Electrophysiological characterization and computational models of HVC neurons in the zebra finch

Daily high doses of atorvastatin alter neuronal morphology in a juvenile songbird model

Statins are highly effective and widely prescribed cholesterol lowering drugs. However, statins cross the blood-brain barrier and decrease neural cholesterol in animal models, raising concern that long-term statin use may impact cholesterol-dependent structures and functions in the brain. Cholesterol is a fundamental component of cell membranes and experimentally decreasing membrane cholesterol has been shown to alter cell morphology in vitro. In addition, brain regions that undergo adult neurogenesis rely on local brain cholesterol for the manufacture of new neuronal membranes. Thus neurogenesis may be particularly vulnerable to long-term statin use. Here we asked whether oral statin treatment impacts neurogenesis in juveniles, either by decreasing numbers of new cells formed or altering the structure of new neurons. The use of statins in children and adolescents has received less attention than in older adults, with few studies on potential unintended effects in young brains. We examined neurons in the juvenile zebra finch songbird in telencephalic regions that function in song perception and memory (caudomedial nidopallium, NCM) and song production (HVC). Birds received either 40 mg/kg of atorvastatin in water or water vehicle once daily for 2-3 months until they reached adulthood. We labeled newborn cells using systemic injections of bromodeoxyuridine (BrdU) and quantified cells double-labeled with antibodies for BrdU and the neuron-specific protein Hu 30-32 days post mitosis. We also quantified a younger cohort of new neurons in the same birds using antibody to the neuronal protein doublecortin (DCX). We then compared numbers of new neurons and soma morphology of BrdU + /Hu+ neurons between statin-treated and control birds. We did not find an effect of statins on the density of newly formed neurons in either brain region, suggesting that statin treatment did not impact neurogenesis or young neuron survival in our paradigm. However, we found that neuronal soma morphology differed significantly between statin-treated and control birds. Somata of BrdU + /Hu+ (30-32 day old) neurons were flatter and had more furrowed contours in statin-treated birds relative to controls. In a larger, heterogeneous cohort of non-birthdated BrdU-/Hu+ neurons, largely born prior to statin treatment, somata were smaller in statin-treated birds than in controls. Our findings indicate that atorvastatin may affect neural cytoarchitecture in both newly formed and mature neurons, perhaps as a consequence of decreased cholesterol availability in the brain.

Differential behavioral engagement of inhibitory interneuron subtypes in the zebra finch brain

Inhibitory interneurons are highly heterogeneous circuit elements often characterized by cell biological properties, but how these factors relate to specific roles underlying complex behavior remains poorly understood. Using chronic silicon probe recordings, we demonstrate that distinct interneuron groups perform different inhibitory roles within HVC, a song production circuit in the zebra finch forebrain. To link these functional subtypes to molecular identity, we performed two-photon targeted electrophysiological recordings of HVC interneurons followed by post hoc immunohistochemistry of subtype-specific markers. We find that parvalbumin-expressing interneurons are highly modulated by sensory input and likely mediate auditory gating, whereas a more heterogeneous set of somatostatin-expressing interneurons can strongly regulate activity based on arousal. Using this strategy, we uncover important cell-type-specific network functions in the context of an ethologically relevant motor skill.

Model of the HVC neural network as a song motor in zebra finch

The nucleus HVC within the avian song system produces crystalized instructions which lead to precise, learned vocalization in zebra finches (Taeniopygia guttata). This paper proposes a model of the HVC neural network based on the physiological properties of individual HVC neurons, their synaptic interactions calibrated by experimental measurements, as well as the synaptic signal into this region which triggers song production. This neural network model comprises of two major neural populations in this area: neurons projecting to the nucleus RA and interneurons. Each single neuron model of HVCRA is constructed with conductance-based ion currents of fast Na+ and K+ and a leak channel, while the interneuron model includes extra transient Ca2+ current and hyperpolarization-activated inward current. The synaptic dynamics is formed with simulated delivered neurotransmitter pulses from presynaptic cells and neurotransmitter receptor opening rates of postsynaptic neurons. We show that this network model qualitatively exhibits observed electrophysiological behaviors of neurons independent or in the network, as well as the importance of bidirectional interactions between the HVCRA neuron and the HVCI neuron. We also simulate the pulse input from A11 neuron group to HVC. This signal successfully suppresses the interneuron, which leads to sequential firing of projection neurons that matches measured burst onset, duration, and spike quantities during the zebra finch motif. The result provides a biophysically based model characterizing the dynamics and functions of the HVC neural network as a song motor, and offers a reference for synaptic coupling strength in the avian brain.

Bursts from the past: Intrinsic properties link a network model to zebra finch song

Neuronal intrinsic excitability is a mechanism implicated in learning and memory that is distinct from synaptic plasticity. Prior work in songbirds established that intrinsic properties (IPs) of premotor basal-ganglia-projecting neurons (HVC X ) relate to learned song. Here we find that temporal song structure is related to specific HVC X IPs: HVC X from birds who sang longer songs including longer invariant vocalizations (harmonic stacks) had IPs that reflected increased post-inhibitory rebound. This suggests a rebound excitation mechanism underlying the ability of HVC X neurons to integrate over long periods of time and represent sequence information. To explore this, we constructed a network model of realistic neurons showing how in-vivo HVC bursting properties link rebound excitation to network structure and behavior. These results demonstrate an explicit link between neuronal IPs and learned behavior. We propose that sequential behaviors exhibiting temporal regularity require IPs to be included in realistic network-level descriptions.

Parallel executive pallio-motor loops in the pigeon brain

A core component of the avian pallial cognitive network is the multimodal nidopallium caudolaterale (NCL) that is considered to be analogous to the mammalian prefrontal cortex (PFC). The NCL plays a key role in a multitude of executive tasks such as working memory, decision-making during navigation, and extinction learning in complex learning environments. Like the PFC, the NCL is positioned at the transition from ascending sensory to descending motor systems. For the latter, it sends descending premotor projections to the intermediate arcopallium (AI) and the medial striatum (MSt). To gain detailed insight into the organization of these projections, we conducted several retrograde and anterograde tracing experiments. First, we tested whether NCL neurons projecting to AI (NCLarco neurons) and MSt (NCLMSt neurons) are constituted by a single neuronal population with bifurcating neurons, or whether they form two distinct populations. Here, we found two distinct projection patterns to both target areas that were associated with different morphologies. Second, we revealed a weak topographic projection toward the medial and lateral striatum and a strong topographic projection toward AI with clearly distinguishable sensory termination fields. Third, we investigated the relationship between the descending NCL pathways to the arcopallium with those from the hyperpallium apicale, which harbors a second major descending pathway of the avian pallium. We embed our findings within a system of parallel pallio-motor loops that carry information from separate sensory modalities to different subpallial systems. Our results also provide insights into the evolution of the avian motor system from which, possibly, the song system has emerged.

Predicting the Behavior of Sparsely-Sampled Systems Across Neurobiology and Epidemiology

Inference is a term that encompasses many techniques including statistical data assimilation (SDA). Unlike machine learning, which is designed to harness predictive power from extremely large data sets, SDA is designed for sparsely-sampled systems. This is the realm of study of nonlinear dynamical systems in nature. Formulated as an optimization procedure, SDA can be considered a path-integral approach to state and parameter estimation. Within this formulation, we can use the physical principle of least action to identify optimal solutions: solutions that are consistent with both measurements and a dynamical model assumed to give rise to those measurements. I review examples from neurobiology and an epidemiological model tailored to the coronavirus SARS-CoV-2, to demonstrate the versatility of SDA across the sciences, and how these distinct applications possess commonalities that can inform one another.

Rhythmically bursting songbird vocomotor neurons are organized into multiple sequences, suggesting a network/intrinsic properties model encoding song and error, not time

In zebra finch, basal ganglia projecting "HVC _X " neurons emit one or more spike bursts during each song motif (canonical sequence of syllables), which are thought to be driven in part by a process of spike rebound excitation. Zebra finch songs are highly stereotyped and recent results indicate that the intrinsic properties of HVC _X neurons are similar within each bird, vary among birds depending on similarity of the songs, and vary with song errors. We tested the hypothesis that the timing of spike bursts during singing also evince individual-specific distributions. Examining previously published data, we demonstrated that the intervals between bursts of multibursting HVC _X are similar for neurons within each bird, in many cases highly clustered at distinct peaks, with the patterns varying among birds. The fixed delay between bursts and different times when neurons are first recruited in the song yields precisely timed multiple sequences of bursts throughout the song, not the previously envisioned single sequence of bursts treated as events having statistically independent timing. A given moment in time engages multiple sequences and both single bursting and multibursting HVC _X simultaneously. This suggests a model where a population of HVC _X sharing common intrinsic properties driving spike rebound excitation influence the timing of a given HVC _X burst through lateral inhibitory interactions. Perturbations in burst timing, representing error, could propagate in time. Our results extend the concept of central pattern generators to complex vertebrate vocal learning and suggest that network activity (timing of inhibition) and HVC _X intrinsic properties become coordinated during developmental birdsong learning.

Optimal control methods for nonlinear parameter estimation in biophysical neuron models

Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neuron models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators.

Systems neuroscience aims to understand how individual neurons and neural networks process external stimuli into behavioral responses. Underlying this characterization are mathematical models intimately shaped by experimental observations. But neural systems are high-dimensional and contain highly nonlinear interactions, so developing accurate models remains a challenge given current experimental capabilities. In practice, this means that the dynamical equations characterizing neural activity have many unknown parameters, and these parameters must be inferred from data. This inference problem is nontrivial owing to model nonlinearity, system and measurement noise, and the sparsity of observations from electrode recordings. Here, we present a novel method for inferring model parameters of neural systems. Our technique combines ideas from control theory and optimization, and amounts to using data to “control” estimates toward the best fit. Our method compares well in accuracy against other state-of-the-art inference methods, both in phenomenological chaotic systems and biophysical neuron models. Our work shows that many unknown model parameters of interest can be inferred from voltage measurements, despite signaling noise, instrument noise, and low observability.

A feedforward inhibitory premotor circuit for auditory-vocal interactions in zebra finches

Significance During conversations, we frequently alternate between listening and speaking. This involves withholding responses while the other person is vocalizing and rapidly initiating a reply once they stop. Similar exchanges also occur in other animals, such as songbirds, yet little is known about how brain areas responsible for vocal production are influenced by areas dedicated to listening. Here, we combined neural recordings and mathematical modeling of a sensorimotor circuit to show that input-dependent inhibition can both suppress vocal responses and regulate the onset latencies of vocalizations. Our resulting model provides a simple generalizable circuit mechanism by which inhibition precisely times vocal output and integrates auditory input within a premotor nucleus.

Fast-slow analysis as a technique for understanding the neuronal response to current ramps

The standard protocol for studying the spiking properties of single neurons is the application of current steps while monitoring the voltage response. Although this is informative, the jump in applied current is artificial. A more physiological input is where the applied current is ramped up, reflecting chemosensory input. Unsurprisingly, neurons can respond differently to the two protocols, since ion channel activation and inactivation are affected differently. Understanding the effects of current ramps, and changes in their slopes, is facilitated by mathematical models. However, techniques for analyzing current ramps are under-developed. In this article, we demonstrate how current ramps can be analyzed in single neuron models. The primary issue is the presence of gating variables that activate on slow time scales and are therefore far from equilibrium throughout the ramp. The use of an appropriate fast-slow analysis technique allows one to fully understand the neural response to ramps of different slopes. This study is motivated by data from olfactory bulb dopamine neurons, where both fast ramp (tens of milliseconds) and slow ramp (tens of seconds) protocols are used to understand the spiking profiles of the cells. The slow ramps generate experimental bifurcation diagrams with the applied current as a bifurcation parameter, thereby establishing asymptotic spiking activity patterns. The faster ramps elicit purely transient behavior that is of relevance to most physiological inputs, which are short in duration. The two protocols together provide a broader understanding of the neuron's spiking profile and the role that slowly activating ion channels can play.

DARPP-32 distinguishes a subset of adult-born neurons in zebra finch HVC

Adult male zebra finches (Taeniopygia guttata) continually incorporate adult-born neurons into HVC, a telencephalic brain region necessary for the production of learned song. These neurons express activity-dependent immediate early genes (e.g., zenk and c-fos) following song production, suggesting that these neurons are active during song production. Half of these adult-born HVC neurons (HVC NNs) can be backfilled from the robust nucleus of the arcopallium (RA) and are a part of the vocal motor pathway underlying learned song production, but the other half do not backfill from RA, and they remain to be characterized. Here, we used cell birth-dating, retrograde tract tracing, and immunofluorescence to demonstrate that half of all HVC NNs express the phosphoprotein DARPP-32, a protein associated with dopamine receptor expression. We also demonstrate that DARPP-32+ HVC NNs are contacted by tyrosine hydroxylase immunoreactive fibers, suggesting that they receive catecholaminergic input, have transiently larger nuclei than DARPP-32-neg HVC NNs, and do not backfill from RA. Taken together, these findings help characterize a group of HVC NNs that have no apparent projections to RA and so far have eluded positive identification other than HVC NN status.

Neurophysiological coordination of duet singing

Coordination of behavior for cooperative performances often relies on linkages mediated by sensory cues exchanged between participants. How neurophysiological responses to sensory information affect motor programs to coordinate behavior between individuals is not known. We investigated how plain-tailed wrens (Pheugopedius euophrys) use acoustic feedback to coordinate extraordinary duet performances in which females and males rapidly take turns singing. We made simultaneous neurophysiological recordings in a song control area "HVC" in pairs of singing wrens at a field site in Ecuador. HVC is a premotor area that integrates auditory feedback and is necessary for song production. We found that spiking activity of HVC neurons in each sex increased for production of its own syllables. In contrast, hearing sensory feedback produced by the bird's partner decreased HVC activity during duet singing, potentially coordinating HVC premotor activity in each bird through inhibition. When birds sang alone, HVC neurons in females but not males were inhibited by hearing the partner bird. When birds were anesthetized with urethane, which antagonizes GABAergic (γ-aminobutyric acid) transmission, HVC neurons were excited rather than inhibited, suggesting a role for GABA in the coordination of duet singing. These data suggest that HVC integrates information across partners during duets and that rapid turn taking may be mediated, in part, by inhibition.

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

Intrinsic neuronal properties represent song and error in zebra finch vocal learning

Neurons regulate their intrinsic physiological properties, which could influence network properties and contribute to behavioral plasticity. Recording from adult zebra finch brain slices we show that within each bird basal ganglia Area X-projecting (HVCX) neurons share similar spike waveform morphology and timing of spike trains, with modeling indicating similar magnitudes of five principal ion currents. These properties vary among birds in lawful relation to acoustic similarity of the birds' songs, with adult sibling pairs (same songs) sharing similar waveforms and spiking characteristics. The properties are maintained dynamically: HVCX within juveniles learning to sing show variable properties, whereas the uniformity rapidly degrades within hours in adults singing while exposed to abnormal (delayed) auditory feedback. Thus, within individual birds the population of current magnitudes covary over the arc of development, while rapidly responding to changes in feedback (in adults). This identifies network interactions with intrinsic properties that affect information storage and processing of learned vocalizations.

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.

Statistical data assimilation for estimating electrophysiology simultaneously with connectivity within a biological neuronal network

A method of data assimilation (DA) is employed to estimate electrophysiological parameters of neurons simultaneously with their synaptic connectivity in a small model biological network. The DA procedure is cast as an optimization, with a cost function consisting of both a measurement error and a model error term. An iterative reweighting of these terms permits a systematic method to identify the lowest minimum, within a local region of state space, on the surface of a nonconvex cost function. In the model, two sets of parameter values are associated with two particular functional modes of network activity: simultaneous firing of all neurons and a pattern-generating mode wherein the neurons burst in sequence. The DA procedure is able to recover these modes if: (i) the stimulating electrical currents have chaotic waveforms and (ii) the measurements consist of the membrane voltages of all neurons in the circuit. Further, this method is able to prune a model of unnecessarily high dimensionality to a representation that contains the maximum dimensionality required to reproduce the provided measurements. This paper offers a proof-of-concept that DA has the potential to inform laboratory designs for estimating properties in small and isolatable functional circuits.

Exploring the molecular basis of neuronal excitability in a vocal learner

BACKGROUND

Vocal learning, the ability to learn to produce vocalizations through imitation, relies on specialized brain circuitry known in songbirds as the song system. While the connectivity and various physiological properties of this system have been characterized, the molecular genetic basis of neuronal excitability in song nuclei remains understudied. We have focused our efforts on examining voltage-gated ion channels to gain insight into electrophysiological and functional features of vocal nuclei. A previous investigation of potassium channel genes in zebra finches (Taeniopygia guttata) revealed evolutionary modifications unique to songbirds, as well as transcriptional specializations in the song system [Lovell PV, Carleton JB, Mello CV. BMC Genomics 14:470 2013]. Here, we expand this approach to sodium, calcium, and chloride channels along with their modulatory subunits using comparative genomics and gene expression analysis encompassing microarrays and in situ hybridization.

RESULTS

We found 23 sodium, 38 calcium, and 33 chloride channel genes (HGNC-based classification) in the zebra finch genome, several of which were previously unannotated. We determined 15 genes are missing relative to mammals, including several genes (CLCAs, BEST2) linked to olfactory transduction. The majority of sodium and calcium but few chloride channels showed differential expression in the song system, among them SCN8A and CACNA1E in the direct motor pathway, and CACNG4 and RYR2 in the anterior forebrain pathway. In several cases, we noted a seemingly coordinated pattern across multiple nuclei (SCN1B, SCN3B, SCN4B, CACNB4) or sparse expression (SCN1A, CACNG5, CACNA1B).

CONCLUSION

The gene families examined are highly conserved between avian and mammalian lineages. Several cases of differential expression likely support high-frequency and burst firing in specific song nuclei, whereas cases of sparse patterns of expression may contribute to the unique electrophysiological signatures of distinct cell populations. These observations lay the groundwork for manipulations to determine how ion channels contribute to the neuronal excitability properties of vocal learning systems.

Experience-Dependent Intrinsic Plasticity During Auditory Learning

Song learning in zebra finches (Taeniopygia guttata) requires exposure to the song of a tutor, resulting in an auditory memory. This memory is the foundation for later sensorimotor learning, resulting in the production of a copy of the tutor's song. The cortical premotor nucleus HVC (proper name) is necessary for auditory and sensorimotor learning as well as the eventual production of adult song. We recently discovered that the intrinsic physiology of HVC neurons changes across stages of song learning, but are those changes the result of learning or are they experience-independent developmental changes? To test the role of auditory experience in driving intrinsic changes, patch-clamp experiments were performed comparing HVC neurons in juvenile birds with varying amounts of tutor exposure. The intrinsic physiology of HVC neurons changed as a function of tutor exposure. Counterintuitively, tutor deprivation resulted in juvenile HVC neurons showing an adult-like phenotype not present in tutor-exposed juveniles. Biophysical models were developed to predict which ion channels were modulated by experience. The models indicate that tutor exposure transiently suppressed the I _h and T-type Ca²⁺ currents in HVC neurons that target the basal ganglia, whereas tutor exposure increased the resting membrane potential and decreased the spike amplitude in HVC neurons that drive singing. Our findings suggest that intrinsic plasticity may be part of the mechanism for auditory learning in the HVC. More broadly, models of learning and memory should consider intrinsic plasticity as a possible mechanism by which the nervous system encodes the lasting effects of experience.SIGNIFICANCE STATEMENT It is well established that learning involves plasticity of the synapses between neurons. However, the activity of a neural circuit can also be dramatically altered by changes in the intrinsic properties (ion channels) of the component neurons. The present experiments show experience-dependent changes in the intrinsic physiology of neurons in the cortical premotor nucleus HVC (proper name) in juvenile zebra finches (Taeniopygia guttata) during auditory learning of a tutor's song. Tutor deprivation does not "arrest" development of intrinsic properties, but rather results in neurons with a premature adult-like physiological phenotype. It is possible that auditory learning involves a form of nonsynaptic plasticity and that experience-dependent suppression of specific ion channels may work in concert with synaptic plasticity to promote vocal learning.

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 ( I_LT). 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.

A unifying view of synchronization for data assimilation in complex nonlinear networks

Networks of nonlinear systems contain unknown parameters and dynamical degrees of freedom that may not be observable with existing instruments. From observable state variables, we want to estimate the connectivity of a model of such a network and determine the full state of the model at the termination of a temporal observation window during which measurements transfer information to a model of the network. The model state at the termination of a measurement window acts as an initial condition for predicting the future behavior of the network. This allows the validation (or invalidation) of the model as a representation of the dynamical processes producing the observations. Once the model has been tested against new data, it may be utilized as a predictor of responses to innovative stimuli or forcing. We describe a general framework for the tasks involved in the "inverse" problem of determining properties of a model built to represent measured output from physical, biological, or other processes when the measurements are noisy, the model has errors, and the state of the model is unknown when measurements begin. This framework is called statistical data assimilation and is the best one can do in estimating model properties through the use of the conditional probability distributions of the model state variables, conditioned on observations. There is a very broad arena of applications of the methods described. These include numerical weather prediction, properties of nonlinear electrical circuitry, and determining the biophysical properties of functional networks of neurons. Illustrative examples will be given of (1) estimating the connectivity among neurons with known dynamics in a network of unknown connectivity, and (2) estimating the biophysical properties of individual neurons in vitro taken from a functional network underlying vocalization in songbirds.

Assimilation of Biophysical Neuronal Dynamics in Neuromorphic VLSI

Representing the biophysics of neuronal dynamics and behavior offers a principled analysis-by-synthesis approach toward understanding mechanisms of nervous system functions. We report on a set of procedures assimilating and emulating neurobiological data on a neuromorphic very large scale integrated (VLSI) circuit. The analog VLSI chip, NeuroDyn, features 384 digitally programmable parameters specifying for 4 generalized Hodgkin-Huxley neurons coupled through 12 conductance-based chemical synapses. The parameters also describe reversal potentials, maximal conductances, and spline regressed kinetic functions for ion channel gating variables. In one set of experiments, we assimilated membrane potential recorded from one of the neurons on the chip to the model structure upon which NeuroDyn was designed using the known current input sequence. We arrived at the programmed parameters except for model errors due to analog imperfections in the chip fabrication. In a related set of experiments, we replicated songbird individual neuron dynamics on NeuroDyn by estimating and configuring parameters extracted using data assimilation from intracellular neural recordings. Faithful emulation of detailed biophysical neural dynamics will enable the use of NeuroDyn as a tool to probe electrical and molecular properties of functional neural circuits. Neuroscience applications include studying the relationship between molecular properties of neurons and the emergence of different spike patterns or different brain behaviors. Clinical applications include studying and predicting effects of neuromodulators or neurodegenerative diseases on ion channel kinetics.

Neuronal Intrinsic Physiology Changes During Development of a Learned Behavior

Juvenile male zebra finches learn their songs over distinct auditory and sensorimotor stages, the former requiring exposure to an adult tutor song pattern. The cortical premotor nucleus HVC (acronym is name) plays a necessary role in both learning stages, as well as the production of adult song. Consistent with neural network models where synaptic plasticity mediates developmental forms of learning, exposure to tutor song drives changes in the turnover, density, and morphology of HVC synapses during vocal development. A network's output, however, is also influenced by the intrinsic properties (e.g., ion channels) of the component neurons, which could change over development. Here, we use patch clamp recordings to show cell-type-specific changes in the intrinsic physiology of HVC projection neurons as a function of vocal development. Developmental changes in HVC neurons that project to the basal ganglia include an increased voltage sag response to hyperpolarizing currents and an increased rebound depolarization following hyperpolarization. Developmental changes in HVC neurons that project to vocal-motor cortex include a decreased resting membrane potential and an increased spike amplitude. HVC interneurons, however, show a relatively stable range of intrinsic features across vocal development. We used mathematical models to deduce possible changes in ionic currents that underlie the physiological changes and to show that the magnitude of the observed changes could alter HVC circuit function. The results demonstrate developmental plasticity in the intrinsic physiology of HVC projection neurons and suggest that intrinsic plasticity may have a role in the process of song learning.

Intrinsic physiology of inhibitory neurons changes over auditory development

During auditory development, changes in membrane properties promote the ability of excitatory neurons in the brain stem to code aspects of sound, including the level and timing of a stimulus. Some of these changes coincide with hearing onset, suggesting that sound-driven neural activity produces developmental plasticity of ion channel expression. While it is known that the coding properties of excitatory neurons are modulated by inhibition in the mature system, it is unknown whether there are also developmental changes in the membrane properties of brain stem inhibitory neurons. We investigated the primary source of inhibition in the avian auditory brain stem, the superior olivary nucleus (SON). The present studies test the hypothesis that, as in excitatory neurons, the membrane properties of these inhibitory neurons change after hearing onset. We examined SON neurons at different stages of auditory development: embryonic days 14-16 (E14-E16), a time at which cochlear ganglion neurons are just beginning to respond to sound; later embryonic stages (E18-E19); and after hatching (P0-P2). We used in vitro whole cell patch electrophysiology to explore physiological changes in SON. Age-related changes were observed at the level of a single spike and in multispiking behavior. In particular, tonic behavior, measured as a neuron's ability to sustain tonic firing over a range of current steps, became more common later in development. Voltage-clamp recordings and biophysical models were employed to examine how age-related increases in ion currents enhance excitability in SON. Our findings suggest that concurrent increases in sodium and potassium currents underlie the emergence of tonic behavior. NEW & NOTEWORTHY This article is the first to examine heterogeneity of neuronal physiology in the inhibitory nucleus of the avian auditory system and demonstrate that tonic firing here emerges over development. By pairing computer simulations with physiological data, we show that increases in both sodium and potassium channels over development are necessary for the emergence of tonic firing.

Temperature manipulation of neuronal dynamics in a forebrain motor control nucleus

Different neuronal types within brain motor areas contribute to the generation of complex motor behaviors. A widely studied songbird forebrain nucleus (HVC) has been recognized as fundamental in shaping the precise timing characteristics of birdsong. This is based, among other evidence, on the stretching and the “breaking” of song structure when HVC is cooled. However, little is known about the temperature effects that take place in its neurons. To address this, we investigated the dynamics of HVC both experimentally and computationally. We developed a technique where simultaneous electrophysiological recordings were performed during temperature manipulation of HVC. We recorded spontaneous activity and found three effects: widening of the spike shape, decrease of the firing rate and change in the interspike interval distribution. All these effects could be explained with a detailed conductance based model of all the neurons present in HVC. Temperature dependence of the ionic channel time constants explained the first effect, while the second was based in the changes of the maximal conductance using single synaptic excitatory inputs. The last phenomenon, only emerged after introducing a more realistic synaptic input to the inhibitory interneurons. Two timescales were present in the interspike distributions. The behavior of one timescale was reproduced with different input balances received form the excitatory neurons, whereas the other, which disappears with cooling, could not be found assuming poissonian synaptic inputs. Furthermore, the computational model shows that the bursting of the excitatory neurons arises naturally at normal brain temperature and that they have an intrinsic delay at low temperatures. The same effect occurs at single synapses, which may explain song stretching. These findings shed light on the temperature dependence of neuronal dynamics and present a comprehensive framework to study neuronal connectivity. This study, which is based on intrinsic neuronal characteristics, may help to understand emergent behavioral changes.

The study of the neuronal mechanisms that give rise to the complex behavior of singing in birds has been hotly debated lately. Many models have been tested and novel tools have been developed to try to understand the role of a key brain nucleus in the song pathway: HVC. It is believed that it is highly responsible for generating the precise timing of songs, and this has been tested by manipulating it with temperature. Results showed that cooling can stretch, but that it can also restructure or “break” the song syllables. However, single neuronal mechanisms are not yet described. To better understand this, we cooled HVC in canaries and measured spontaneous activity electrophysiologically. We found three effects: spike shape widening, spike rate reduction and changes in inter-spike-interval (ISI) distributions. To explain them, we built a computational model with a detailed description of ion channel conductances and temperature dependency. We could explain the first effect with a single neuron model. The second, could be explained adding single synapses. Finally, we showed similar ISI modifications of one of the timescales present by means of multiple stochastic inputs. In addition, we found that excitatory neurons show natural bursting behavior at normal brain temperatures and that synaptic delays are the main candidates to explain song stretching at low temperatures.

A distributed neural network model for the distinct roles of medial and lateral HVC in zebra finch song production

Male zebra finches produce a song consisting of a canonical sequence of syllables, learned from a tutor and repeated throughout its adult life. Much of the neural circuitry responsible for this behavior is located in the cortical premotor region HVC (acronym is name). In a recent study from our laboratory, we found that partial bilateral ablation of the medial portion of HVC has effects on the song that are qualitatively different from those of bilateral ablation of the lateral portion. In this report we describe a neural network organization that can explain these data, and in so doing suggests key roles for other brain nuclei in the production of song. We also suggest that syllables and the gaps between them are each coded separately by neural chains within HVC, and that the timing mechanisms for syllables and gaps are distinct. The design principles underlying this model assign distinct roles for medial and lateral HVC circuitry that explain the data on medial and lateral ablations. In addition, despite the fact that the neural coding of song sequence is distributed among several brain nuclei in our model, it accounts for data showing that cooling of HVC stretches syllables uniformly and to a greater extent than gaps. Finally, the model made unanticipated predictions about details of the effects of medial and lateral HVC ablations that were then confirmed by reanalysis of these previously acquired behavioral data.NEW & NOTEWORTHY Zebra finch song consists of a string of syllables repeated in a nearly invariant sequence. We propose a neural network organization that can explain recent data indicating that the medial and lateral portions of the premotor cortical nucleus HVC have different roles in zebra finch song production. Our model explains these data, as well as data on the effects on song of cooling HVC, and makes predictions that we test in the singing bird.

Nonlinear statistical data assimilation for HVC[Formula: see text] neurons in the avian song system

With the goal of building a model of the HVC nucleus in the avian song system, we discuss in detail a model of HVC[Formula: see text] projection neurons comprised of a somatic compartment with fast Na[Formula: see text] and K[Formula: see text] currents and a dendritic compartment with slower Ca[Formula: see text] dynamics. We show this model qualitatively exhibits many observed electrophysiological behaviors. We then show in numerical procedures how one can design and analyze feasible laboratory experiments that allow the estimation of all of the many parameters and unmeasured dynamical variables, given observations of the somatic voltage [Formula: see text] alone. A key to this procedure is to initially estimate the slow dynamics associated with Ca, blocking the fast Na and K variations, and then with the Ca parameters fixed estimate the fast Na and K dynamics. This separation of time scales provides a numerically robust method for completing the full neuron model, and the efficacy of the method is tested by prediction when observations are complete. The simulation provides a framework for the slice preparation experiments and illustrates the use of data assimilation methods for the design of those experiments.

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.

Model of the songbird nucleus HVC as a network of central pattern generators

We propose a functional architecture of the adult songbird nucleus HVC in which the core element is a "functional syllable unit" (FSU). In this model, HVC is organized into FSUs, each of which provides the basis for the production of one syllable in vocalization. Within each FSU, the inhibitory neuron population takes one of two operational states: 1) simultaneous firing wherein all inhibitory neurons fire simultaneously, and 2) competitive firing of the inhibitory neurons. Switching between these basic modes of activity is accomplished via changes in the synaptic strengths among the inhibitory neurons. The inhibitory neurons connect to excitatory projection neurons such that during state 1 the activity of projection neurons is suppressed, while during state 2 patterns of sequential firing of projection neurons can occur. The latter state is stabilized by feedback from the projection to the inhibitory neurons. Song composition for specific species is distinguished by the manner in which different FSUs are functionally connected to each other. Ours is a computational model built with biophysically based neurons. We illustrate that many observations of HVC activity are explained by the dynamics of the proposed population of FSUs, and we identify aspects of the model that are currently testable experimentally. In addition, and standing apart from the core features of an FSU, we propose that the transition between modes may be governed by the biophysical mechanism of neuromodulation.

Mesoscopic patterns of neural activity support songbird cortical sequences

Time-locked sequences of neural activity can be found throughout the vertebrate forebrain in various species and behavioral contexts. From "time cells" in the hippocampus of rodents to cortical activity controlling movement, temporal sequence generation is integral to many forms of learned behavior. However, the mechanisms underlying sequence generation are not well known. Here, we describe a spatial and temporal organization of the songbird premotor cortical microcircuit that supports sparse sequences of neural activity. Multi-channel electrophysiology and calcium imaging reveal that neural activity in premotor cortex is correlated with a length scale of 100 µm. Within this length scale, basal-ganglia-projecting excitatory neurons, on average, fire at a specific phase of a local 30 Hz network rhythm. These results show that premotor cortical activity is inhomogeneous in time and space, and that a mesoscopic dynamical pattern underlies the generation of the neural sequences controlling song.

A circular model for song motor control in Serinus canaria

Song production in songbirds is controlled by a network of nuclei distributed across several brain regions, which drives respiratory and vocal motor systems to generate sound. We built a model for birdsong production, whose variables are the average activities of different neural populations within these nuclei of the song system. We focus on the predictions of respiratory patterns of song, because these can be easily measured and therefore provide a validation for the model. We test the hypothesis that it is possible to construct a model in which (1) the activity of an expiratory related (ER) neural population fits the observed pressure patterns used by canaries during singing, and (2) a higher forebrain neural population, HVC, is sparsely active, simultaneously with significant motor instances of the pressure patterns. We show that in order to achieve these two requirements, the ER neural population needs to receive two inputs: a direct one, and its copy after being processed by other areas of the song system. The model is capable of reproducing the measured respiratory patterns and makes specific predictions on the timing of HVC activity during their production. These results suggest that vocal production is controlled by a circular network rather than by a simple top-down architecture.

Interplay of inhibition and excitation shapes a premotor neural sequence

In the zebra finch, singing behavior is driven by a sequence of bursts within premotor neurons located in the forebrain nucleus HVC (proper name). In addition to these excitatory projection neurons, HVC also contains inhibitory interneurons with a role in premotor patterning that is unclear. Here, we used a range of electrophysiological and behavioral observations to test previously described models suggesting discrete functional roles for inhibitory interneurons in song production. We show that single HVC premotor neuron bursts are sufficient to drive structured activity within the interneuron network because of pervasive and facilitating synaptic connections. We characterize interneuron activity during singing and describe reliable pauses in the firing of those neurons. We then demonstrate that these gaps in inhibition are likely to be necessary for driving normal bursting behavior in HVC premotor neurons and suggest that structured inhibition and excitation may be a general mechanism enabling sequence generation in other circuits.

Two neural streams, one voice: pathways for theme and variation in the songbird brain

Birdsong offers a unique model system to understand how a developing brain - once given a set of purely acoustic targets - teaches itself the vocal-tract gestures necessary to imitate those sounds. Like human infants, to juvenile male zebra finches (Taeniopygia guttata) falls the burden of initiating the vocal-motor learning of adult sounds. In both species, adult caregivers provide only a set of sounds to be imitated, with little or no information about the vocal-tract gestures used to produce the sounds. Here, we focus on the central control of birdsong and review the recent discovery that zebra finch song is under dual premotor control. Distinct forebrain pathways for structured (theme) and unstructured (variation) singing not only raise new questions about mechanisms of sensory-motor integration, but also provide a fascinating new research opportunity. A cortical locus for a motor memory of the learned song is now firmly established, meaning that anatomical, physiological, and computational approaches are poised to reveal the neural mechanisms used by the brain to compose the songs of birds.

Estimating the biophysical properties of neurons with intracellular calcium dynamics

We investigate the dynamics of a conductance-based neuron model coupled to a model of intracellular calcium uptake and release by the endoplasmic reticulum. The intracellular calcium dynamics occur on a time scale that is orders of magnitude slower than voltage spiking behavior. Coupling these mechanisms sets the stage for the appearance of chaotic dynamics, which we observe within certain ranges of model parameter values. We then explore the question of whether one can, using observed voltage data alone, estimate the states and parameters of the voltage plus calcium (V+Ca) dynamics model. We find the answer is negative. Indeed, we show that voltage plus another observed quantity must be known to allow the estimation to be accurate. We show that observing both the voltage time course V(t) and the intracellular Ca time course will permit accurate estimation, and from the estimated model state, accurate prediction after observations are completed. This sets the stage for how one will be able to use a more detailed model of V+Ca dynamics in neuron activity in the analysis of experimental data on individual neurons as well as functional networks in which the nodes (neurons) have these biophysical properties.

Developmental but not adult cannabinoid treatments persistently alter axonal and dendritic morphology within brain regions important for zebra finch vocal learning

Prior work shows developmental cannabinoid exposure alters zebra finch vocal development in a manner associated with altered CNS physiology, including changes in patterns of CB1 receptor immunoreactivity, endocannabinoid concentrations and dendritic spine densities. These results raise questions about the selectivity of developmental cannabinoid effects: are they a consequence of a generalized developmental disruption, or are effects produced through more selective and distinct interactions with biochemical pathways that control receptor, endogenous ligand and dendritic spine dynamics? To begin to address this question we have examined effects of developmental cannabinoid exposure on the pattern and density of expression of proteins critical to dendritic (MAP2) and axonal (Nf-200) structure to determine the extent to which dendritic vs. axonal neuronal morphology may be altered. Results demonstrate developmental, but not adult cannabinoid treatments produce generalized changes in expression of both dendritic and axonal cytoskeletal proteins within brain regions and cells known to express CB1 cannabinoid receptors. Results clearly demonstrate that cannabinoid exposure during a period of sensorimotor development, but not adulthood, produce profound effects upon both dendritic and axonal morphology that persist through at least early adulthood. These findings suggest an ability of exogenous cannabinoids to alter general processes responsible for normal brain development. Results also further implicate the importance of endocannabinoid signaling to peri-pubertal periods of adolescence, and underscore potential consequences of cannabinoid abuse during periods of late-postnatal CNS development.

Developmental pattern of diacylglycerol lipase-α (DAGLα) immunoreactivity in brain regions important for song learning and control in the zebra finch (Taeniopygia guttata)

Zebra finch song is a learned behavior dependent upon successful progress through a sensitive period of late-postnatal development. This learning is associated with maturation of distinct brain nuclei and the fiber tract interconnections between them. We have previously found remarkably distinct and dense CB1 cannabinoid receptor expression within many of these song control brain regions, implying a normal role for endocannabinoid signaling in vocal learning. Activation of CB1 receptors via daily treatments with exogenous agonist during sensorimotor stages of song learning (but not in adulthood) results in persistent alteration of song patterns. Now we are working to understand physiological changes responsible for this cannabinoid-altered vocal learning. We have found that song-altering developmental treatments are associated with changes in expression of endocannabinoid signaling elements, including CB1 receptors and the principal CNS endogenous agonist, 2-AG. Within CNS, 2-AG is produced largely through activity of the α isoform of the enzyme diacylglycerol lipase (DAGLα). To better appreciate the role of 2-AG production in normal vocal development we have determined the spatial distribution of DAGLα expression within zebra finch CNS during vocal development. Early during vocal development at 25 days, DAGLα staining is typically light and of fibroid processes. Staining peaks late in the sensorimotor stage of song learning at 75 days and is characterized by fiber, neuropil and some staining of both small and large cell somata. Results provide insight to the normal role for endocannabinoid signaling in the maturation of brain regions responsible for song learning and vocal-motor output, and suggest mechanisms by which exogenous cannabinoid exposure alters acquisition of this form of vocal communication.