By Anthony Leonardo, Harvard University

We have all heard the songs of birds, heralding the approach of spring. As beautiful as these songs are, there is an even greater beauty to be appreciated, literally beneath the surface, in the elegant mechanisms used by the bird's brain to generate the song. Just as the cover of a watch can be removed to reveal a dazzling array of interlocking gears whose combined activity encodes time, in this note I will attempt to sketch out the broad strokes of our current understanding of song control, and reveal some of the beauty of how birds sing.

It has been known for many years that songbirds have a specialized set of brain nuclei dedicated to song learning and song production (Nottebohm et al. 1976). Although these two processes are intertwined, our understanding of song production is most detailed through three stages of motor control (Figure 1). The high-level control nucleus, HVC, encodes the unique sequence of sounds that make up the bird's song (Hahnloser et al. 2002; Yu & Margoliash 1996). Each HVC neuron is activated for 5–10 ms at a single time during the song (Figure 1, top row; Hahnloser et al. 2002). So, for example, HVC neuron 1 is associated with sound 1, HVC neuron 2 is associated with sound 2, etc. However, HVC does not generate sound itself, nor does it even project to the motor neurons that drive the sound-producing vocal muscles; in this sense, HVC really only encodes the sequence, rather than the content, of the sound elements in the song.

HVC lies at the top of the song generation hierarchy; at the bottom lies the vocal muscles and the syrinx (the vocal organ). Contractions of these muscles puts the syrinx into different dynamical states so that different sounds are produced when the abdominal muscles force air out of the air sacs and through the syrinx (Fee et al. 1998; Goller & Suthers 1996; Wild et al. 1998) (Figure 1, third row). The song contains some sound elements that change very rapidly, in a few milliseconds, and other sound elements that change very slowly, over hundreds of milliseconds (Figure 1, bottom row). Sounds that change quickly are generally produced by fast changes in vocal muscles activity, and sounds that change slowly are produced by fixed configurations of the vocal muscles. There is thus a relatively direct correspondence between the time course of changes in these sounds, and the time course of activation in the vocal muscles (Figure 1, third row).

The temporal structure in the song and the vocal muscle signals spans a wide range of time scales, yet the HVC neurons that encode the song sequence are active only briefly. This raises an interesting question: how is the sparse HVC activity transformed into continuous patterns of vocal muscle activity? Sequence-encoding neurons in HVC project to premotor neurons in nucleus RA; these RA neurons connect directly to the motor neurons in the brainstem that drive the vocal muscles (Vicario 1991). The central role of RA is to map the temporal sequence onto particular acoustic patterns (Leonardo & Fee 2005). Like HVC neurons, RA neurons are also active only briefly, for ~10 ms. Unlike HVC neurons, however, RA neurons are active at multiple times in the song and therefore provide little information about the temporal position in the song sequence. Individual RA neurons thus encode neither song temporal structure nor song sequence (Figure 1, second row).

It is at the level of RA population activity, rather than individual neurons, that we begin to understand how neural activity is transformed into sound. There is a large convergence between RA and the vocal muscles: roughly 1000 RA neurons (Gurney 1981) connect, via motor neurons in the brainstem, to each vocal muscle (Greenewalt 1968). This is somewhat like a series of 1000 input pipes (RA neurons) connecting to a single output pipe (a vocal muscle). Even if we turn the water of the individual input pipes on and off very quickly, as long as one of them is always on at any instant in time, there will always be a constant flow of water from the output pipe. Similarly, we find that although individual RA neurons turn on and off very quickly, the population activity, if combined appropriately, can be transformed into a continuous muscle control signal (Leonardo & Fee 2005). The water pipe analogy highlights another unusual aspect of the RA code. If we were to choose 100 input pipes at random, and poured 1 cup of water into each simultaneously, we would get 100 cups of water from the output pipe—regardless of which input pipes we choose. This property is called degeneracy: there is a many-to-one mapping from input pipe combinations to the same output. The convergence from RA to the vocal muscles creates a similar degeneracy, allowing many different patterns of RA activity to generate the same sound (Leonardo & Fee 2005). We suspect this degeneracy in the neural code may allow the bird to have more efficient song learning—but this is a hypothesis that has yet to be verified.

Zebra finch song is generated by the concerted activity of many brain areas, and by carefully studying the system as a whole one begins to appreciate the beauty of the underlying mechanics. Just as the hands on a watch can seem to move continuously, revealing no more than a whispered tick-tick-tick of the gears within, birdsong, with its wide range of fast and slow temporal structure, is generated by neural circuitry that runs on a single fast clock. Each tick of this clock, encoded in an HVC-RA activity pattern, describes the song at one moment in time. Although song itself reveals little of these neural steps, they lie just below the surface waiting to be heard.

References

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Gurney ME. 1981. Hormonal control of cell form and number in the zebra finch song system. Journal of Neuroscience 1: 658–673. [Link]

Hahnloser RHR, Kozhevnikov AA, Fee MS. 2002. An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419: 65–70. [Link]

Leonardo A, Fee M. 2005. Ensemble coding of vocal control in a songbird. Journal of Neuroscience: in press.

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