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Neural sequences are a fundamental feature of brain dynamics underlying diverse behaviours, but the mechanisms by which they develop during learning remain unknown. Songbirds learn vocalizations composed of syllables; in adult birds, each syllable is produced by a different sequence of action potential bursts in the premotor cortical area HVC. Here we carried out recordings of large populations of HVC neurons in singing juvenile birds throughout learning to examine the emergence of neural sequences. Early in vocal development, HVC neurons begin producing rhythmic bursts, temporally locked to a prototype syllable. Different neurons are active at different latencies relative to syllable onset to form a continuous sequence. Through development, as new syllables emerge from the prototype syllable, initially highly overlapping burst sequences become increasingly distinct. We propose a mechanistic model in which multiple neural sequences can emerge from the growth and splitting of a commo n precursor sequence., National Institutes of Health (U.S.) (Grant R01DC009183), National Science Foundation (U.S.) (Grant DGE-114747)

In songbirds, the remarkable temporal precision of song is generated by a sparse sequence of bursts in the premotor nucleus HVC. To distinguish between two possible classes of models of neural sequence generation, we carried out intracellular recordings of HVC neurons in singing zebra finches (Taeniopygia guttata). We found that the subthreshold membrane potential is characterized by a large, rapid depolarization 5-10 ms before 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 ∼10-ms duration, probably 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 provide insight into the fundamental mechanisms by which neural circuits can generate complex sequential behaviours.

Many complex behaviours, like speech or music, have a hierarchical organization with structure on many timescales, but it is not known how the brain controls the timing of behavioural sequences, or whether different circuits control different timescales of the behaviour. Here we address these issues by using temperature to manipulate the biophysical dynamics in different regions of the songbird forebrain involved in song production. We find that cooling the premotor nucleus HVC (formerly known as the high vocal centre) slows song speed across all timescales by up to 45 per cent but only slightly alters the acoustic structure, whereas cooling the downstream motor nucleus RA (robust nucleus of the arcopallium) has no observable effect on song timing. Our observations suggest that dynamics within HVC are involved in the control of song timing, perhaps through a chain-like organization. Local manipulation of brain temperature should be broadly applicable to the identification of neural circuitry that controls the timing of behavioural sequences and, more generally, to the study of the origin and role of oscillatory and other forms of brain dynamics in neural systems.

Birdsong is characterized by the modulation of sound properties over a wide image of timescales. Understanding the mechanisms by which the brain organizes this complex temporal behaviour is a central motivation in the study of the song control and learning system. Here we present evidence that, in addition to central neural control, a further level of temporal organization is provided by nonlinear oscillatory dynamics that are intrinsic to the avian vocal organ. A detailed temporal and spectral examination of song of the zebra finch (Taeniopygia guttata) reveals a class of rapid song modulations that are consistent with transitions in the dynamical state of the syrinx. Furthermore, in vitro experiments show that the syrinx can produce a sequence of oscillatory states that are both spectrally and temporally complex in response to the slow variation of respiratory or syringeal parameters. As a consequence, simple variations in a small number of neural signals can result in a complex acoustic sequence.

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.