Using temperature to analyse temporal dynamics in the songbird motor pathway

Regulation of learned vocal behavior by an auditory motor cortical nucleus in juvenile zebra finches

In the process of song learning, songbirds such as the zebra finch shape their initial soft and poorly formed vocalizations (subsong) first into variable plastic songs with a discernable recurring motif and then into highly stereotyped adult songs. A premotor brain area critically involved in plastic and adult song production is the cortical nucleus HVC. One of HVC's primary afferents, the nucleus interface of the nidopallium (NIf), provides a significant source of auditory input to HVC. However, the premotor involvement of NIf has not been extensively studied yet. Here we report that brief and reversible pharmacological inactivation of NIf in juvenile birds leads to transient degradation of plastic song toward subsong, as revealed by spectral and temporal song features. No such song degradation is seen following NIf inactivation in adults. However, in both juveniles and adults NIf inactivation leads to a transient decrease in song stereotypy. Our findings reveal a contribution of NIf to song production in juveniles that agrees with its known role in adults in mediating thalamic drive to downstream vocal motor areas during sleep.

Learning to breathe and sing: development of respiratory-vocal coordination in young songbirds

How do animals with learned vocalizations coordinate vocal production with respiration? Songbirds such as the zebra finch learn their songs, beginning with highly variable babbling vocalizations known as subsong. After several weeks of practice, zebra finches are able to produce a precisely timed pattern of syllables and silences, precisely coordinated with expiratory and inspiratory pulses (Franz M, Goller F. J Neurobiol 51: 129-141, 2002). While respiration in adult song is well described, relatively little is known about respiratory patterns in subsong or about the processes by which respiratory and vocal patterns become coordinated. To address these questions, we recorded thoracic air sac pressure in juvenile zebra finches prior to the appearance of any consistent temporal or acoustic structure in their songs. We found that subsong contains brief inspiratory pulses (50 ms) alternating with longer pulses of sustained expiratory pressure (50-500 ms). In striking contrast to adult song, expiratory pulses often contained multiple (0-8) variably timed syllables separated by expiratory gaps and were only partially vocalized. During development, expiratory pulses became shorter and more stereotyped in duration with shorter and fewer nonvocalized parts. These developmental changes eventually resulted in the production of a single syllable per expiratory pulse and a single inspiratory pulse filling each gap, forming a coordinated sequence similar to that of adult song. To examine the role of forebrain song-control nuclei in the development of respiratory patterns, we performed pressure recordings before and after lesions of nucleus HVC (proper name) and found that this manipulation reverses the developmental trends in measures of the respiratory pattern.

Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus

Hippocampal neurons can display reliable and long-lasting sequences of transient firing patterns, even in the absence of changing external stimuli. We suggest that time-keeping is an important function of these sequences, and propose a network mechanism for their generation. We show that sequences of neuronal assemblies recorded from rat hippocampal CA1 pyramidal cells can reliably predict elapsed time (15-20 s) during wheel running with a precision of 0.5 s. In addition, we demonstrate the generation of multiple reliable, long-lasting sequences in a recurrent network model. These sequences are generated in the presence of noisy, unstructured inputs to the network, mimicking stationary sensory input. Identical initial conditions generate similar sequences, whereas different initial conditions give rise to distinct sequences. The key ingredients responsible for sequence generation in the model are threshold-adaptation and a Mexican-hat-like pattern of connectivity among pyramidal cells. This pattern may arise from recurrent systems such as the hippocampal CA3 region or the entorhinal cortex. We hypothesize that mechanisms that evolved for spatial navigation also support tracking of elapsed time in behaviorally relevant contexts.

Vocalization frequency and duration are coded in separate hindbrain nuclei

Temporal patterning is an essential feature of neural networks producing precisely timed behaviours such as vocalizations that are widely used in vertebrate social communication. Here we show that intrinsic and network properties of separate hindbrain neuronal populations encode the natural call attributes of frequency and duration in vocal fish. Intracellular structure/function analyses indicate that call duration is encoded by a sustained membrane depolarization in vocal prepacemaker neurons that innervate downstream pacemaker neurons. Pacemaker neurons, in turn, encode call frequency by rhythmic, ultrafast oscillations in their membrane potential. Pharmacological manipulations show prepacemaker activity to be independent of pacemaker function, thus accounting for natural variation in duration which is the predominant feature distinguishing call types. Prepacemaker neurons also innervate key hindbrain auditory nuclei thereby effectively serving as a call-duration corollary discharge. We propose that premotor compartmentalization of neurons coding distinct acoustic attributes is a fundamental trait of hindbrain vocal pattern generators among vertebrates.

Changes in the neural control of a complex motor sequence during learning

The acquisition of complex motor sequences often proceeds through trial-and-error learning, requiring the deliberate exploration of motor actions and the concomitant evaluation of the resulting performance. Songbirds learn their song in this manner, producing highly variable vocalizations as juveniles. As the song improves, vocal variability is gradually reduced until it is all but eliminated in adult birds. In the present study we examine how the motor program underlying such a complex motor behavior evolves during learning by recording from the robust nucleus of the arcopallium (RA), a motor cortex analog brain region. In young birds, neurons in RA exhibited highly variable firing patterns that throughout development became more precise, sparse, and bursty. We further explored how the developing motor program in RA is shaped by its two main inputs: LMAN, the output nucleus of a basal ganglia-forebrain circuit, and HVC, a premotor nucleus. Pharmacological inactivation of LMAN during singing made the song-aligned firing patterns of RA neurons adultlike in their stereotypy without dramatically affecting the spike statistics or the overall firing patterns. Removing the input from HVC, on the other hand, resulted in a complete loss of stereotypy of both the song and the underlying motor program. Thus our results show that a basal ganglia-forebrain circuit drives motor exploration required for trial-and-error learning by adding variability to the developing motor program. As learning proceeds and the motor circuits mature, the relative contribution of LMAN is reduced, allowing the premotor input from HVC to drive an increasingly stereotyped song.

Cooperation of deterministic dynamics and random noise in production of complex syntactical avian song sequences: a neural network model

How the brain learns and generates temporal sequences is a fundamental issue in neuroscience. The production of birdsongs, a process which involves complex learned sequences, provides researchers with an excellent biological model for this topic. The Bengalese finch in particular learns a highly complex song with syntactical structure. The nucleus HVC (HVC), a premotor nucleus within the avian song system, plays a key role in generating the temporal structures of their songs. From lesion studies, the nucleus interfacialis (NIf) projecting to the HVC is considered one of the essential regions that contribute to the complexity of their songs. However, the types of interaction between the HVC and the NIf that can produce complex syntactical songs remain unclear. In order to investigate the function of interactions between the HVC and NIf, we have proposed a neural network model based on previous biological evidence. The HVC is modeled by a recurrent neural network (RNN) that learns to generate temporal patterns of songs. The NIf is modeled as a mechanism that provides auditory feedback to the HVC and generates random noise that feeds into the HVC. The model showed that complex syntactical songs can be replicated by simple interactions between deterministic dynamics of the RNN and random noise. In the current study, the plausibility of the model is tested by the comparison between the changes in the songs of actual birds induced by pharmacological inhibition of the NIf and the changes in the songs produced by the model resulting from modification of parameters representing NIf functions. The efficacy of the model demonstrates that the changes of songs induced by pharmacological inhibition of the NIf can be interpreted as a trade-off between the effects of noise and the effects of feedback on the dynamics of the RNN of the HVC. These facts suggest that the current model provides a convincing hypothesis for the functional role of NIf-HVC interaction.

A compact statistical model of the song syntax in Bengalese finch

Songs of many songbird species consist of variable sequences of a finite number of syllables. A common approach for characterizing the syntax of these complex syllable sequences is to use transition probabilities between the syllables. This is equivalent to the Markov model, in which each syllable is associated with one state, and the transition probabilities between the states do not depend on the state transition history. Here we analyze the song syntax in Bengalese finch. We show that the Markov model fails to capture the statistical properties of the syllable sequences. Instead, a state transition model that accurately describes the statistics of the syllable sequences includes adaptation of the self-transition probabilities when states are revisited consecutively, and allows associations of more than one state to a given syllable. Such a model does not increase the model complexity significantly. Mathematically, the model is a partially observable Markov model with adaptation (POMMA). The success of the POMMA supports the branching chain network model of how syntax is controlled within the premotor song nucleus HVC, but also suggests that adaptation and many-to-one mapping from the syllable-encoding chain networks in HVC to syllables should be included in the network model.

Analyzing the dynamics of brain circuits with temperature: design and implementation of a miniature thermoelectric device

Traditional lesion or inactivation methods are useful for determining if a given brain area is involved in the generation of a behavior, but not for determining if circuit dynamics in that area control the timing of the behavior. In contrast, localized mild cooling or heating of a brain area alters the speed of neuronal and circuit dynamics and can reveal the role of that area in the control of timing. It has been shown that miniaturized solid-state heat pumps based on the Peltier effect can be useful for analyzing brain dynamics in small freely behaving animals (Long and Fee, 2008). Here we present a theoretical analysis of these devices and a procedure for optimizing their design. We describe the construction and implementation of one device for cooling surface brain areas, such as cortex, and another device for cooling deep brain regions. We also present measurements of the magnitude and localization of the brain temperature changes produced by these two devices.

Neural syntax: cell assemblies, synapsembles, and readers

A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as "cell assemblies," underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by "reader-actuator" mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights ("synapsembles"). The existing support for this tripartite framework is reviewed and strategies for experimental testing of its predictions are discussed.

Support for a synaptic chain model of neuronal sequence generation

In songbirds, the remarkable temporal precision of song is generated by a sparse sequence of bursts in the premotor nucleus HVC (proper name). To distinguish between two possible classes of models of neural sequence generation, we carried out intracellular recordings of HVC neurons in singing birds. We found that the subthreshold membrane potential is characterized by a large rapid depolarization 5–10 ms prior to burst onset, consistent with a synaptically-connected chain of neurons in HVC. We found no evidence for the slow membrane potential modulation predicted by models in which burst timing is controlled by subthreshold dynamics. Furthermore, bursts ride on an underlying depolarization of ~10ms duration, likely the result of a regenerative calcium spike within HVC neurons that could facilitate the propagation of activity through a chain network with high temporal precision. Our results shed light on the fundamental mechanisms by which neural circuits can generate complex sequential behaviours.

Sleep, off-line processing, and vocal learning

The study of song learning and the neural song system has provided an important comparative model system for the study of speech and language acquisition. We describe some recent advances in the bird song system, focusing on the role of off-line processing including sleep in processing sensory information and in guiding developmental song learning. These observations motivate a new model of the organization and role of the sensory memories in vocal learning.

The origins of vocal learning: New sounds, new circuits, new cells

We do not know how vocal learning came to be, but it is such a salient trait in human evolution that many have tried to imagine it. In primates this is difficult because we are the only species known to possess this skill. Songbirds provide a richer and independent set of data. I use comparative data and ask broad questions: How does vocal learning emerge during ontogeny? In what contexts? What are its benefits? How did it evolve from unlearned vocal signals? How was brain anatomy altered to enable vocal learning? What is the relation of vocal learning to adult neurogenesis? No one has described yet a circuit or set of circuits that can master vocal learning, but this knowledge may soon be within reach. Moreover, as we uncover how birds encode their learned song, we may also come closer to understanding how we encode our thoughts.

From central pattern generator to sensory template in the evolution of birdsong

Central nervous networks, be they a part of the human brain or a group of neurons in a snail, may be designed to produce distinct patterns of movement. Central pattern generators can account for the development and production of normal vocal signals without auditory feedback in non-songbirds. Songbirds need auditory feedback to develop and maintain the normal song of their species. The prerequisite for the use of auditory feedback for the control of song is a set of acoustic criteria or a template to which voice must match. The template method perhaps evolved to free birds from fixed central pattern generators, resulting in the evolution of diverse and complex songs among oscine songbirds. The evolution of human speech may have followed a similar course.

Neurobiology of vocal communication: mechanisms for sensorimotor integration and vocal patterning

This review will focus on recent developments in the sensorimotor integration of vocal communication. Two broad themes are emphasized: the evolution of vocal production and perception, and the role of social context. Advances include: a proposal for the emergence of vocal patterning during vertebrate evolution, the role of sensory mechanisms such as categorical perception in decoding communication signals, contributions of sensorimotor integration phenomena including mirror neurons and vocal learning, and mechanisms of hormone-dependent plasticity in both auditory and vocal systems. Transcriptional networks activated in humans but not in chimps by the FoxP2 gene suggest molecular mechanisms underlying vocal gestures and the emergence of human language.

New brain pathways found in the vocal control system of a songbird

Songbirds use a complex network of discrete brain areas and connecting fiber tracts to sing their song, but our knowledge of this circuitry may be incomplete. The forebrain area, "caudal mesopallium" (CM), has received much attention recently for its song-related activities. HVC, a prominent song system nucleus, projects to a restricted area of the CM known as the avalanche nucleus (Av). However, the other connections of Av remain unknown. Here we used tract-tracing methods to examine the connections of Av to other song system nuclei. Injections of biotinylated dextran amine (BDA) into Av labeled both afferent terminals and neurons in HVC and the interfacial nucleus of the nidopallium (NIf), suggesting that there is complex feedforward and feedback communication between these nuclei (HVC<-->Av<-->NIf). Labeled neurons were also found in the uvaeform nucleus (Uva), which was substantiated by BDA injections into Uva that labeled terminals in Av. Double fluorescent tracing experiments confirm that both HVC and Uva project to Av. The present study adds complex new connections that expand the traditional song system circuitry into the caudal mesopallium. These new pathways are likely to have broad implications for deciphering how this intricate system works.

Activity in a cortical-basal ganglia circuit for song is required for social context-dependent vocal variability

Variability in adult motor output is important for enabling animals to respond to changing external conditions. Songbirds are useful for studying variability because they alter the amount of variation in their song depending on social context. When an adult zebra finch male sings to a female ("directed"), his song is highly stereotyped, but when he sings alone ("undirected"), his song varies across renditions. Lesions of the lateral magnocellular nucleus of the anterior nidopallium (LMAN), the output nucleus of a cortical-basal ganglia circuit for song, reduce song variability to that of the stereotyped "performance" state. However, such lesions not only eliminate LMAN's synaptic input to its targets, but can also cause structural or physiological changes in connected brain regions, and thus cannot assess whether the acute activity of LMAN is important for social modulation of adult song variability. To evaluate the effects of ongoing LMAN activity, we reversibly silenced LMAN in singing zebra finches by bilateral reverse microdialysis of the GABA(A) receptor agonist muscimol. We found that LMAN inactivation acutely reduced undirected song variability, both across and even within syllable renditions, to the level of directed song variability in all birds examined. Song variability returned to pre-muscimol inactivation levels after drug washout. However, unlike LMAN lesions, LMAN inactivation did not eliminate social context effects on song tempo in adult birds. These results indicate that the activity of LMAN neurons acutely and actively generates social context-dependent increases in adult song variability but that social regulation of tempo is more complex.

Precise temperature compensation of phase in a rhythmic motor pattern

Most animal species are cold-blooded, and their neuronal circuits must maintain function despite environmental temperature fluctuations. The central pattern generating circuits that produce rhythmic motor patterns depend on the orderly activation of circuit neurons. We describe the effects of temperature on the pyloric rhythm of the stomatogastric ganglion of the crab, Cancer borealis. The pyloric rhythm is a triphasic motor pattern in which the Pyloric Dilator (PD), Lateral Pyloric (LP), and Pyloric (PY) neurons fire in a repeating sequence. While the frequency of the pyloric rhythm increased about 4-fold (Q(10) approximately 2.3) as the temperature was shifted from 7 degrees C to 23 degrees C, the phase relationships of the PD, LP, and PY neurons showed almost perfect temperature compensation. The Q(10)'s of the input conductance, synaptic currents, transient outward current (I(A)), and the hyperpolarization-activated inward current (I(h)), all of which help determine the phase of LP neuron activity, ranged from 1.8 to 4. We studied the effects of temperature in >1,000 computational models (with different sets of maximal conductances) of a bursting neuron and the LP neuron. Many bursting models failed to monotonically increase in frequency as temperature increased. Temperature compensation of LP neuron phase was facilitated when model neurons' currents had Q(10)'s close to 2. Together, these data indicate that although diverse sets of maximal conductances may be found in identified neurons across animals, there may be strong evolutionary pressure to restrict the Q(10)'s of the processes that contribute to temperature compensation of neuronal circuits.

Neuronal stability and drift across periods of sleep: premotor activity patterns in a vocal control nucleus of adult zebra finches

How stable are neural activity patterns compared across periods of sleep? We evaluated this question in adult zebra finches, whose premotor neurons in the nucleus robustus arcopallialis (RA) exhibit sequences of bursts during daytime singing that are characterized by precise timing relative to song syllables. Each burst has a highly regulated pattern of spikes. We assessed these spike patterns in singing that occurred before and after periods of sleep. For about half of the neurons, one or more premotor bursts had changed after sleep, an average of 20% of all bursts across all RA neurons. After sleep, modified bursts were characterized by a discrete, albeit modest, loss of spikes with compensatory increases in spike intervals, but not changes in timing relative to the syllable. Changes in burst structure followed both interrupted bouts of sleep (1.5-3 h) and full nights of sleep, implicating sleep and not circadian cycle as mediating these effects. Changes in burst structure were also observed during the day, but far less frequently. In cases where multiple bursts in the sequence changed in a single cell, the sequence position of those bursts tended to cluster together. Bursts that did not show discrete changes in structure also showed changes in spike counts, but not biased toward losses. We hypothesize that changes in burst patterns during sleep represent active sculpting of the RA network, supporting auditory feedback-mediated song maintenance.

Parallel pathways for vocal learning in basal ganglia of songbirds

The cortical nucleus LMAN provides the output of a basal ganglia pathway that is necessary for vocal learning in juvenile songbirds. The shell subregion of LMAN gives rise to recurrent loops that may subserve specific learning-related functions. We report here that lesions within the LMAN_shell pathway cause no immediate disruption of vocal behavior, but prevent the development of stable vocal sequences as well as the ability to imitate vocal sounds.

Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner

Complex neural dynamics produced by the recurrent architecture of neocortical circuits is critical to the cortex's computational power. However, the synaptic learning rules underlying the creation of stable propagation and reproducible neural trajectories within recurrent networks are not understood. Here, we examined synaptic learning rules with the goal of creating recurrent networks in which evoked activity would: (1) propagate throughout the entire network in response to a brief stimulus while avoiding runaway excitation; (2) exhibit spatially and temporally sparse dynamics; and (3) incorporate multiple neural trajectories, i.e., different input patterns should elicit distinct trajectories. We established that an unsupervised learning rule, termed presynaptic-dependent scaling (PSD), can achieve the proposed network dynamics. To quantify the structure of the trained networks, we developed a recurrence index, which revealed that presynaptic-dependent scaling generated a functionally feedforward network when training with a single stimulus. However, training the network with multiple input patterns established that: (1) multiple non-overlapping stable trajectories can be embedded in the network; and (2) the structure of the network became progressively more complex (recurrent) as the number of training patterns increased. In addition, we determined that PSD and spike-timing-dependent plasticity operating in parallel improved the ability of the network to incorporate multiple and less variable trajectories, but also shortened the duration of the neural trajectory. Together, these results establish one of the first learning rules that can embed multiple trajectories, each of which recruits all neurons, within recurrent neural networks in a self-organizing manner.

Neurobiology of song learning

Birdsong is a culturally transmitted behavior that depends on a juvenile songbird's ability to imitate the song of an adult tutor. Neurobiological studies of birdsong can reveal how a complex form of imitative learning, which bears strong parallels to human speech learning, can be understood at the level of underlying circuit, cellular, and synaptic mechanisms. This review focuses on recent studies that illuminate the neurobiological mechanisms for singing and song learning.

Generating variable birdsong syllable sequences with branching chain networks in avian premotor nucleus HVC

Songs of songbird species such as Bengalese finch consist of sequences of syllables. While syllables are temporally stereotypical, syllable sequences can vary and follow complex, probabilistic transition rules. Recent experiments and computational models suggest that a syllable is encoded in a chain network of projection neurons in premotor nucleus HVC (proper name). Precisely timed spikes propagate along the chain, driving vocalization of the syllable through downstream nuclei. However, the neural basis of the probabilistic transitions between the syllables is not understood. Here we propose that variable syllable sequences are generated through spike propagations in a network in HVC in which the syllable-encoding chain networks are connected into a branching chain pattern. The neurons mutually inhibit each other through the inhibitory HVC interneurons, and are driven by external inputs from nuclei upstream of HVC. At a branching point that connects the final group of a chain to the first groups of several chains, the spike activity selects one branch to continue the propagation. The selection is probabilistic, and is due to the winner-take-all mechanism mediated by the inhibition and noise. The transitions between the chains are Markovian. If the same syllable can be driven by multiple chains, the generated syllable sequences are statistically described by partially observable Markov models. We suggest that the syntax of birdsong syllable sequences is embedded in the connection patterns of HVC projection neurons.

Neural mechanisms for learned birdsong

Learning by imitation is essential for transmitting many aspects of human culture, including speech, language, art, and music. How the human brain enables imitation remains a mystery, but the underlying neural mechanisms must harness sensory feedback to adaptively modify performance in reference to the object of imitation. Although examples of imitative learning in nonhuman animals are relatively rare, juvenile songbirds learn to sing by copying the song of an adult tutor. The delineation of neural circuits for birdsong raises the promise that this complex form of vocal learning, which bears strong parallels to human speech learning, can be understood in terms of underlying neural mechanisms. This promise is now being more fully realized, with recent experimental advances leading to better understanding of the central motor codes for song and the central mechanisms by which auditory experience modifies song motor commands to enable vocal learning.

Social context rapidly modulates the influence of auditory feedback on avian vocal motor control

Sensory feedback is important for the learning and control of a variety of behaviors. Vocal motor production in songbirds is a powerful model system to study sensory influences on behavior because the learning, maintenance, and control of song are critically dependent on auditory feedback. Based on previous behavioral and neural experiments, it has been hypothesized that songs produced in isolation [undirected (UD) song] represent a form of vocal practice, whereas songs produced to females during courtship interactions [female-directed (FD) song] represent a form of vocal performance. According to this "practice versus performance" framework, auditory feedback should be more influential when birds engage in vocal practice than when they engage in vocal performance. To directly test this hypothesis, we used a computerized system to perturb auditory feedback at precise locations during the songs of Bengalese finches and compared the degree to which feedback perturbations caused song interruptions as well as changes to the sequencing and timing of syllables between interleaved renditions of UD and FD song. We found that feedback perturbation caused fewer song interruptions and smaller changes to syllable timing during FD song than during UD song. These data show that changes in the social context in which song is produced rapidly modulate the influence of auditory feedback on song control in a manner consistent with the practice versus performance framework. More generally, they indicate that, for song, as for other motor skills including human speech, the influence of sensory feedback on activity within vocal premotor circuitry can be dynamically modulated.

Recognizing sequences of sequences

The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of ‘stable heteroclinic channels’. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.

Despite tremendous advances in neuroscience, we cannot yet build machines that recognize the world as effortlessly as we do. One reason might be that there are computational approaches to recognition that have not yet been exploited. Here, we demonstrate that the ability to recognize temporal sequences might play an important part. We show that an artificial decoding device can extract natural speech sounds from sound waves if speech is generated as dynamic and transient sequences of sequences. In principle, this means that artificial recognition can be implemented robustly and online using dynamic systems theory and Bayesian inference.

Brain stem feedback in a computational model of birdsong sequencing

Uncovering the roles of neural feedback in the brain is an active area of experimental research. In songbirds, the telencephalic premotor nucleus HVC receives neural feedback from both forebrain and brain stem areas. Here we present a computational model of birdsong sequencing that incorporates HVC and associated nuclei and builds on the model of sparse bursting presented in our preceding companion paper. Our model embodies the hypotheses that 1) different networks in HVC control different syllables or notes of birdsong, 2) interneurons in HVC not only participate in sparse bursting but also provide mutual inhibition between networks controlling syllables or notes, and 3) these syllable networks are sequentially excited by neural feedback via the brain stem and the afferent thalamic nucleus Uva, or a similar feedback pathway. We discuss the model's ability to unify physiological, behavioral, and lesion results and we use it to make novel predictions that can be tested experimentally. The model suggests a neural basis for sequence variations, shows that stimulation in the feedback pathway may have different effects depending on the balance of excitation and inhibition at the input to HVC from Uva, and predicts deviations from uniform expansion of syllables and gaps during HVC cooling.

Dynamics of crowing development in the domestic Japanese quail (Coturnix coturnix japonica)

Species-specific behaviours gradually emerge, via incomplete patterns, to the final complete adult form. A classical example is birdsong, a learned behaviour ideally suited for studying the neural and molecular substrates of vocal learning. Young songbirds gradually transform primitive unstructured vocalizations (subsong, akin to human babbling) into complex, stereotyped sequences of syllables that constitute adult song. In comparison with birdsong, territorial and mating calls of vocal non-learner species are thought to exhibit little change during development. We revisited this issue using the crowing behaviour of domestic Japanese quail (Coturnix coturnix japonica). Crowing activity was continuously recorded in young males maintained in social isolation from the age of three weeks to four months. We observed developmental changes in crow structure, both the temporal and the spectral levels. Speed and trajectories of these developmental changes exhibited an unexpected high inter-individual variability. Mechanisms used by quails to transform sounds during ontogeny resemble those described in oscines during the sensorimotor phase of song learning. Studies on vocal non-learners could shed light on the specificity and evolution of vocal learning.

Plastic and stable electrophysiological properties of adult avian forebrain song-control neurons across changing breeding conditions

Steroid sex hormones drive changes in the nervous system and behavior in many animal taxa, but integrating the former with the latter remains challenging. One useful model system for meeting this challenge is seasonally breeding songbirds. In these species, plasma testosterone levels rise and fall across the seasons, altering song behavior and causing dramatic growth and regression of the song-control system, a discrete set of nuclei that control song behavior. Whereas the cellular mechanisms underlying changes in nucleus volume have been studied as a model for neural growth and degeneration, it is unknown whether these changes in neural structure are accompanied by changes in electrophysiological properties other than spontaneous firing rate. Here we test the hypothesis that passive and active neuronal properties in the forebrain song-control nuclei HVC and RA change across breeding conditions. We exposed adult male Gambel's white-crowned sparrows to either short-day photoperiod or long-day photoperiod and systemic testosterone to simulate nonbreeding and breeding conditions, respectively. We made whole-cell recordings from RA and HVC neurons in acute brain slices. We found that RA projection neuron membrane time constant, capacitance, and evoked and spontaneous firing rates were all increased in the breeding condition; the measured electrophysiological properties of HVC interneurons and projection neurons were stable across breeding conditions. This combination of plastic and stable intrinsic properties could directly impact the song-control system's motor control across seasons, underlying changes in song stereotypy. These results provide a valuable framework for integrating how steroid hormones modulate cellular physiology to change behavior.

Wireless neural stimulation in freely behaving small animals

We introduce a novel wireless, low-power neural stimulation system for use in freely behaving animals. The system consists of an external transmitter and a miniature, implantable wireless receiver-stimulator. The implant uses a custom integrated chip to deliver biphasic current pulses to four addressable bipolar electrodes at 32 selectable current levels (10 microA to 1 mA). To achieve maximal battery life, the chip enters a sleep mode when not needed and can be awakened remotely when required. To test our device, we implanted bipolar stimulating electrodes into the songbird motor nucleus HVC (formerly called the high vocal center) of zebra finches. Single-neuron recordings revealed that wireless stimulation of HVC led to a strong increase of spiking activity in its downstream target, the robust nucleus of the arcopallium. When we used this device to deliver biphasic pulses of current randomly during singing, singing activity was prematurely terminated in all birds tested. Thus our device is highly effective for remotely modulating a neural circuit and its corresponding behavior in an untethered, freely behaving animal.