Temperature Manipulation in Songbird Brain Implicates the Premotor Nucleus HVC in Birdsong Syntax.

Variable motor sequences of animals are often structured and can be described by probabilistic transition rules between action elements. Examples include the songs of many songbird species such as the Bengalese finch, which consist of stereotypical syllables sequenced according to probabilistic rules (song syntax). The neural mechanisms behind such rules are poorly understood. Here, we investigate where the song syntax is encoded in the brain of the Bengalese finch by rapidly and reversibly manipulating the temperature in the song production pathway. Cooling the premotor nucleus HVC (proper name) slows down the song tempo, consistent with the idea that HVC controls moment-to-moment timings of acoustic features in the syllables. More importantly, cooling HVC alters the transition probabilities between syllables. Cooling HVC reduces the number of repetitions of long-repeated syllables and increases the randomness of syllable sequences. In contrast, cooling the downstream motor area RA (robust nucleus of the acropallium), which is critical for singing, does not affect the song syntax. Unilateral cooling of HVC shows that control of syllables is mostly lateralized to the left HVC, whereas transition probabilities between the syllables can be affected by cooling HVC in either hemisphere to varying degrees. These results show that HVC is a key site for encoding song syntax in the Bengalese finch. HVC is thus involved both in encoding timings within syllables and in sequencing probabilistic transitions between syllables. Our finding suggests that probabilistic selections and fine-grained timings of action elements can be integrated within the same neural circuits.SIGNIFICANCE STATEMENT Many animal behaviors such as birdsong consist of variable sequences of discrete actions. Where and how the probabilistic rules of such sequences are encoded in the brain is poorly understood. We locally and reversibly cooled brain areas in songbirds during singing. Mild cooling of area HVC in the Bengalese finch brain-a premotor area homologous to the mammalian premotor cortex-alters the statistics of the syllable sequences, suggesting that HVC is critical for birdsong sequences. HVC is also known for controlling moment-to-moment timings within syllables. Our results show that timing and probabilistic sequencing of actions can share the same neural circuits in local brain areas.

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.

Singing-related activity of identified HVC neurons in the zebra finch.

High vocal center (HVC) is part of the premotor pathway necessary for song production and is also a primary source of input to the anterior forebrain pathway (AFP), a basal ganglia-related circuit essential for vocal learning. We have examined the activity of identified HVC neurons of zebra finches during singing. Antidromic activation was used to identify three classes of HVC cells: neurons projecting to the premotor nucleus RA, neurons projecting to area X in the AFP, and putative HVC interneurons. HVC interneurons are active throughout the song and display tonic patterns of activity. Projection neurons exhibit highly phasic stereotyped firing patterns. X-projecting (HVC((X))) neurons burst zero to four times per motif, whereas RA-projecting neurons burst extremely sparsely--at most once per motif. The bursts of HVC projection neurons are tightly locked to the song and typically have a jitter of <1 ms. Population activity of interneurons, but not projection neurons, was significantly correlated with syllable patterns. Consistent with the idea that HVC codes for the temporal order in the song rather than for sound, the vocal dynamics and neural dynamics in HVC occur on different and uncorrelated time scales. We test whether HVC((X)) neurons are auditory sensitive during singing. We recorded the activity of these neurons in juvenile birds during singing and found that firing patterns of these neurons are not altered by distorted auditory feedback, which is known to disrupt learning or to cause degradation of song already learned.

Sleep-related neural activity in a premotor and a basal-ganglia pathway of the songbird.

During singing, neurons in premotor nucleus RA (robust nucleus of the arcopallium) of the zebra finch produce complex temporal sequences of bursts that are recapitulated during sleep. RA receives input from nucleus HVC via the premotor pathway, and also from the lateral magnocellular nucleus of the anterior nidopallium (LMAN), part of a basal ganglia-related circuit essential for vocal learning. We explore the propagation of sleep-related spike patterns in these two pathways and their influences on RA activity. We promote sleep in head-fixed birds by injections of melatonin and make single-neuron recordings from the three major classes of neurons in HVC: RA-projecting neurons, Area X-projecting neurons, and interneurons. We also record LMAN neurons that project to RA. In paired recordings, spike trains from identified HVC neuron types are strongly coherent with spike trains in RA neurons, whereas LMAN projection neurons on average exhibit only a weak coherency with neurons in HVC and RA. We further examine the relative roles of HVC and LMAN in generating RA burst sequences with reversible inactivation. Lidocaine inactivation of HVC completely abolishes bursting in RA, whereas inactivation of LMAN has no effect on burst rates in RA. In combination, our data suggest that in adult birds, RA burst sequences in sleep are driven via the premotor pathway from HVC. We present a simple generative model of spike trains in HVC, RA, and LMAN neurons that is able to qualitatively reproduce observed coherency functions. We propose that commonly observed coherency peaks at positive and negative time lags are caused by sequentially correlated HVC activity.

Neural mechanisms of vocal sequence generation in the songbird.

Little is known about the biophysical and neuronal circuit mechanisms underlying the generation and learning of behavioral sequences. Songbirds provide a marvelous animal model in which to study these phenomena. By use of a motorized microdrive to record the activity of single neurons in the singing bird, we are beginning to understand the circuits that generate complex vocal sequences. We describe recent experiments elucidating the role of premotor song-control nucleus HVC in the production of song. We find that HVC neurons projecting to premotor nucleus RA each generate a single burst of spikes at a particular time in the song and may form a sparse representation of temporal order. We incorporate this observation into a working hypothesis for the generation of vocal sequences in the songbird, and examine some implications for song learning.

An ultra-sparse code underlies the generation of neural sequences in a songbird.

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the 'grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.