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Auditory processing of vocal sounds in birds
Auditory processing of vocal sounds in birds Fre´de´ric E Theunissen and Sarita S Shaevitz The avian auditory system has become a model system to investigate how vocalizations are memorized and processed by the brain in order to mediate behavioral discrimination and recognition. Recent studies have shown that most of the avian auditory system responds preferentially and efficiently to sounds that have natural spectro-temporal statistics. In addition, neurons in secondary auditory forebrain areas have plastic response properties and are the most active when processing behaviorally relevant vocalizations. Physiological measurements show differential responses for vocalizations that were recently learned in discrimination tasks, and for the tutor song, a longer-term auditory memory that is used to guide vocal learning in male songbirds. Addresses Department of Psychology and Neurosciences Institute, University of California, Berkeley, CA, USA
by the avian auditory system. In keeping with the theme of vocal learning, researchers are investigating the role of the auditory system in making memories of a model or tutor song and in processing the bird’s own vocalizations during song production and learning [4,5]. In addition, researchers studying auditory processing of vocal sounds in both oscines and non-oscines have the independent goal of elucidating the neurobiology of purely perceptual tasks. Birds are particularly adept at recognizing conspecifics solely on the basis of their vocalizations, and often in very unfavorable acoustical environments [6,7,8]. How the auditory system accomplishes such complex auditory discrimination and auditory scene analysis tasks is an unresolved mystery in auditory science [9].
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Recent studies have investigated the physiology of natural sound processing in the avian auditory system with an impressive array of experimental techniques: single unit recordings [10], awake behaving recordings [11], gene expression [12], auto-radiography [13] and functional magnetic resonance imaging (fMRI) [14]. The emerging picture from these studies is that first, the auditory system of songbirds shows some degree of specialization for processing natural sounds and vocalizations, second, the specialization for behaviorally relevant vocalizations can be learned and third, this specialization can be understood in terms of the joint spectral-temporal tuning properties of neuronal ensembles.
DOI 10.1016/j.conb.2006.07.003
Auditory behavior
Corresponding author: Theunissen, Fre´de´ric E. ([email protected])
Current Opinion in Neurobiology 2006, 16:400–407 This review comes from a themed issue on Sensory systems Edited by Yang Dan and Richard D Mooney Available online 13th July 2006
Introduction Animals rely on auditory processing for survival. Listening to others enables an animal to classify them as conspecific or heterospecific, neighbor or stranger, mate or non-mate, kin or non-kin. Listening serves another important function for birds of the suborder oscine, that is, the songbirds. Juvenile songbirds listen to adult conspecifics to form a memory of a normal song that they will use to guide their own song learning. Depending upon the songbird species, songbirds begin vocalizing either while the tutor memory is forming or once it is formed [1]. Nearly 50 years ago, researchers began to examine carefully the ‘song system’ of songbirds, a system of specialized brain structures found only in birds that learn to sing [2]. That system of structures has become a well-known model for studying the neural basis of vocal learning [3]. More recently, neuroscientists have moved beyond characterizing the song system and begun to study how complex sounds, including vocalizations, are processed Current Opinion in Neurobiology 2006, 16:400–407
Birds use song and other vocalizations for an array of communication tasks in the wild. Males use songs for territorial defense and mate attraction [15], and male– female pairs use songs for pair-bonding and cooperation [16,17]. Although only the males of most temperate songbird species sing, communication calls are produced by both male and female birds and are varied in function. Calls are used to maintain contact (contact call), restore contact (separation call), obtain food (begging call) or advertise danger (alarm call) [18]. The behavioral discrimination of conspecific and heterospecific calls and song has been well documented in the laboratory [8,19]. Within a species, songbirds use song to discriminate between neighbors and strangers, relatives and non-relatives, and mates and non-mates [20]. Very young male songbirds that have not yet started to sing show their preference for the familiar tutor song over unfamiliar song by spending more time next to speaker, and producing more calls in response to it. In addition to this early discrimination ability, song learning requires the capacity for longer-term storage of the tutor song [21]. www.sciencedirect.com
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Similar to male songbirds, female songbirds show familiarity-based discrimination among different individual songs [22], even after a relatively long period of time of not hearing the familiar song [23]. In fact, female discrimination on the basis of the quality of conspecific song appears to be similar or superior to that of males [24]. The discriminability for subtle features of vocalizations has also been demonstrated with physiological measures by evaluating the effect of sexy song features on hormonal levels in female songbirds [25,26]. During auditory perceptual tasks that are measured quantitatively in the laboratory, such as psycho-acoustical tasks of loudness threshold and discrimination, pitch discrimination, and temporal discrimination, birds show a performance that is similar or slightly worse to that of humans [27]. More importantly, songbirds excel in psycho-acoustical tasks that involve sounds that have some of the spectral and temporal qualities of their own vocalizations [28]. The computations performed on both simple and complex sounds (e.g. vocalizations) by the avian auditory system must, therefore, be similar to those performed by the human auditory system. Moreover, the specialization for processing and memorizing the complexities of song and using this memory for vocal learning make the avian
auditory system an excellent model system to study the neural mechanisms of speech processing and learning.
Anatomy Whereas the specialized brain circuit for song production and learning has evolved fairly recently in evolutionary time and in only a few avian orders [29], the avian auditory system is much older and shares features across all avian groups and other vertebrates, including mammals [30]. The most noticeable similarity rests in the number of auditory nuclei (or neural processing stages) and the pattern of feed-forward connections from the cochlear nucleus to the auditory forebrain. As illustrated in Figure 1, afferents from the inner ear project to the cochlear nucleus in the medulla. Similar to the situation in mammals, there is both a direct and an indirect route connecting the cochlear nucleus and the auditory midbrain. In the midbrain, these pathways converge in the dorsal lateral nucleus of the mesencephalon (MLd), which is analogous to the inferior colliculus (IC) in mammals. The auditory midbrain projects to ovoidalis (Ov), a relay nucleus in the thalamus, just as the IC projects to the medial geniculate body (MGB) in mammals. Ovoidalis, in turn, sends projections to the primary auditory area in the pallium, called field L [31]. Principally for this reason, the
Figure 1
Anatomical and functional cartoons of the avian auditory system shown in saggital section. The anatomical cartoon shows the auditory nuclei and NCM regions (fields L and CM) in grey in addition to two of the song system nuclei (CN, SO, LL, MLd and Ov) that would be found in songbirds (HVC and RA). The feed-forward pathways are shown in solid and the feedback pathway is shown with a dotted line. Note that not all the pathways are shown. For example, the reciprocal pathway between NCM and CM has been omitted for clarity. The diagram is also only approximately anatomically correct: NCM would be found in a more medial region than indicated here. The functional cartoon summarizes and simplifies some of the findings described in the text. Mld is tuned for low-level statistics of natural sounds and its population response efficiently represents the temporal changes in the amplitude envelope of the sound. The primary auditory forebrain (field L) also efficiently represents sounds with natural statistics and its heterogenous neurons code complex temporal and spectral acoustical features. The secondary auditory areas (NCM and CM) are sensitive to higher order acoustic features of vocalizations. The neural response in these areas is also affected by the sounds that the animal has learned and remembered. www.sciencedirect.com
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avian field L can be thought as analogous to the mammalian primary auditory cortex (A1). Field L has been further divided into subregions (L1, L2a, L2b, L3, L) on the basis of differences in cytoarchitecture and connectivity [32,33]. Input from the auditory thalamus goes to subregions L2a and L2b, which in turn project to L1 and L3. Subregions L1 and L3 make bi-directional connections with two secondary auditory areas in the pallium: the nidopallium caudal medial (NCM) and the caudal mesopallium (CM). Although analogies at this level might be unnecessary and premature given our state of knowledge, it is tempting to make a link between L2 and layer 4 of A1, with L1 and L3 corresponding to the supragranular layers of the cortex. NCM and CM could also be compared to the superficial layers of auditory cortex or to secondary mammalian auditory regions, such as the lateral belt in primates or Wernicke’s area in humans [34]. Only further neurophysiological work in mammals and birds will tell us whether such comparisons are relevant.
Neurophysiology Neurophysiologists have recorded responses to vocalizations and other complex sounds throughout most of the auditory system shown in Figure 1. The results can be organized along three directions of analysis: first, the selectivity of the responses for vocalizations relative to other complex sounds; second, the effect of short term experience, learning and development on the responses; and third, the more quantitative description of the tuning of single neurons for specific sound features that could explain the selectivity observed for vocalizations. This third line of analysis makes the bridge between a neuroethological study of auditory responses that provides the link to behavior and a more classical auditory analysis of the tuning response along well defined acoustic parameters that provides insight on the underlying computations (Mainen, this issue). Neural selectivity for vocalizations Dorsal lateral nucleus of the mesencephalon
Auditory neurons in MLd respond robustly to pure tones, complex tones and songs [35,36]. However, stimuli that contain the spectro-temporal features found in natural sounds elicited a richer ensemble of spike patterns from MLd neurons than simple synthetic sounds did. Information theoretic calculations show that these spike patterns led to higher measures of neural discriminability for natural sounds relative to synthetic sounds [36]. Neuronal responses to simple synthetic sounds show tuning for fast temporal modulations and exhibit precise spike timing [35]. This suggested that these properties might be beneficial for encoding the fast changes that are characteristic of the amplitude envelopes of song. Woolley et al. [37] validated this hypothesis with a recent analysis of the ensemble response to song: the Current Opinion in Neurobiology 2006, 16:400–407
population response encodes the temporal changes of song very accurately. Field L
Selectivity for natural sounds was tested in early work in the primary avian auditory forebrain, field L, and the neighboring secondary auditory areas NCM and CM [38,39]. The results from those initial characterizations showed that a subset of neurons in field L did not produce robust responses to simple sounds such as tones or noise but, instead, responded selectively to specific vocalizations. These selective neurons were found in greater numbers outside the thalamorecipient subregion L2 [40]. In many cases the selectivity for vocalizations could be explained in terms of selectivity for specific spectrotemporal acoustical features of the vocalizations [40,41]. The issue of the scope of the selectivity was revisited in more detail recently by analyzing the statistics of the spectro-temporal structure found in song [42] and generating synthetic sounds that preserved these statistics to various degrees [36,43]. It was found that, on average, neurons in field L show stronger responses to conspecific song than to sequences of tones with the same spectral distribution or synthetic harmonic sounds that had the same pitch as the harmonic sounds found in song [43]. By contrast, the neural responses to synthetic stimuli that preserved the joint spectral-temporal statistics of song were similar in strength to those of the natural songs. In particular, modulation-limited noise, a form of white-noise for which the fluctuations in the spectro-temporal envelope are limited to lower frequencies, such as those found in natural sounds, is able to drive auditory neurons robustly at all stages of the auditory system [36]. Modulation-limited noise has intensity fluctuations in time and frequency that give it a tempo and pitch that is easily perceived, whereas these percepts are absent in white noise. Nonetheless, the patterns in spike train responses showed a larger information capacity for song stimuli than they did in response to the matched synthetic sounds: even with the same number of spikes, the neural responses to songs could be used to discriminate among different songs with better results than one would obtain for different segments of synthetic sound [36]. To summarize, selectivity for conspecific sounds is present in avian primary auditory forebrain but restricted to the relatively low level statistics of the spectral-temporal acoustical structure of vocalizations and of other natural sounds. The efficient representation of such sounds might constitute the building blocks for further computations and more selective representations in other auditory regions. Caudal mesopallium
The selectivity for natural sounds in secondary auditory forebrain regions NCM and CM has also been examined. www.sciencedirect.com
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Neurophysiological recordings with complex stimuli showed that the selectivity in CM based on mean spike rate was not different from that found in field L [43]. However, when the response to tones is compared to the response to vocalizations, the number of selective units is greater in L2 than in CM or in the auditory nidopallium excluding L2 (i.e. L1, L3 and NCM) [40]. Furthermore, information theoretic analyses suggest some degree of hierarchical processing, with CM neurons being more sensitive to the natural order in time and frequency of naturally occurring spectro-temporal acoustical features than field L neurons are [36]. Evidence for hierarchical processing between L and CM can also be demonstrated by the degree to which linear models of stimulus– response functions can explain the response of CM neurons to song [44,45]. In female songbirds, electrolytic lesions in CM eliminated preference for conspecific song over heterospecific songs, as assessed by observing the number of sexual displays [46].
with NCM [50]. A direct comparison using vocalizations in NCM and all the subareas of field L is still needed to better quantify the differences in selectivity between these areas.
Nidopallium caudal medial
Learned responses and development
Neurophysiological responses to sounds in the avian auditory system have also been assessed by measuring the level of mRNA of immediate early genes expressed in neurons, in particular the gene zenk (also known as Zif268). In these experiments, a particular bird hears a single sound repeatedly and is then rapidly sacrificed to assess the level of gene induction. By comparing genetic responses across birds and sound types the neural selectivity for particular sounds can be assessed indirectly. In female songbirds, zenk expression is correlated with the presence of features of conspecific songs that are also preferred in mate selection [47]. In male songbirds, zenk expression is higher for conspecific song than for heterospecific song, whereas no expression is seen in response to tone bursts [48]. Synthetic stimuli with more natural spectro-temporal features have also been used in gene expression experiments and yielded interesting results. Whereas whistle sounds of different pitches taken from the canary song elicited clustered and topographically organized (and, thus, differentiable) patterns of gene expression, synthetic whistle or guitar notes at the same pitch elicited more distributed (and putatively less informative) patterns of activation [12]. Changes in the blood oxygenation level dependent (BOLD) response measured with fMRI showed that NCM has a differential response for song versus noise that is not observed in field L [14]. All these results suggest that unlike field L, NCM shows selectivity for higher level attributes of conspecific song.
Vocal sound recognition in birds involves both innate and learned processing. For example, as demonstrated by measuring their heart rate, young birds recognize conspecific song [51] or begging calls [52]. However, song learning, neighbor recognition and mate recognition are examples of behaviors that require the learned recognition of particular songs. Short-term plasticity in adult birds has been measured in the avian auditory system in both NCM and CM, whereas longer term developmental changes have been observed in the primary auditory field.
Neurophysiological studies in NCM also suggest selectivity for complex sounds [49], which might be absent in subarea L2a [40]. When simple tones are used to characterize the response properties of neurons in both L2 and NCM, one observes tighter frequency tuning and less or no habituation to repeated stimulation in L2 as compared www.sciencedirect.com
To summarize, the two secondary auditory areas CM and NCM show signs of selectivity in their electrophysiological responses for complex sounds. Nevertheless, what particular features are being selected for and what the degree of selectivity relative to other forebrain auditory areas is remain open questions. It is also indicated that the nature of the responses is different in these secondary areas: linearity of neural response is decreased relative to that in field L and gene expression is observed, whereas it is not observed in L2. The functional significance of these physiological differences is not yet clear. One potentially important difference might be the role of these secondary areas in learned auditory discriminations, as discussed in the next section.
The first evidence of plastic mechanisms in the auditory system of adult birds came from IEG expression studies in NCM. In these brain areas, the zenk response decreases to the repeated presentation of the same conspecific song but recovers completely upon the presentation of novel song. The decrease in zenk expression for a particular song persists, as it can be observed the following day [53]. Neurophysiological studies support the IEG results: repeated stimulation with the same song results in stimulus-dependent adaptation [49]. Because of these results and because zenk has been linked to synaptic plasticity in mammals, it has been proposed that the decrease in zenk expression to familiar songs is a marker of the learning process for familiar songs [54]. Recent studies have also implicated NCM in the longer term storage of the tutor song. Bolhuis and co-workers found that the expression of the gene c-fos was greater for the tutor song than for unfamiliar conspecific song and that the strength of the response was correlated with the degree of learning [55]. In a similar neurophysiological study, Vicario and co-workers compared the adaptation in the responses to tutor song with those observed in response to unfamiliar conspecific song or the bird’s own song. The different adaptation curves show that Current Opinion in Neurobiology 2006, 16:400–407
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the tutor song was familiar although it had not been heard for a prolonged period of time. The degree of familiarity was correlated with how well the bird copied the song [4]. It should be noted, however, that in both these studies the birds were also exposed repeatedly to their own song which resembled the tutor song and, indeed, it was shown that this vocal experience was important. More studies are, therefore, needed to address this potential confound and to understand how an adaptive response could be used as a template in vocal learning. Shorter-term plasticity has also been measured in neurophysiological recordings in CM. After training birds to recognize individual conspecific songs in either a twoalternative forced choice (AFC) or a go no-go paradigm, neurons in CM became selective as a population for the songs used in the experiment. Interestingly, the two songs in the AFC experiment elicited similar enhanced responses, whereas in the go no-go experiment, the response to the go song was enhanced relative to the response to the no-go song. Therefore, this learned selectivity cannot simply be explained by familiarity but must also involve top down processing that assigns different values to similarly familiar sounds [11]. The avian auditory system also has great potential as model to study the effect of experience on neural development. This line of research has just recently been initiated. The first study on this subject recorded significant changes in the response of field L neurons of birds that were deprived of normal acoustical experience during early development; both the selectivity and the organization of the frequency tuning properties were altered [56]. A natural acoustical environment during development might, therefore, be needed not only for complex behaviors such as song learning but also for simpler perceptual tasks such as pitch discrimination. Spectro-temporal tuning and representation of vocalizations
The neurophysiological experiments described above constitute substantial proof of both the selectivity and the plasticity of the avian auditory system. In addition, the demonstrated selectivity and learning can be directly related to the processing of behaviorally relevant vocalizations. However, these studies tell us little about the actual underlying neural mechanisms. For example, the actual neural representation and computations occurring at each stage were not directly addressed. To accomplish this, one needs to first characterize the stimulus–response function of individual neurons and then relate that function to the observed selectivity, or to use the functional description to track plasticity.
vocalizations [57,58]. In order to obtain unbiased stimulus–response functions given that only a subset of sounds is presented in any one set of experiments, regression techniques of normalization and regularization must be used [37,59]. In this manner, we were able to obtain the spectro-temporal receptive fields (STRFs) of single neurons and measure changes in the responses to different stimulus ensembles. The STRFs of single neurons in MLd, field L and CM constitute a heterogenous and complex set [10,37,41,44]. However, in all three brain regions the population response is tuned for the spectrotemporal modulations that are particularly informative in natural sounds [10]. This tuning can, in part, explain the selectivity for natural sounds found in field L and CM, because simple synthetic sounds such as tones or white noise do not contain the spectral and temporal structure that preferentially excites the neurons. Moreover, the tuning obtained from the STRFs also yields significantly different spike train patterns to different songs and, consequently, a high level of neural discrimination [10]. This prediction is consistent with the information-theoretic analysis of the neural response to different sounds described above. It should be noted, however, that the STRF only explains a fraction of the neural response and that non-linear functions between the sound and the neural response will have to be characterized to completely describe the stimulus–response relationships of the neurons. These non-linear responses also play a role in the selectivity for natural sounds [10], and this role could be crucial in higher auditory areas such as CM. The STRF methodology has also enabled the observation of tuning changes in neurons in MLd while processing song and modulation-limited noise stimuli. On average, the STRFs when processing song were tighter in time and more spread out in frequency than those to modulationlimited noise stimuli, indicating that the neurons became less sensitive to spectral features and more sensitive to temporal features. Moreover, the STRFs of different neurons are more similar to each other while processing song, resulting in a synchronization of the neural response across cells. This synchronized response enables a very accurate population code for the temporal features of the song [37]. Beyond the population code, it is clear that different subsets of neurons can be classified according to their STRFs, and that these subsets can subserve different auditory computations. There is also some initial evidence that in field L these functional groupings have an anatomical correlate, which could facilitate the study of the microcircuitry within and across auditory areas [41].
Conclusions and goals for the future In the avian auditory system, stimulus–response functions of single auditory neurons have been estimated directly from neural data obtained in response to Current Opinion in Neurobiology 2006, 16:400–407
There is no doubt that the study of the avian auditory system has already been successful. As a consequence of the strong ties to vocal behavior and the use of many www.sciencedirect.com
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different techniques from researchers with a breadth of expertise, the demonstration that the avian auditory system can be used to understand how vocalizations are processed and learned has already been made and, in our opinion, rivals or surpasses the demonstrations of similar properties in the mammalian auditory system. However, there is still much work to be done. As an overarching objective, avian auditory researchers need to go beyond demonstrations and into explanations. Can the neural selectivity be understood in terms of specific computations? How are these computations achieved through both the macro and the micro circuitry of the auditory system? What is the relationship between the slope of adaptation of zenk expression and the neural responses to tutor song, and how can these responses be used to guide vocal learning? What are the mechanisms of plasticity at the cellular or circuit level? Can we define and understand the role of functional groups within particular auditory regions? What is the relationship between these functional groups and the microcircuitry? These are just a few examples of important questions that we need to address. As a role model, we can use another well known avian auditory model: the barn owl hindbrain and midbrain auditory circuits for sound localization, in which a significant level of understanding has been achieved [60]. For this ambitious goal, we make a few suggestions. First, we need to be able to better integrate the different methodologies that are used to probe the physiology. The relationships among gene expression, BOLD responses in fMRI and electrophysiology in awake and anesthetized preparations need to be examined and understood. Second, using intracellular recordings the microcircuitry needs to be investigated and correlated to the different types of functional descriptions that have been found. Such level of analysis has already been performed in the song system with great success [61]. Third, because the processing of vocalizations is a computationally complex task, the use of modeling and theory will enable one to understand what computations are required and/or the potential significance of certain types of computations that are discovered [42,62–64]. Finally, we also believe that the findings in the avian auditory system will generalize to other animals including humans. We, therefore, encourage greater exchanges and collaborative efforts with auditory researchers in other systems, such as the marmoset [65], bat [66], macaque [67,68] and human [69]. The processing of vocal sounds by the brain is a complex task and much will be learned from both birds and the comparative approach.
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
Marler P: A comparative approach to vocal learning: song development in white-crowned sparrows. J Comp Physiol Psychol 1970, 71:1-25.
2.
Kroodsma DE, Konishi M: A suboscine bird (eastern phoebe, sayornis phoebe) develops normal song without auditory feedback. Anim Behav 1991, 42:477-487.
3.
Zeigler HP, Marler P: Behavioral Neurobiology of Birdsong. In Annals of the New York Academy of Sciences Volume 1016. Edited .by Zeigler HP, Marler P. The New York Academy of Sciences; 2002
4.
Phan ML, Pytte CL, Vicario DS: Early auditory experience generates long-lasting memories that may subserve vocal learning in songbirds. Proc Natl Acad Sci USA 2006, 103:1088-1093. Where is the tutor song stored in the songbird brain? The adaptation curve of the neurophysiological response to the tutor song suggests that this song is treated as familiar in NCM. The correlation of the strength of this effect and the quality of song learning suggests that NCM could play a role in the memory storage of the tutor song. 5.
Theunissen FE, Amin N, Shaevitz SS, Woolley SM, Fremouw T, Hauber ME: Song selectivity in the song system and in the auditory forebrain. Ann N Y Acad Sci 2004, 1016:222-245.
6.
Aubin T, Jouventin P, Hildebrand C: Penguins use the two-voice system to recognize each other. Proc Biol Sci 2000, 267:1081-1087.
7.
Leonard ML, Horn AG: Ambient noise and the design of begging signals. Proc Biol Sci 2005, 272:651-656.
8.
Appeltants D, Gentner TQ, Hulse SH, Balthazart J, Ball GF: The effect of auditory distractors on song discrimination in male canaries (serinus canaria). Behav Processes 2005, 69:331-341. Birds’ recognition abilities based on acoustical cues appear impressive on the basis of field observations and experiments. In this study, an auditory scene analysis task is performed in the laboratory and the discriminability is quantified as a function of the level and type of auditory distractor. The combination of behavioral experiments such as these and various physiological measurements is one of the strengths of the avian auditory system as a model system. 9.
Bergman A: Auditory Scene Analysis. MIT Press; 1990.
10. Woolley SM, Fremouw TE, Hsu A, Theunissen FE: Tuning for spectro-temporal modulations as a mechanism for auditory discrimination of natural sounds. Nat Neurosci 2005, 8:1371-1379. The authors present the first quantitative comparison between neural tuning and the statistics of natural sounds. The authors show that ensemble neural tuning emphasizes the most informative features of natural sounds. 11. Gentner TQ, Margoliash D: Neuronal populations and single cells representing learned auditory objects. Nature 2003, 424:669-674. 12. Ribeiro S, Cecchi GA, Magnasco MO, Mello CV: Toward a song code: evidence for a syllabic representation in the canary brain. Neuron 1998, 21:359-371. 13. Adret P: In search of the song template. Ann N Y Acad Sci 2004, 1016:303-324. 14. Van Meir V, Boumans T, De Groof G, Van Audekerke J, Smolders A, Scheunders P, Sijbers J, Verhoye M, Balthazart J, Van der Linden A: Spatiotemporal properties of the BOLD response in the songbirds’ auditory circuit during a variety of listening tasks. Neuroimage 2005, 25:1242-1255. 15. Catchpole CK, Slater PBJ: Bird Song. Biological Themes and Variations. Cambridge University Press; 1995.
Acknowledgements The work described in this review was funded by grants MH-059189 and DC-007293 from the National Institutes of Health to F Theunissen. www.sciencedirect.com
16. Marshall-Bal L, Slater PJ: Duet singing and repertoire use in threat signalling of individuals and pairs. Proc Biol Sci 2004, 271:S440-S443. Current Opinion in Neurobiology 2006, 16:400–407
406 Sensory systems
17. Hile AG, Plummer TK, Striedter GF: Male vocal imitation produces call convergence during pair bonding in budgerigars, melopsittacus undulatus. Anim Behav 2000, 59:1209-1218. 18. Marler P: Bird calls: their potential for behavioral neurobiology. Ann N Y Acad Sci 2004, 1016:31-44. 19. Dooling RJ, Brown SD, Klump GM, Okanoya K: Auditory perception of conspecific and heterospecific vocalizations in birds: evidence for special processes. J Comp Psychol 1992, 106:20-28. 20. Sherman P, Reeve H, Pfennig D: Recognition systems. In Behavioral Ecology. Edited by Krebs J, Davies NB. Blackwell Science; 1997. 21. Marler P, Peters S: Long-term storage of learned birdsong prior to production. Anim Behav 1982, 30:479-482.
vocalizations in the songbird midbrain. J Neurosci 2006, 26:2499-2512. 38. Leppelsack HJ, Vogt M: Responses of auditory neurons in the forebrain of a songbird to stimulation with species-specific sounds. J Comp Neurol 1976, 107:263-274. 39. Bonke D, Scheich H, Langner G: Responsiveness of units in the auditory neostriatum of the guinea fowl (numida meleagris) to species-specific calls and synthetic stimuli I. Tonotopy and functional zones of field L. J Comp Physiol 1979, 132:243-255. 40. Muller CM, Leppelsack HJ: Feature extraction and tonotopic organization in the avian auditory forebrain. Exp Brain Res 1985, 59:587-599. 41. Cousillas H, Leppelsack HJ, Leppelsack E, Richard JP, Mathelier M, Hausberger M: Functional organization of the forebrain auditory centres of the european starling: a study based on natural sounds. Hear Res 2005, 207:10-21.
22. Riebel K, Smallegange IM, Terpstra NJ, Bolhuis JJ: Sexual equality in zebra finch song preference: evidence for a dissociation between song recognition and production learning. Proc R Soc Lond B Biol Sci 2002, 269:729-733.
42. Singh NC, Theunissen FE: Modulation spectra of natural sounds and ethological theories of auditory processing. J Acoust Soc Am 2003, 114:3394-3411.
23. Miller D: Long term recognition of father’s song by female zebra finches (taeniopygia guttata). Nature 1979, 280:389-391.
43. Grace JA, Amin N, Singh NC, Theunissen FE: Selectivity for conspecific song in the zebra finch auditory forebrain. J Neurophysiol 2003, 89:472-487.
24. Searcy WA, Brenowitz EA: Sexual differences in species recognition of avian song. Nat 1988, 332:152-154.
44. Sen K, Theunissen FE, Doupe AJ: Feature analysis of natural sounds in the songbird auditory forebrain. J Neurophysiol 2001, 86:1445-1458.
25. Gil D, Leboucher G, Lacroix A, Cue R, Kreutzer M: Female canaries produce eggs with greater amounts of testosterone when exposed to preferred male song. Horm Behav 2004, 45:64-70. 26. Marshall RC, Leisler B, Catchpole CK, Schwabl H: Male song quality affects circulating but not yolk steroid concentrations in female canaries (serinus canaria). J Exp Biol 2005, 208:4593-4598. 27. Dooling RJ: Auditory perception in birds. In Acoustic Communication in Birds vol 1. Edited by Edited by Kroodsma DE, Miller EH. Academic Press; 1982:95-130. 28. Lohr B, Dooling RJ: Detection of changes in timbre and harmonicity in complex sounds by zebra finches (taeniopygia guttata) and budgerigars (melopsittacus undulatus). J Comp Psychol 1998, 112:36-47. 29. Jarvis ED, Ribeiro S, da Silva ML, Ventura D, Vielliard J, Mello CV: Behaviourally driven gene expression reveals song nuclei in hummingbird brain. Nature 2000, 406:628-632.
45. Gill PR, Zhang J, Woolley SM, Fremouw T, Theunissen FE: Sound representation methods for spectro-temporal receptive field estimation. Journal of Computational Neurosciences 2006. In press. 46. MacDougall-Shackleton SA, Hulse SH, Ball GF: Neural bases of song preferences in female zebra finches (taeniopygia guttata). Neuroreport 1998, 9:3047-3052. 47. Gentner TQ, Hulse SH, Duffy D, Ball GF: Response biases in auditory forebrain regions of female songbirds following exposure to sexually relevant variation in male song. J Neurobiol 2001, 46:48-58. 48. Mello CV, Vicario DS, Clayton DF: Song presentation induces gene expression in the songbird forebrain. Proc Natl Acad Sci USA 1992, 89:6818-6822. 49. Stripling R, Volman S, Clayton D: Response modulation in the zebra finch caudal neostriatum: relationship to nuclear gene regulation. J Neurosci 1997, 17:3883-3893.
30. Butler AB, Hodos W: Comparative Vertebrate Neuroanatomy: Evolution and Adaptation. Wiley-Liss; 1996.
50. Terleph TA, Mello CV, Vicario DS: Auditory topography and temporal response dynamics of canary caudal telencephalon. J Neurobiol 2006, 66:281-292.
31. Zaretsky MD, Konishi M: Tonotopic organization in the avian telencephalon. Brain Res 1976, 111:167-171.
51. Dooling R, Searcy M: Early perceptual selectivity in the swamp sparrow. Dev Psychobiol 1980, 13:499-506.
32. Fortune ES, Margoliash D: Cytoarchitectonic organization and morphology of cells of the field L complex in male zebra finches (taenopygia guttata). J Comp Neurol 1992, 325:388-404.
52. Nelson DA, Marler P: Innate recognition of song in whitecrowned sparrows: a role in selective vocal learning? Anim Behav 1993, 46:806-808.
33. Vates GE, Broome BM, Mello CV, Nottebohm F: Auditory pathways of caudal telencephalon and their relation to the song system of adult male zebra finches (taenopygia guttata). J Comp Neurol 1996, 366:613-642.
53. Mello C, Nottebohm F, Clayton D: Repeated exposure to one song leads to a rapid and persistent decline in an immediate early gene’s response to that song in zebra finch telencephalon. J Neurosci 1995, 15:6919-6925.
34. Jarvis ED: Learned birdsong and the neurobiology of human language. Ann N Y Acad Sci 2004, 1016:749-777.
54. Mello CV, Velho TA, Pinaud R: Song-induced gene expression: a window on song auditory processing and perception. Ann N Y Acad Sci 2004, 1016:263-281.
35. Woolley SM, Casseday JH: Response properties of single neurons in the zebra finch auditory midbrain: response patterns, frequency coding, intensity coding, and spike latencies. J Neurophysiol 2004, 91:136-151.
55. Bolhuis JJ, Hetebrij E, Den Boer-Visser AM, De Groot JH, Zijlstra GG: Localized immediate early gene expression related to the strength of song learning in socially reared zebra finches. Eur J Neurosci 2001, 13:2165-2170.
36. Hsu A, Woolley SM, Fremouw TE, Theunissen FE: Modulation power and phase spectrum of natural sounds enhance neural encoding performed by single auditory neurons. J Neurosci 2004, 24:9201-9211.
56. Cousillas H, Richard JP, Mathelier M, Henry L, George I, Hausberger M: Experience-dependent neuronal specialization and functional organization in the central auditory area of a songbird. Eur J Neurosci 2004, 19:3343-3352.
37. Woolley SM, Gill PR, Theunissen FE: Stimulus-dependent auditory tuning results in synchronous population coding of
57. Richard JP, Leppelsack HJ, Hausberger M: A rapid correlation method for the analysis of spectro-temporal
Current Opinion in Neurobiology 2006, 16:400–407
www.sciencedirect.com
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receptive fields of auditory neurons. J Neurosci Methods 1995, 61:99-103. 58. Theunissen FE, Sen K, Doupe AJ: Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J Neurosci 2000, 20:2315-2331. 59. Theunissen FE, David SV, Singh NC, Hsu A, Vinje W, Gallant JL: Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network 2001, 12:289-316. 60. Knudsen EI: Instructed learning in the auditory localization pathway of the barn owl. Nature 2002, 417:322-328. 61. Mooney R, Rosen MJ, Sturdy CB: A bird’s eye view: top down intracellular analyses of auditory selectivity for learned vocalizations. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2002, 188:879-895. 62. Lewicki MS: Efficient coding of natural sounds. Nat Neurosci 2002, 5:356-363. 63. Smith EC, Lewicki MS: Efficient auditory coding. Nature 2006, 439:978-982.
www.sciencedirect.com
64. Narayan R, Ergun A, Sen K: Delayed inhibition in cortical receptive fields and the discrimination of complex stimuli. J Neurophysiol 2005, 94:2970-2975. 65. DiMattina C, Wang X: Virtual vocalization stimuli for investigating neural representations of species-specific vocalizations. J Neurophysiol 2006, 95:1244-1262. 66. Medvedev AV, Kanwal JS: Local field potentials and spiking activity in the primary auditory cortex in response to social calls. J Neurophysiol 2004, 92:52-65. 67. Romanski LM, Averbeck BB, Diltz M: Neural representation of vocalizations in the primate ventrolateral prefrontal cortex. J Neurophysiol 2004, 93:734-747. 68. Gifford GW III, MacLean KA, Hauser MD, Cohen YE: The neurophysiology of functionally meaningful categories: macaque ventrolateral prefrontal cortex plays a critical role in spontaneous categorization of species-specific vocalizations. J Cogn Neurosci 2005, 17:1471-1482. 69. Griffiths TD, Warren JD, Scott SK, Nelken I, King AJ: Cortical processing of complex sound: a way forward? Trends Neurosci 2004, 27:181-185.
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by the avian auditory system. In keeping with the theme of vocal learning, researchers are investigating the role of the auditory system in making memories of a model or tutor song and in processing the bird’s own vocalizations during song production and learning [4,5]. In addition, researchers studying auditory processing of vocal sounds in both oscines and non-oscines have the independent goal of elucidating the neurobiology of purely perceptual tasks. Birds are particularly adept at recognizing conspecifics solely on the basis of their vocalizations, and often in very unfavorable acoustical environments [6,7,8]. How the auditory system accomplishes such complex auditory discrimination and auditory scene analysis tasks is an unresolved mystery in auditory science [9].
0959-4388/$ – see front matter # 2006 Elsevier Ltd. All rights reserved.
Recent studies have investigated the physiology of natural sound processing in the avian auditory system with an impressive array of experimental techniques: single unit recordings [10], awake behaving recordings [11], gene expression [12], auto-radiography [13] and functional magnetic resonance imaging (fMRI) [14]. The emerging picture from these studies is that first, the auditory system of songbirds shows some degree of specialization for processing natural sounds and vocalizations, second, the specialization for behaviorally relevant vocalizations can be learned and third, this specialization can be understood in terms of the joint spectral-temporal tuning properties of neuronal ensembles.
DOI 10.1016/j.conb.2006.07.003
Auditory behavior
Corresponding author: Theunissen, Fre´de´ric E. ([email protected])
Current Opinion in Neurobiology 2006, 16:400–407 This review comes from a themed issue on Sensory systems Edited by Yang Dan and Richard D Mooney Available online 13th July 2006
Introduction Animals rely on auditory processing for survival. Listening to others enables an animal to classify them as conspecific or heterospecific, neighbor or stranger, mate or non-mate, kin or non-kin. Listening serves another important function for birds of the suborder oscine, that is, the songbirds. Juvenile songbirds listen to adult conspecifics to form a memory of a normal song that they will use to guide their own song learning. Depending upon the songbird species, songbirds begin vocalizing either while the tutor memory is forming or once it is formed [1]. Nearly 50 years ago, researchers began to examine carefully the ‘song system’ of songbirds, a system of specialized brain structures found only in birds that learn to sing [2]. That system of structures has become a well-known model for studying the neural basis of vocal learning [3]. More recently, neuroscientists have moved beyond characterizing the song system and begun to study how complex sounds, including vocalizations, are processed Current Opinion in Neurobiology 2006, 16:400–407
Birds use song and other vocalizations for an array of communication tasks in the wild. Males use songs for territorial defense and mate attraction [15], and male– female pairs use songs for pair-bonding and cooperation [16,17]. Although only the males of most temperate songbird species sing, communication calls are produced by both male and female birds and are varied in function. Calls are used to maintain contact (contact call), restore contact (separation call), obtain food (begging call) or advertise danger (alarm call) [18]. The behavioral discrimination of conspecific and heterospecific calls and song has been well documented in the laboratory [8,19]. Within a species, songbirds use song to discriminate between neighbors and strangers, relatives and non-relatives, and mates and non-mates [20]. Very young male songbirds that have not yet started to sing show their preference for the familiar tutor song over unfamiliar song by spending more time next to speaker, and producing more calls in response to it. In addition to this early discrimination ability, song learning requires the capacity for longer-term storage of the tutor song [21]. www.sciencedirect.com
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Similar to male songbirds, female songbirds show familiarity-based discrimination among different individual songs [22], even after a relatively long period of time of not hearing the familiar song [23]. In fact, female discrimination on the basis of the quality of conspecific song appears to be similar or superior to that of males [24]. The discriminability for subtle features of vocalizations has also been demonstrated with physiological measures by evaluating the effect of sexy song features on hormonal levels in female songbirds [25,26]. During auditory perceptual tasks that are measured quantitatively in the laboratory, such as psycho-acoustical tasks of loudness threshold and discrimination, pitch discrimination, and temporal discrimination, birds show a performance that is similar or slightly worse to that of humans [27]. More importantly, songbirds excel in psycho-acoustical tasks that involve sounds that have some of the spectral and temporal qualities of their own vocalizations [28]. The computations performed on both simple and complex sounds (e.g. vocalizations) by the avian auditory system must, therefore, be similar to those performed by the human auditory system. Moreover, the specialization for processing and memorizing the complexities of song and using this memory for vocal learning make the avian
auditory system an excellent model system to study the neural mechanisms of speech processing and learning.
Anatomy Whereas the specialized brain circuit for song production and learning has evolved fairly recently in evolutionary time and in only a few avian orders [29], the avian auditory system is much older and shares features across all avian groups and other vertebrates, including mammals [30]. The most noticeable similarity rests in the number of auditory nuclei (or neural processing stages) and the pattern of feed-forward connections from the cochlear nucleus to the auditory forebrain. As illustrated in Figure 1, afferents from the inner ear project to the cochlear nucleus in the medulla. Similar to the situation in mammals, there is both a direct and an indirect route connecting the cochlear nucleus and the auditory midbrain. In the midbrain, these pathways converge in the dorsal lateral nucleus of the mesencephalon (MLd), which is analogous to the inferior colliculus (IC) in mammals. The auditory midbrain projects to ovoidalis (Ov), a relay nucleus in the thalamus, just as the IC projects to the medial geniculate body (MGB) in mammals. Ovoidalis, in turn, sends projections to the primary auditory area in the pallium, called field L [31]. Principally for this reason, the
Figure 1
Anatomical and functional cartoons of the avian auditory system shown in saggital section. The anatomical cartoon shows the auditory nuclei and NCM regions (fields L and CM) in grey in addition to two of the song system nuclei (CN, SO, LL, MLd and Ov) that would be found in songbirds (HVC and RA). The feed-forward pathways are shown in solid and the feedback pathway is shown with a dotted line. Note that not all the pathways are shown. For example, the reciprocal pathway between NCM and CM has been omitted for clarity. The diagram is also only approximately anatomically correct: NCM would be found in a more medial region than indicated here. The functional cartoon summarizes and simplifies some of the findings described in the text. Mld is tuned for low-level statistics of natural sounds and its population response efficiently represents the temporal changes in the amplitude envelope of the sound. The primary auditory forebrain (field L) also efficiently represents sounds with natural statistics and its heterogenous neurons code complex temporal and spectral acoustical features. The secondary auditory areas (NCM and CM) are sensitive to higher order acoustic features of vocalizations. The neural response in these areas is also affected by the sounds that the animal has learned and remembered. www.sciencedirect.com
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avian field L can be thought as analogous to the mammalian primary auditory cortex (A1). Field L has been further divided into subregions (L1, L2a, L2b, L3, L) on the basis of differences in cytoarchitecture and connectivity [32,33]. Input from the auditory thalamus goes to subregions L2a and L2b, which in turn project to L1 and L3. Subregions L1 and L3 make bi-directional connections with two secondary auditory areas in the pallium: the nidopallium caudal medial (NCM) and the caudal mesopallium (CM). Although analogies at this level might be unnecessary and premature given our state of knowledge, it is tempting to make a link between L2 and layer 4 of A1, with L1 and L3 corresponding to the supragranular layers of the cortex. NCM and CM could also be compared to the superficial layers of auditory cortex or to secondary mammalian auditory regions, such as the lateral belt in primates or Wernicke’s area in humans [34]. Only further neurophysiological work in mammals and birds will tell us whether such comparisons are relevant.
Neurophysiology Neurophysiologists have recorded responses to vocalizations and other complex sounds throughout most of the auditory system shown in Figure 1. The results can be organized along three directions of analysis: first, the selectivity of the responses for vocalizations relative to other complex sounds; second, the effect of short term experience, learning and development on the responses; and third, the more quantitative description of the tuning of single neurons for specific sound features that could explain the selectivity observed for vocalizations. This third line of analysis makes the bridge between a neuroethological study of auditory responses that provides the link to behavior and a more classical auditory analysis of the tuning response along well defined acoustic parameters that provides insight on the underlying computations (Mainen, this issue). Neural selectivity for vocalizations Dorsal lateral nucleus of the mesencephalon
Auditory neurons in MLd respond robustly to pure tones, complex tones and songs [35,36]. However, stimuli that contain the spectro-temporal features found in natural sounds elicited a richer ensemble of spike patterns from MLd neurons than simple synthetic sounds did. Information theoretic calculations show that these spike patterns led to higher measures of neural discriminability for natural sounds relative to synthetic sounds [36]. Neuronal responses to simple synthetic sounds show tuning for fast temporal modulations and exhibit precise spike timing [35]. This suggested that these properties might be beneficial for encoding the fast changes that are characteristic of the amplitude envelopes of song. Woolley et al. [37] validated this hypothesis with a recent analysis of the ensemble response to song: the Current Opinion in Neurobiology 2006, 16:400–407
population response encodes the temporal changes of song very accurately. Field L
Selectivity for natural sounds was tested in early work in the primary avian auditory forebrain, field L, and the neighboring secondary auditory areas NCM and CM [38,39]. The results from those initial characterizations showed that a subset of neurons in field L did not produce robust responses to simple sounds such as tones or noise but, instead, responded selectively to specific vocalizations. These selective neurons were found in greater numbers outside the thalamorecipient subregion L2 [40]. In many cases the selectivity for vocalizations could be explained in terms of selectivity for specific spectrotemporal acoustical features of the vocalizations [40,41]. The issue of the scope of the selectivity was revisited in more detail recently by analyzing the statistics of the spectro-temporal structure found in song [42] and generating synthetic sounds that preserved these statistics to various degrees [36,43]. It was found that, on average, neurons in field L show stronger responses to conspecific song than to sequences of tones with the same spectral distribution or synthetic harmonic sounds that had the same pitch as the harmonic sounds found in song [43]. By contrast, the neural responses to synthetic stimuli that preserved the joint spectral-temporal statistics of song were similar in strength to those of the natural songs. In particular, modulation-limited noise, a form of white-noise for which the fluctuations in the spectro-temporal envelope are limited to lower frequencies, such as those found in natural sounds, is able to drive auditory neurons robustly at all stages of the auditory system [36]. Modulation-limited noise has intensity fluctuations in time and frequency that give it a tempo and pitch that is easily perceived, whereas these percepts are absent in white noise. Nonetheless, the patterns in spike train responses showed a larger information capacity for song stimuli than they did in response to the matched synthetic sounds: even with the same number of spikes, the neural responses to songs could be used to discriminate among different songs with better results than one would obtain for different segments of synthetic sound [36]. To summarize, selectivity for conspecific sounds is present in avian primary auditory forebrain but restricted to the relatively low level statistics of the spectral-temporal acoustical structure of vocalizations and of other natural sounds. The efficient representation of such sounds might constitute the building blocks for further computations and more selective representations in other auditory regions. Caudal mesopallium
The selectivity for natural sounds in secondary auditory forebrain regions NCM and CM has also been examined. www.sciencedirect.com
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Neurophysiological recordings with complex stimuli showed that the selectivity in CM based on mean spike rate was not different from that found in field L [43]. However, when the response to tones is compared to the response to vocalizations, the number of selective units is greater in L2 than in CM or in the auditory nidopallium excluding L2 (i.e. L1, L3 and NCM) [40]. Furthermore, information theoretic analyses suggest some degree of hierarchical processing, with CM neurons being more sensitive to the natural order in time and frequency of naturally occurring spectro-temporal acoustical features than field L neurons are [36]. Evidence for hierarchical processing between L and CM can also be demonstrated by the degree to which linear models of stimulus– response functions can explain the response of CM neurons to song [44,45]. In female songbirds, electrolytic lesions in CM eliminated preference for conspecific song over heterospecific songs, as assessed by observing the number of sexual displays [46].
with NCM [50]. A direct comparison using vocalizations in NCM and all the subareas of field L is still needed to better quantify the differences in selectivity between these areas.
Nidopallium caudal medial
Learned responses and development
Neurophysiological responses to sounds in the avian auditory system have also been assessed by measuring the level of mRNA of immediate early genes expressed in neurons, in particular the gene zenk (also known as Zif268). In these experiments, a particular bird hears a single sound repeatedly and is then rapidly sacrificed to assess the level of gene induction. By comparing genetic responses across birds and sound types the neural selectivity for particular sounds can be assessed indirectly. In female songbirds, zenk expression is correlated with the presence of features of conspecific songs that are also preferred in mate selection [47]. In male songbirds, zenk expression is higher for conspecific song than for heterospecific song, whereas no expression is seen in response to tone bursts [48]. Synthetic stimuli with more natural spectro-temporal features have also been used in gene expression experiments and yielded interesting results. Whereas whistle sounds of different pitches taken from the canary song elicited clustered and topographically organized (and, thus, differentiable) patterns of gene expression, synthetic whistle or guitar notes at the same pitch elicited more distributed (and putatively less informative) patterns of activation [12]. Changes in the blood oxygenation level dependent (BOLD) response measured with fMRI showed that NCM has a differential response for song versus noise that is not observed in field L [14]. All these results suggest that unlike field L, NCM shows selectivity for higher level attributes of conspecific song.
Vocal sound recognition in birds involves both innate and learned processing. For example, as demonstrated by measuring their heart rate, young birds recognize conspecific song [51] or begging calls [52]. However, song learning, neighbor recognition and mate recognition are examples of behaviors that require the learned recognition of particular songs. Short-term plasticity in adult birds has been measured in the avian auditory system in both NCM and CM, whereas longer term developmental changes have been observed in the primary auditory field.
Neurophysiological studies in NCM also suggest selectivity for complex sounds [49], which might be absent in subarea L2a [40]. When simple tones are used to characterize the response properties of neurons in both L2 and NCM, one observes tighter frequency tuning and less or no habituation to repeated stimulation in L2 as compared www.sciencedirect.com
To summarize, the two secondary auditory areas CM and NCM show signs of selectivity in their electrophysiological responses for complex sounds. Nevertheless, what particular features are being selected for and what the degree of selectivity relative to other forebrain auditory areas is remain open questions. It is also indicated that the nature of the responses is different in these secondary areas: linearity of neural response is decreased relative to that in field L and gene expression is observed, whereas it is not observed in L2. The functional significance of these physiological differences is not yet clear. One potentially important difference might be the role of these secondary areas in learned auditory discriminations, as discussed in the next section.
The first evidence of plastic mechanisms in the auditory system of adult birds came from IEG expression studies in NCM. In these brain areas, the zenk response decreases to the repeated presentation of the same conspecific song but recovers completely upon the presentation of novel song. The decrease in zenk expression for a particular song persists, as it can be observed the following day [53]. Neurophysiological studies support the IEG results: repeated stimulation with the same song results in stimulus-dependent adaptation [49]. Because of these results and because zenk has been linked to synaptic plasticity in mammals, it has been proposed that the decrease in zenk expression to familiar songs is a marker of the learning process for familiar songs [54]. Recent studies have also implicated NCM in the longer term storage of the tutor song. Bolhuis and co-workers found that the expression of the gene c-fos was greater for the tutor song than for unfamiliar conspecific song and that the strength of the response was correlated with the degree of learning [55]. In a similar neurophysiological study, Vicario and co-workers compared the adaptation in the responses to tutor song with those observed in response to unfamiliar conspecific song or the bird’s own song. The different adaptation curves show that Current Opinion in Neurobiology 2006, 16:400–407
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the tutor song was familiar although it had not been heard for a prolonged period of time. The degree of familiarity was correlated with how well the bird copied the song [4]. It should be noted, however, that in both these studies the birds were also exposed repeatedly to their own song which resembled the tutor song and, indeed, it was shown that this vocal experience was important. More studies are, therefore, needed to address this potential confound and to understand how an adaptive response could be used as a template in vocal learning. Shorter-term plasticity has also been measured in neurophysiological recordings in CM. After training birds to recognize individual conspecific songs in either a twoalternative forced choice (AFC) or a go no-go paradigm, neurons in CM became selective as a population for the songs used in the experiment. Interestingly, the two songs in the AFC experiment elicited similar enhanced responses, whereas in the go no-go experiment, the response to the go song was enhanced relative to the response to the no-go song. Therefore, this learned selectivity cannot simply be explained by familiarity but must also involve top down processing that assigns different values to similarly familiar sounds [11]. The avian auditory system also has great potential as model to study the effect of experience on neural development. This line of research has just recently been initiated. The first study on this subject recorded significant changes in the response of field L neurons of birds that were deprived of normal acoustical experience during early development; both the selectivity and the organization of the frequency tuning properties were altered [56]. A natural acoustical environment during development might, therefore, be needed not only for complex behaviors such as song learning but also for simpler perceptual tasks such as pitch discrimination. Spectro-temporal tuning and representation of vocalizations
The neurophysiological experiments described above constitute substantial proof of both the selectivity and the plasticity of the avian auditory system. In addition, the demonstrated selectivity and learning can be directly related to the processing of behaviorally relevant vocalizations. However, these studies tell us little about the actual underlying neural mechanisms. For example, the actual neural representation and computations occurring at each stage were not directly addressed. To accomplish this, one needs to first characterize the stimulus–response function of individual neurons and then relate that function to the observed selectivity, or to use the functional description to track plasticity.
vocalizations [57,58]. In order to obtain unbiased stimulus–response functions given that only a subset of sounds is presented in any one set of experiments, regression techniques of normalization and regularization must be used [37,59]. In this manner, we were able to obtain the spectro-temporal receptive fields (STRFs) of single neurons and measure changes in the responses to different stimulus ensembles. The STRFs of single neurons in MLd, field L and CM constitute a heterogenous and complex set [10,37,41,44]. However, in all three brain regions the population response is tuned for the spectrotemporal modulations that are particularly informative in natural sounds [10]. This tuning can, in part, explain the selectivity for natural sounds found in field L and CM, because simple synthetic sounds such as tones or white noise do not contain the spectral and temporal structure that preferentially excites the neurons. Moreover, the tuning obtained from the STRFs also yields significantly different spike train patterns to different songs and, consequently, a high level of neural discrimination [10]. This prediction is consistent with the information-theoretic analysis of the neural response to different sounds described above. It should be noted, however, that the STRF only explains a fraction of the neural response and that non-linear functions between the sound and the neural response will have to be characterized to completely describe the stimulus–response relationships of the neurons. These non-linear responses also play a role in the selectivity for natural sounds [10], and this role could be crucial in higher auditory areas such as CM. The STRF methodology has also enabled the observation of tuning changes in neurons in MLd while processing song and modulation-limited noise stimuli. On average, the STRFs when processing song were tighter in time and more spread out in frequency than those to modulationlimited noise stimuli, indicating that the neurons became less sensitive to spectral features and more sensitive to temporal features. Moreover, the STRFs of different neurons are more similar to each other while processing song, resulting in a synchronization of the neural response across cells. This synchronized response enables a very accurate population code for the temporal features of the song [37]. Beyond the population code, it is clear that different subsets of neurons can be classified according to their STRFs, and that these subsets can subserve different auditory computations. There is also some initial evidence that in field L these functional groupings have an anatomical correlate, which could facilitate the study of the microcircuitry within and across auditory areas [41].
Conclusions and goals for the future In the avian auditory system, stimulus–response functions of single auditory neurons have been estimated directly from neural data obtained in response to Current Opinion in Neurobiology 2006, 16:400–407
There is no doubt that the study of the avian auditory system has already been successful. As a consequence of the strong ties to vocal behavior and the use of many www.sciencedirect.com
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different techniques from researchers with a breadth of expertise, the demonstration that the avian auditory system can be used to understand how vocalizations are processed and learned has already been made and, in our opinion, rivals or surpasses the demonstrations of similar properties in the mammalian auditory system. However, there is still much work to be done. As an overarching objective, avian auditory researchers need to go beyond demonstrations and into explanations. Can the neural selectivity be understood in terms of specific computations? How are these computations achieved through both the macro and the micro circuitry of the auditory system? What is the relationship between the slope of adaptation of zenk expression and the neural responses to tutor song, and how can these responses be used to guide vocal learning? What are the mechanisms of plasticity at the cellular or circuit level? Can we define and understand the role of functional groups within particular auditory regions? What is the relationship between these functional groups and the microcircuitry? These are just a few examples of important questions that we need to address. As a role model, we can use another well known avian auditory model: the barn owl hindbrain and midbrain auditory circuits for sound localization, in which a significant level of understanding has been achieved [60]. For this ambitious goal, we make a few suggestions. First, we need to be able to better integrate the different methodologies that are used to probe the physiology. The relationships among gene expression, BOLD responses in fMRI and electrophysiology in awake and anesthetized preparations need to be examined and understood. Second, using intracellular recordings the microcircuitry needs to be investigated and correlated to the different types of functional descriptions that have been found. Such level of analysis has already been performed in the song system with great success [61]. Third, because the processing of vocalizations is a computationally complex task, the use of modeling and theory will enable one to understand what computations are required and/or the potential significance of certain types of computations that are discovered [42,62–64]. Finally, we also believe that the findings in the avian auditory system will generalize to other animals including humans. We, therefore, encourage greater exchanges and collaborative efforts with auditory researchers in other systems, such as the marmoset [65], bat [66], macaque [67,68] and human [69]. The processing of vocal sounds by the brain is a complex task and much will be learned from both birds and the comparative approach.
References and recommended reading Papers of particular interest, published within the annual period of review, have been highlighted as: of special interest of outstanding interest 1.
Marler P: A comparative approach to vocal learning: song development in white-crowned sparrows. J Comp Physiol Psychol 1970, 71:1-25.
2.
Kroodsma DE, Konishi M: A suboscine bird (eastern phoebe, sayornis phoebe) develops normal song without auditory feedback. Anim Behav 1991, 42:477-487.
3.
Zeigler HP, Marler P: Behavioral Neurobiology of Birdsong. In Annals of the New York Academy of Sciences Volume 1016. Edited .by Zeigler HP, Marler P. The New York Academy of Sciences; 2002
4.
Phan ML, Pytte CL, Vicario DS: Early auditory experience generates long-lasting memories that may subserve vocal learning in songbirds. Proc Natl Acad Sci USA 2006, 103:1088-1093. Where is the tutor song stored in the songbird brain? The adaptation curve of the neurophysiological response to the tutor song suggests that this song is treated as familiar in NCM. The correlation of the strength of this effect and the quality of song learning suggests that NCM could play a role in the memory storage of the tutor song. 5.
Theunissen FE, Amin N, Shaevitz SS, Woolley SM, Fremouw T, Hauber ME: Song selectivity in the song system and in the auditory forebrain. Ann N Y Acad Sci 2004, 1016:222-245.
6.
Aubin T, Jouventin P, Hildebrand C: Penguins use the two-voice system to recognize each other. Proc Biol Sci 2000, 267:1081-1087.
7.
Leonard ML, Horn AG: Ambient noise and the design of begging signals. Proc Biol Sci 2005, 272:651-656.
8.
Appeltants D, Gentner TQ, Hulse SH, Balthazart J, Ball GF: The effect of auditory distractors on song discrimination in male canaries (serinus canaria). Behav Processes 2005, 69:331-341. Birds’ recognition abilities based on acoustical cues appear impressive on the basis of field observations and experiments. In this study, an auditory scene analysis task is performed in the laboratory and the discriminability is quantified as a function of the level and type of auditory distractor. The combination of behavioral experiments such as these and various physiological measurements is one of the strengths of the avian auditory system as a model system. 9.
Bergman A: Auditory Scene Analysis. MIT Press; 1990.
10. Woolley SM, Fremouw TE, Hsu A, Theunissen FE: Tuning for spectro-temporal modulations as a mechanism for auditory discrimination of natural sounds. Nat Neurosci 2005, 8:1371-1379. The authors present the first quantitative comparison between neural tuning and the statistics of natural sounds. The authors show that ensemble neural tuning emphasizes the most informative features of natural sounds. 11. Gentner TQ, Margoliash D: Neuronal populations and single cells representing learned auditory objects. Nature 2003, 424:669-674. 12. Ribeiro S, Cecchi GA, Magnasco MO, Mello CV: Toward a song code: evidence for a syllabic representation in the canary brain. Neuron 1998, 21:359-371. 13. Adret P: In search of the song template. Ann N Y Acad Sci 2004, 1016:303-324. 14. Van Meir V, Boumans T, De Groof G, Van Audekerke J, Smolders A, Scheunders P, Sijbers J, Verhoye M, Balthazart J, Van der Linden A: Spatiotemporal properties of the BOLD response in the songbirds’ auditory circuit during a variety of listening tasks. Neuroimage 2005, 25:1242-1255. 15. Catchpole CK, Slater PBJ: Bird Song. Biological Themes and Variations. Cambridge University Press; 1995.
Acknowledgements The work described in this review was funded by grants MH-059189 and DC-007293 from the National Institutes of Health to F Theunissen. www.sciencedirect.com
16. Marshall-Bal L, Slater PJ: Duet singing and repertoire use in threat signalling of individuals and pairs. Proc Biol Sci 2004, 271:S440-S443. Current Opinion in Neurobiology 2006, 16:400–407
406 Sensory systems
17. Hile AG, Plummer TK, Striedter GF: Male vocal imitation produces call convergence during pair bonding in budgerigars, melopsittacus undulatus. Anim Behav 2000, 59:1209-1218. 18. Marler P: Bird calls: their potential for behavioral neurobiology. Ann N Y Acad Sci 2004, 1016:31-44. 19. Dooling RJ, Brown SD, Klump GM, Okanoya K: Auditory perception of conspecific and heterospecific vocalizations in birds: evidence for special processes. J Comp Psychol 1992, 106:20-28. 20. Sherman P, Reeve H, Pfennig D: Recognition systems. In Behavioral Ecology. Edited by Krebs J, Davies NB. Blackwell Science; 1997. 21. Marler P, Peters S: Long-term storage of learned birdsong prior to production. Anim Behav 1982, 30:479-482.
vocalizations in the songbird midbrain. J Neurosci 2006, 26:2499-2512. 38. Leppelsack HJ, Vogt M: Responses of auditory neurons in the forebrain of a songbird to stimulation with species-specific sounds. J Comp Neurol 1976, 107:263-274. 39. Bonke D, Scheich H, Langner G: Responsiveness of units in the auditory neostriatum of the guinea fowl (numida meleagris) to species-specific calls and synthetic stimuli I. Tonotopy and functional zones of field L. J Comp Physiol 1979, 132:243-255. 40. Muller CM, Leppelsack HJ: Feature extraction and tonotopic organization in the avian auditory forebrain. Exp Brain Res 1985, 59:587-599. 41. Cousillas H, Leppelsack HJ, Leppelsack E, Richard JP, Mathelier M, Hausberger M: Functional organization of the forebrain auditory centres of the european starling: a study based on natural sounds. Hear Res 2005, 207:10-21.
22. Riebel K, Smallegange IM, Terpstra NJ, Bolhuis JJ: Sexual equality in zebra finch song preference: evidence for a dissociation between song recognition and production learning. Proc R Soc Lond B Biol Sci 2002, 269:729-733.
42. Singh NC, Theunissen FE: Modulation spectra of natural sounds and ethological theories of auditory processing. J Acoust Soc Am 2003, 114:3394-3411.
23. Miller D: Long term recognition of father’s song by female zebra finches (taeniopygia guttata). Nature 1979, 280:389-391.
43. Grace JA, Amin N, Singh NC, Theunissen FE: Selectivity for conspecific song in the zebra finch auditory forebrain. J Neurophysiol 2003, 89:472-487.
24. Searcy WA, Brenowitz EA: Sexual differences in species recognition of avian song. Nat 1988, 332:152-154.
44. Sen K, Theunissen FE, Doupe AJ: Feature analysis of natural sounds in the songbird auditory forebrain. J Neurophysiol 2001, 86:1445-1458.
25. Gil D, Leboucher G, Lacroix A, Cue R, Kreutzer M: Female canaries produce eggs with greater amounts of testosterone when exposed to preferred male song. Horm Behav 2004, 45:64-70. 26. Marshall RC, Leisler B, Catchpole CK, Schwabl H: Male song quality affects circulating but not yolk steroid concentrations in female canaries (serinus canaria). J Exp Biol 2005, 208:4593-4598. 27. Dooling RJ: Auditory perception in birds. In Acoustic Communication in Birds vol 1. Edited by Edited by Kroodsma DE, Miller EH. Academic Press; 1982:95-130. 28. Lohr B, Dooling RJ: Detection of changes in timbre and harmonicity in complex sounds by zebra finches (taeniopygia guttata) and budgerigars (melopsittacus undulatus). J Comp Psychol 1998, 112:36-47. 29. Jarvis ED, Ribeiro S, da Silva ML, Ventura D, Vielliard J, Mello CV: Behaviourally driven gene expression reveals song nuclei in hummingbird brain. Nature 2000, 406:628-632.
45. Gill PR, Zhang J, Woolley SM, Fremouw T, Theunissen FE: Sound representation methods for spectro-temporal receptive field estimation. Journal of Computational Neurosciences 2006. In press. 46. MacDougall-Shackleton SA, Hulse SH, Ball GF: Neural bases of song preferences in female zebra finches (taeniopygia guttata). Neuroreport 1998, 9:3047-3052. 47. Gentner TQ, Hulse SH, Duffy D, Ball GF: Response biases in auditory forebrain regions of female songbirds following exposure to sexually relevant variation in male song. J Neurobiol 2001, 46:48-58. 48. Mello CV, Vicario DS, Clayton DF: Song presentation induces gene expression in the songbird forebrain. Proc Natl Acad Sci USA 1992, 89:6818-6822. 49. Stripling R, Volman S, Clayton D: Response modulation in the zebra finch caudal neostriatum: relationship to nuclear gene regulation. J Neurosci 1997, 17:3883-3893.
30. Butler AB, Hodos W: Comparative Vertebrate Neuroanatomy: Evolution and Adaptation. Wiley-Liss; 1996.
50. Terleph TA, Mello CV, Vicario DS: Auditory topography and temporal response dynamics of canary caudal telencephalon. J Neurobiol 2006, 66:281-292.
31. Zaretsky MD, Konishi M: Tonotopic organization in the avian telencephalon. Brain Res 1976, 111:167-171.
51. Dooling R, Searcy M: Early perceptual selectivity in the swamp sparrow. Dev Psychobiol 1980, 13:499-506.
32. Fortune ES, Margoliash D: Cytoarchitectonic organization and morphology of cells of the field L complex in male zebra finches (taenopygia guttata). J Comp Neurol 1992, 325:388-404.
52. Nelson DA, Marler P: Innate recognition of song in whitecrowned sparrows: a role in selective vocal learning? Anim Behav 1993, 46:806-808.
33. Vates GE, Broome BM, Mello CV, Nottebohm F: Auditory pathways of caudal telencephalon and their relation to the song system of adult male zebra finches (taenopygia guttata). J Comp Neurol 1996, 366:613-642.
53. Mello C, Nottebohm F, Clayton D: Repeated exposure to one song leads to a rapid and persistent decline in an immediate early gene’s response to that song in zebra finch telencephalon. J Neurosci 1995, 15:6919-6925.
34. Jarvis ED: Learned birdsong and the neurobiology of human language. Ann N Y Acad Sci 2004, 1016:749-777.
54. Mello CV, Velho TA, Pinaud R: Song-induced gene expression: a window on song auditory processing and perception. Ann N Y Acad Sci 2004, 1016:263-281.
35. Woolley SM, Casseday JH: Response properties of single neurons in the zebra finch auditory midbrain: response patterns, frequency coding, intensity coding, and spike latencies. J Neurophysiol 2004, 91:136-151.
55. Bolhuis JJ, Hetebrij E, Den Boer-Visser AM, De Groot JH, Zijlstra GG: Localized immediate early gene expression related to the strength of song learning in socially reared zebra finches. Eur J Neurosci 2001, 13:2165-2170.
36. Hsu A, Woolley SM, Fremouw TE, Theunissen FE: Modulation power and phase spectrum of natural sounds enhance neural encoding performed by single auditory neurons. J Neurosci 2004, 24:9201-9211.
56. Cousillas H, Richard JP, Mathelier M, Henry L, George I, Hausberger M: Experience-dependent neuronal specialization and functional organization in the central auditory area of a songbird. Eur J Neurosci 2004, 19:3343-3352.
37. Woolley SM, Gill PR, Theunissen FE: Stimulus-dependent auditory tuning results in synchronous population coding of
57. Richard JP, Leppelsack HJ, Hausberger M: A rapid correlation method for the analysis of spectro-temporal
Current Opinion in Neurobiology 2006, 16:400–407
www.sciencedirect.com
Auditory processing of vocal sounds in birds Theunissen and Shaevitz 407
receptive fields of auditory neurons. J Neurosci Methods 1995, 61:99-103. 58. Theunissen FE, Sen K, Doupe AJ: Spectral-temporal receptive fields of nonlinear auditory neurons obtained using natural sounds. J Neurosci 2000, 20:2315-2331. 59. Theunissen FE, David SV, Singh NC, Hsu A, Vinje W, Gallant JL: Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network 2001, 12:289-316. 60. Knudsen EI: Instructed learning in the auditory localization pathway of the barn owl. Nature 2002, 417:322-328. 61. Mooney R, Rosen MJ, Sturdy CB: A bird’s eye view: top down intracellular analyses of auditory selectivity for learned vocalizations. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2002, 188:879-895. 62. Lewicki MS: Efficient coding of natural sounds. Nat Neurosci 2002, 5:356-363. 63. Smith EC, Lewicki MS: Efficient auditory coding. Nature 2006, 439:978-982.
www.sciencedirect.com
64. Narayan R, Ergun A, Sen K: Delayed inhibition in cortical receptive fields and the discrimination of complex stimuli. J Neurophysiol 2005, 94:2970-2975. 65. DiMattina C, Wang X: Virtual vocalization stimuli for investigating neural representations of species-specific vocalizations. J Neurophysiol 2006, 95:1244-1262. 66. Medvedev AV, Kanwal JS: Local field potentials and spiking activity in the primary auditory cortex in response to social calls. J Neurophysiol 2004, 92:52-65. 67. Romanski LM, Averbeck BB, Diltz M: Neural representation of vocalizations in the primate ventrolateral prefrontal cortex. J Neurophysiol 2004, 93:734-747. 68. Gifford GW III, MacLean KA, Hauser MD, Cohen YE: The neurophysiology of functionally meaningful categories: macaque ventrolateral prefrontal cortex plays a critical role in spontaneous categorization of species-specific vocalizations. J Cogn Neurosci 2005, 17:1471-1482. 69. Griffiths TD, Warren JD, Scott SK, Nelken I, King AJ: Cortical processing of complex sound: a way forward? Trends Neurosci 2004, 27:181-185.
Current Opinion in Neurobiology 2006, 16:400–407