selectivity and coding efficiency relative to models without this current. These results demonstrate the impact of intrinsic dynamics on sensory coding and the importance of including the biophysical characteristics of neural populations in simulation studies.

Phasic and tonic cell types in the zebra finch auditory caudal mesopallium.

The caudal mesopallium (CM) is a cortical-level area in the songbird auditory pathway where selective, invariant responses to familiar songs emerge. To characterize the cell types that perform this computation, we made whole cell recordings from brain slices in juvenile zebra finches ( Taeniopygia guttata) of both sexes. We found three groups of putatively excitatory neurons with distinct firing patterns. Tonic cells produced sustained responses to depolarizing step currents, phasic cells produced only a few spikes at the onset, and an intermediate group was also phasic but responded for up to a few hundred milliseconds. Phasic cells had smaller dendritic fields, higher resting potentials, and strong low-threshold outward rectification. Pharmacological treatment with voltage-gated potassium channel antagonists 4-aminopyridine and α-dendrotoxin converted phasic to tonic firing. When stimulated with broadband currents, phasic cells fired coherently with frequencies up to 20-30 Hz, whereas tonic neurons were more responsive to frequencies around 0-10 Hz. The distribution of peak coherence frequencies was similar to the distribution of temporal modulation rates in zebra finch song. We reproduced these observations in a single-compartment biophysical model by varying cell size and the magnitude of a slowly inactivating, low-threshold potassium current ( ILT). These data suggest that intrinsic dynamics in CM are matched to the temporal statistics of conspecific song. NEW & NOTEWORTHY In songbirds, the caudal mesopallium is a key brain area involved in recognizing the songs of other individuals. This study identifies three cell types in this area with distinct firing patterns (tonic, phasic, and intermediate) that reflect differences in cell size and a low-threshold potassium current. The phasic-firing neurons, which do not have a counterpart in mammalian auditory cortex, are better able to follow rapid modulations at the frequencies found in song.

Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight.

Song in a Social and Sexual Context: Vocalizations Signal Identity and Rank in Both Sexes of a Cooperative Breeder.

In most songbirds, both sexes produce calls, or short vocalizations used to coordinate behaviors and maintain social cohesion. In contrast, songs are longer, more elaborate vocalizations typically only produced by males in behavioral contexts shaped by sexual selection operating through female choice. However, both males and females sing in many cooperatively breeding species, including the superb starling (Lamprotornis superbus). In this species, both sexes produce songs and calls composed of sequences of temporally discrete elements called motifs. Calls signal social group and individual identity, but the function of songs is currently unknown. Because superb starlings often sing in groups, song could be used not only in a sexual context, but also to signal identity and rank within the separate dominance hierarchies observed in males and females. To determine whether songs are used in mate attraction (sexually selected) and/or to influence social rank (socially selected), we compared song diversity with three potential indicators of fitness and dominance: social status, the number of seasons spent breeding, and age. We found that age is correlated with song diversity in both males and females, suggesting that (1) these signals serve similar purposes in both sexes, and (2) song diversity is likely the result of selection by both mutual mate choice and social competition. To test whether songs carry a signal of individuality, we applied spectrogram dynamic time warping to measure pairwise similarity among song motifs, and then calculated motif similarity within and between individuals. We found that motif similarity is higher within individuals than between individuals, suggesting that songs signal individual identity, which may help to establish social rank. These results are consistent with the hypothesis that superb starling vocal behavior in each sex is shaped by both social and sexual selection. Additionally, because call motifs are also used in songs, our data suggest that at least some vocal building blocks have evolved to convey multiple signaler traits and to facilitate complex social and sexual interactions in different contexts.

Silicon central pattern generators for cardiac diseases.

Cardiac rhythm management devices provide therapies for both arrhythmias and resynchronisation but not heart failure, which affects millions of patients worldwide. This paper reviews recent advances in biophysics and mathematical engineering that provide a novel technological platform for addressing heart disease and enabling beat-to-beat adaptation of cardiac pacing in response to physiological feedback. The technology consists of silicon hardware central pattern generators (hCPGs) that may be trained to emulate accurately the dynamical response of biological central pattern generators (bCPGs). We discuss the limitations of present CPGs and appraise the advantages of analog over digital circuits for application in bioelectronic medicine. To test the system, we have focused on the cardio-respiratory oscillators in the medulla oblongata that modulate heart rate in phase with respiration to induce respiratory sinus arrhythmia (RSA). We describe here a novel, scalable hCPG comprising physiologically realistic (Hodgkin-Huxley type) neurones and synapses. Our hCPG comprises two neurones that antagonise each other to provide rhythmic motor drive to the vagus nerve to slow the heart. We show how recent advances in modelling allow the motor output to adapt to physiological feedback such as respiration. In rats, we report on the restoration of RSA using an hCPG that receives diaphragmatic electromyography input and use it to stimulate the vagus nerve at specific time points of the respiratory cycle to slow the heart rate. We have validated the adaptation of stimulation to alterations in respiratory rate. We demonstrate that the hCPG is tuneable in terms of the depth and timing of the RSA relative to respiratory phase. These pioneering studies will now permit an analysis of the physiological role of RSA as well as its any potential therapeutic use in cardiac disease.

Estimating parameters and predicting membrane voltages with conductance-based neuron models.

Recent results demonstrate techniques for fully quantitative, statistical inference of the dynamics of individual neurons under the Hodgkin-Huxley framework of voltage-gated conductances. Using a variational approximation, this approach has been successfully applied to simulated data from model neurons. Here, we use this method to analyze a population of real neurons recorded in a slice preparation of the zebra finch forebrain nucleus HVC. Our results demonstrate that using only 1,500 ms of voltage recorded while injecting a complex current waveform, we can estimate the values of 12 state variables and 72 parameters in a dynamical model, such that the model accurately predicts the responses of the neuron to novel injected currents. A less complex model produced consistently worse predictions, indicating that the additional currents contribute significantly to the dynamics of these neurons. Preliminary results indicate some differences in the channel complement of the models for different classes of HVC neurons, which accords with expectations from the biology. Whereas the model for each cell is incomplete (representing only the somatic compartment, and likely to be missing classes of channels that the real neurons possess), our approach opens the possibility to investigate in modeling the plausibility of additional classes of channels the cell might possess, thus improving the models over time. These results provide an important foundational basis for building biologically realistic network models, such as the one in HVC that contributes to the process of song production and developmental vocal learning in songbirds.

Dynamical estimation of neuron and network properties III: network analysis using neuron spike times.

Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.

Flight calls signal group and individual identity but not kinship in a cooperatively breeding bird.

In many complex societies, intricate communication and recognition systems may evolve to help support both direct and indirect benefits of group membership. In cooperatively breeding species where groups typically comprise relatives, both learned and innate vocal signals may serve as reliable cues for kin recognition. Here, we investigated vocal communication in the plural cooperatively breeding superb starling, Lamprotornis superbus, where flight calls-short, stereotyped vocalizations used when approaching conspecifics-may communicate kin relationships, group membership, and/or individual identity. We found that flight calls were most similar within individual repertoires but were also more similar within groups than within the larger population. Although starlings responded differently to playback of calls from their own versus other neighboring and distant social groups, call similarity was uncorrelated with genetic relatedness. Additionally, immigrant females showed similar patterns to birds born in the study population. Together, these results suggest that flight calls are learned signals that reflect social association but may also carry a signal of individuality. Flight calls, therefore, provide a reliable recognition mechanism for groups and may also be used to recognize individuals. In complex societies comprising related and unrelated individuals, signaling individuality and group association, rather than kinship, may be a route to cooperation.

Pitch- and spectral-based dynamic time warping methods for comparing field recordings of harmonic avian vocalizations.

Quantitative measures of acoustic similarity can reveal patterns of shared vocal behavior in social species. Many methods for computing similarity have been developed, but their performance has not been extensively characterized in noisy environments and with vocalizations characterized by complex frequency modulations. This paper describes methods of bioacoustic comparison based on dynamic time warping (DTW) of the fundamental frequency or spectrogram. Fundamental frequency is estimated using a Bayesian particle filter adaptation of harmonic template matching. The methods were tested on field recordings of flight calls from superb starlings, Lamprotornis superbus, for how well they could separate distinct categories of call elements (motifs). The fundamental-frequency-based method performed best, but the spectrogram-based method was less sensitive to noise. Both DTW methods provided better separation of categories than spectrographic cross correlation, likely due to substantial variability in the duration of superb starling flight call motifs.

Emergence of selectivity and tolerance in the avian auditory cortex.

The ability to recognize auditory objects like words and bird songs is thought to depend on neural responses that are selective between categories of the objects and tolerant of variation within those categories. To determine whether a hierarchy of increasing selectivity and tolerance exists in the avian auditory system, we trained European starlings (Sturnus vulgaris) to differentially recognize sets of songs, then measured extracellular single unit responses under urethane anesthesia in six areas of the auditory cortex. Responses were analyzed with a novel, generalized linear mixed model that provides robust estimates of the variance in responses to different stimuli. There were significant differences between areas in selectivity, tolerance, and the effects of training. The L2b and L1 subdivisions of field L had the least selectivity and tolerance. The caudal nidopallium (NCM) and subdivision L3 of field L were more selective than other areas, whereas the medial and lateral caudal mesopallium were more tolerant than NCM or L2b. L3 had a multimodal distribution of tolerance. Sensitivity to songs that were familiar and those that were not also distinguished the responses of caudomedial mesopallium and NCM. There were significant differences across areas between neurons with wide and narrow spikes. Collectively these results do not fit the traditional hierarchical view of the avian auditory forebrain, but are consistent with emerging concepts homologizing avian cortical and neocortical circuitry. The results suggest a functional divergence within the cortex into processing streams that respond to complementary aspects of the variability in communicative sounds.

Song tutoring in presinging zebra finch juveniles biases a small population of higher-order song-selective neurons toward the tutor song.

We explored physiological changes correlated with song tutoring by recording the responses of caudal nidopallium neurons of zebra finches aged P21-P24 (days post hatching) to a broad spectrum of natural and synthetic stimuli. Those birds raised with their fathers tended to show behavioral evidence of song memorization but not of singing; thus auditory responses were not confounded by the birds' own vocalizations. In study 1, 37 of 158 neurons (23%) in 17 of 22 tutored and untutored birds were selective for only 1 of 10 stimuli comprising broadband signals, early juvenile songs and calls, female calls, and adult songs. Approximately 30% of the selective neurons (12/37 neurons in 9 birds) were selective for adult conspecific songs. All these were found in the song system nuclei HVC and paraHVC. Of 122 neurons (17 birds) in tutored birds, all of the conspecific song-selective neurons (8 neurons in 6 birds) were selective for the adult tutor song; none was selective for unfamiliar song. In study 2 with a different sampling strategy, we found that 11 of 12 song-selective neurons in 6 of 7 birds preferred the tutor song; none preferred unfamiliar or familiar conspecific songs. Most of these neurons were found in caudal lateral nidopallium (NCL) below HVC. Thus by the time a bird begins to sing, there are small numbers of tutor song-selective neurons distributed in several forebrain regions. We hypothesize that a small population of higher-order auditory neurons is innately selective for complex features of behaviorally relevant stimuli and these responses are modified by specific perceptual/social experience during development.

Dynamical estimation of neuron and network properties II: Path integral Monte Carlo methods.

Hodgkin-Huxley (HH) models of neuronal membrane dynamics consist of a set of nonlinear differential equations that describe the time-varying conductance of various ion channels. Using observations of voltage alone we show how to estimate the unknown parameters and unobserved state variables of an HH model in the expected circumstance that the measurements are noisy, the model has errors, and the state of the neuron is not known when observations commence. The joint probability distribution of the observed membrane voltage and the unobserved state variables and parameters of these models is a path integral through the model state space. The solution to this integral allows estimation of the parameters and thus a characterization of many biological properties of interest, including channel complement and density, that give rise to a neuron's electrophysiological behavior. This paper describes a method for directly evaluating the path integral using a Monte Carlo numerical approach. This provides estimates not only of the expected values of model parameters but also of their posterior uncertainty. Using test data simulated from neuronal models comprising several common channels, we show that short (<50 ms) intracellular recordings from neurons stimulated with a complex time-varying current yield accurate and precise estimates of the model parameters as well as accurate predictions of the future behavior of the neuron. We also show that this method is robust to errors in model specification, supporting model development for biological preparations in which the channel expression and other biophysical properties of the neurons are not fully known.

Effects of auditory recognition learning on the perception of vocal features in European starlings (Sturnus vulgaris).

Learning to recognize complex sensory signals can change the way they are perceived. European starlings (Sturnus vulgaris) recognize other starlings by their song, which consists of a series of complex, stereotyped motifs. Song recognition learning is accompanied by plasticity in secondary auditory areas, suggesting that perceptual learning is involved. Here, to investigate whether perceptual learning can be observed behaviorally, a same-different operant task was used to measure how starlings perceived small differences in motif structure. Birds trained to recognize conspecific songs were better at detecting variations in motifs from the songs they learned, even though this variation was not directly necessary to learn the associative task. Discrimination also improved as the reference stimulus was repeated multiple times. Perception of the much larger differences between different motifs was unaffected by training. These results indicate that sensory representations of motifs are enhanced when starlings learn to recognize songs.

Dynamical estimation of neuron and network properties I: variational methods.

We present a method for using measurements of membrane voltage in individual neurons to estimate the parameters and states of the voltage-gated ion channels underlying the dynamics of the neuron's behavior. Short injections of a complex time-varying current provide sufficient data to determine the reversal potentials, maximal conductances, and kinetic parameters of a diverse range of channels, representing tens of unknown parameters and many gating variables in a model of the neuron's behavior. These estimates are used to predict the response of the model at times beyond the observation window. This method of [Formula: see text] extends to the general problem of determining model parameters and unobserved state variables from a sparse set of observations, and may be applicable to networks of neurons. We describe an exact formulation of the tasks in nonlinear data assimilation when one has noisy data, errors in the models, and incomplete information about the state of the system when observations commence. This is a high dimensional integral along the path of the model state through the observation window. In this article, a stationary path approximation to this integral, using a variational method, is described and tested employing data generated using neuronal models comprising several common channels with Hodgkin-Huxley dynamics. These numerical experiments reveal a number of practical considerations in designing stimulus currents and in determining model consistency. The tools explored here are computationally efficient and have paths to parallelization that should allow large individual neuron and network problems to be addressed.

Representations of conspecific song by starling secondary forebrain auditory neurons: toward a hierarchical framework.

The functional organization giving rise to stimulus selectivity in higher-order auditory neurons remains under active study. We explored the selectivity for motifs, spectrotemporally distinct perceptual units in starling song, recording the responses of 96 caudomedial mesopallium (CMM) neurons in European starlings (Sturnus vulgaris) under awake-restrained and urethane-anesthetized conditions. A subset of neurons was highly selective between motifs. Selectivity was correlated with low spontaneous firing rates and high spike timing precision, and all but one of the selective neurons had similar spike waveforms. Neurons were further tested with stimuli in which the notes comprising the motifs were manipulated. Responses to most of the isolated notes were similar in amplitude, duration, and temporal pattern to the responses elicited by those notes in the context of the motif. For these neurons, we could accurately predict the responses to motifs from the sum of the responses to notes. Some notes were suppressed by the motif context, such that removing other notes from motifs unmasked additional excitation. Models of linear summation of note responses consistently outperformed spectrotemporal receptive field models in predicting responses to song stimuli. Tests with randomized sequences of notes confirmed the predictive power of these models. Whole notes gave better predictions than did note fragments. Thus in CMM, auditory objects (motifs) can be represented by a linear combination of excitation and suppression elicited by the note components of the object. We hypothesize that the receptive fields arise from selective convergence by inputs responding to specific spectrotemporal features of starling notes.

Receptive-field modification in rat visual cortex induced by paired visual stimulation and single-cell spiking.

Experience-dependent plasticity of visual cortical receptive fields (RFs) involves synaptic modifications in the underlying neural circuits, but the site and mechanism of these modifications remain to be elucidated. Using in vivo whole-cell recordings, we show that pairing visual stimulation at a given retinal location with spiking of a single neuron in developing rat visual cortex induces rapid RF modifications. The time course of the response to the visual stimulus at the paired RF location is altered, with an enhancement of the response preceding the spike time and a reduction following the spike. Such bidirectional modification is consistent with spike timing-dependent plasticity. Response modification also occurs at nearby locations, the direction and magnitude of which are correlated with the change at the paired location. In addition, changes at unpaired locations show a negative correlation with the initial strength of the response, which may facilitate rapid modification of the spatial RF profile.