Most new skills, from playing a sport to learning a language, are acquired through a gradual process of trial and error. While some of this learning is driven by direct external rewards, such as praise, much of it occurs when the individual compares their current performance with their own impression of what a ‘correct’ performance should be. The way that the brain responds to external rewards is relatively well understood, but much less is known about the processes used by the brain to evaluate its own performance.

One way to study this process is to examine how songbirds learn their songs. While in the nest, young male birds memorize another bird's song, usually that of their father. They learn to sing by comparing their own vocals with this memorized template, tweaking their song until the two versions match. Now, Mandelblat-Cerf et al. have identified a pathway in the brain that enables the birds to make this comparison and to use any discrepancies to improve their subsequent attempts.

Anatomical labeling experiments revealed that a brain structure called the arcopallium has a key role in this process. The ventral part of this structure (known as AIV) receives inputs from the auditory cortex—meaning that it has access to the bird’s own song—and then forms connections with a specific group of neurons in the midbrain. These midbrain neurons, which communicate using the chemical transmitter dopamine, project to brain regions that ultimately control the movements involved in singing. This means that the AIV is ideally positioned to be able to evaluate and then adjust the song as required.

Consistent with this possibility, young zebra finches were less able to imitate a template song if their AIV was destroyed before they had started practicing. By contrast, destroying the AIV in adult birds who had already learned their song did not impair performance, indicating that the arcopallium circuit supports song learning rather than singing per se. Finally, recordings of neurons in the AIV made during singing revealed that this brain area sends signals about discrepancies between what the young bird tries to sing and what he hears himself sing.

In addition to providing further clues as to how the songbirds learn their songs, this work also highlights the fact that dopaminergic neurons in the midbrain—which are best known for being involved in our response to external rewards such as food and drugs—also contribute to learning that is driven internally.

Most human behaviors, such as speech, music, or athletic performance, are learned through a gradual process of trial and error. In all of these behaviors, the motor actions are shaped during learning by an internal model of good performance (Wolpert et al., 1995). While a great deal has been learned about the neural mechanisms by which external rewards, such as food or juice drops, are represented in the brain and might shape future behavior (Schultz et al., 1997; Hikosaka, 2007), little is known about how the brain evaluates its own performance, where such self-evaluation is computed and how these signals might shape future behavior.

Here we use the songbird as a model system to understand how complex behaviors can be learned by self-evaluation of motor performance, rather than relying on explicit external rewards such as food or social reinforcement. Songbirds learn their vocalizations by storing a memory, or ‘template’, of their tutor's song (Doupe and Kuhl, 1999). By listening to themselves sing and comparing their own song to this template (Konishi, 1965), they gradually converge to what can be a nearly exact copy of the tutor's song. Vocal learning requires a basal-ganglia forebrain circuit called the anterior forebrain pathway (AFP), the output of which projects to cortical premotor vocal areas (Nottebohm et al., 1976; Bottjer et al., 1984; Scharff and Nottebohm, 1991). The AFP is crucial for driving learned changes in song and shaping plasticity in the song motor pathway (Brainard and Doupe, 2000; Andalman and Fee, 2009; Warren et al., 2011). A key component of this vocal learning circuit is Area X, a basal ganglia homologue containing both striatal and pallidal components (Person et al., 2008).

Of critical importance in understanding the mechanisms of vocal sensorimotor learning in the songbird is to determine where song is evaluated and how the resulting evaluation signals are transmitted to the AFP to shape learning. Some hypotheses have emphasized the possible role of HVC (used as a proper name), a premotor cortical circuit that receives auditory inputs and also projects to Area X (Troyer and Doupe, 2000; Mooney, 2006; Prather et al., 2008; Roberts et al., 2012). While the involvement of HVC in evaluating ongoing song remains an open question, several lines of evidence suggest that HVC does not transmit error-related signals to the AFP (Hessler and Doupe, 1999; Kozhevnikov and Fee, 2007), or receive auditory inputs (Hamaguchi et al., 2014) during singing.

Another possibility is suggested by the large projection to Area X from dopaminergic nuclei in the midbrain—the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) (Person et al., 2008; Figure 1A). Inspired by the recently hypothesized role of dopaminergic signaling in reinforcement learning (Houk et al., 1994; Schultz et al., 1997; Bayer and Glimcher, 2005; Tsai et al., 2009), we and others have proposed that this dopaminergic input to Area X may play a role in song learning by signaling vocal errors (Hara et al., 2007; Gale et al., 2008; Gale and Perkel, 2010; Fee and Goldberg, 2011). Notably, a recent anatomical study in songbirds has described a projection to the dopaminergic midbrain from the arcopallium (Gale et al., 2008), an avian cortical region containing subtelencephalically projecting neurons analogous to those in deep layer-5 of mammalian cortex (Dugas-Ford et al., 2012; Karten, 2013).

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Here we set out to examine the role of the cortical area projecting to VTA/SNc in song learning. First, we characterized the spatial extent of the arcopallial neurons projecting to VTA or SNc, and characterized the inputs to these neurons from auditory cortical fields. We then carried out lesions targeted to this area to examine the effect on tutor imitation and on vocal plasticity that occurs after deafening. Finally, we recorded from VTA/SNc-projecting neurons in the arcopallium of singing birds and observed fast transient responses to disruptive auditory feedback during singing. Altogether, our findings suggest that descending auditory cortical projections may play a role in song evaluation via projections to the dopaminergic midbrain and/or other subcortical targets.

Motivated by the hypothesized role of dopaminergic signaling in reinforcement learning (Houk et al., 1994; Schultz et al., 1997; Bayer and Glimcher, 2005; Tsai et al., 2009), we set out to examine the role in vocal learning of a recently discovered songbird cortical area (Gale et al., 2008) that projects to VTA and SNc. We have characterized the spatial extent of neurons within the intermediate arcopallium that project to VTA and SNc, and refer to the region of arcopallium retrogradely labeled from these midbrain dopaminergic areas as AIV. Using a combination of anatomical and electrophysiological techniques, we have elucidated the afferent inputs to AIV neurons from other cortical areas. We examined the effect on vocal learning of lesions targeted to AIV, and carried out electrophysiological recordings of AIV neurons. Our findings are broadly consistent with the hypothesis that an arcopallial region with descending auditory cortical projections, both to the dopaminergic midbrain and to midbrain and brainstem auditory centers, plays a role in vocal learning.

The area we have identified as AIV is adjacent to other structures that have been hypothesized to play a role in vocal learning (Bottjer and Altenau, 2010). The dorsal arcopallium (also referred to as the lateral intermediate arcopallium, LAI, by Jarvis et al., 2013), receives inputs from the nidopallium adjacent to LMAN (LMAN-shell), and projects to the basal ganglia, motor thalamus, tectum, and the reticular formation (Bottjer et al., 2000). Our anatomical findings suggest that AIV, while adjacent to Ad, is part of a distinct, largely non-overlapping, anatomical circuit. In contrast to Ad, AIV does not receive a projection from LMAN-shell; rather it receives inputs from auditory cortical areas (CM, L1, and HVC-shelf) and caudal nidopallium. Furthermore, our findings show that AIV, but not Ad, projects to VTA/SNc. Given its anatomical overlap with RAcup, parts of AIV also likely project to MLD or to the shell of the thalamic nucleus ovoidalis (Mello et al., 1998).

Earlier studies have presented conflicting views on the function of Ad/LAI, particularly in regard to its role in vocal learning (Bottjer et al., 2000; Achiro and Bottjer, 2013). Bottjer and Altenau (2010) describe evidence that lesions of Ad produce deficits in vocal learning, not unlike those we report here for lesions of AIV. In contrast, Feenders et al. showed immediate early gene activation in Ad/LAI and in LMAN-shell (which projects to Ad/LAI) during locomotory activity (i.e., hopping), but find no evidence for activation in these areas during singing. This led them to suggest that Ad/LAI and RA form the output of a general avian cortical motor circuit, of which RA is a component specialized for vocal production (Feenders et al., 2008).

Our findings are consistent with the view that AIV, but not Ad, play a role in vocal motor learning. We found that lesions of AIV caused deficits in vocal imitation, while lesions of Ad did not cause any such deficit, suggesting that the loss of song imitation following lesions targeted to AIV was not a secondary consequence of an unintended lesion in Ad. Our finding that larger lesions of Ad produced severe akinesia and immobility is consistent with the hypothesis that Ad plays a role in locomotion and other motor behaviors, rather than vocal learning (Feenders et al., 2008).

There are several potential explanations for the discrepancy between our findings on the effects of Ad lesions and those of Bottjer and Altenau (2010). Clearly, the Ad lesion protocol of Bottjer and Altenau impacted a subset of neurons important for vocal learning, while our Ad lesion protocol did not. This subset of neurons, in principle, could be within Ad, within AIV, or could be in another, as yet undescribed, adjacent region important for vocal learning. In our view, the most parsimonious explanation for the detrimental effects on vocal learning is that the Ad lesions of Bottjer and Altenau had an unintended impact on neurons in AIV. More detailed electrophysiological studies of the pathways into and out of Ad will be needed to completely resolve these different views of its function.

The effects of lesions directed to AIV in juvenile birds bear some resemblance to the effects of lesions in the AFP, a basal ganglia–thalamocortical circuit necessary for vocal learning. Lesions of Area X, the basal ganglia component of the AFP, in young birds have a substantial detrimental effect on song imitation and result in persistent song variability into adulthood (Scharff and Nottebohm, 1991). In contrast, lesions of Area X have relatively little immediate effect on song performance in adult and juvenile birds (Goldberg and Fee, 2011; Kojima et al., 2013). Similarly, we found that birds in which AIV was lesioned at an early age produced a poor imitation of the tutor song and developed a less stereotyped song than did control birds, and AIV lesions in adult or juvenile birds have little immediate effect on vocal performance. Lesions of Area X also largely block the degradation of song following deafening (Kojima et al., 2013), and similarly, but to a lesser extent, we found that lesions of AIV slow song degradation in deafened birds. The similar patterns of findings in AIV-lesioned birds and Area X-lesioned birds is consistent with the possibility that the AIV, or some components of it, may interact with the AFP, perhaps by transmitting to it a signal important for its function.

In principle, there are several ways the loss of a descending auditory cortical projections might affect vocal learning. We first address the possible role of the projection from AIV to the dopaminergic midbrain in vocal learning. In the songbird, dopaminergic inputs to Area X have been hypothesized to carry signals related to motivational aspects of singing and to regulate vocal variability, in particular while switching between less variable song directed to a female bird to more variable song produced in social isolation (Yanagihara and Hessler, 2006; Hara et al., 2007; Leblois et al., 2010). Notably, we find that juvenile and young adult birds exhibit normal song variability after AIV lesions, suggesting that AIV may not be involved in the regulation of vocal variability, and that the deficits in vocal learning do not result from a loss of exploratory song variability. Alternatively, reduced glutamatergic drive after AIV lesion could result in reduced tonic dopaminergic input to Area X. In mammals, reduced dopaminergic input to the striatum can have deleterious effects on BG function, including reduced spontaneous activity and the loss of spines in the medium spiny neurons (Kreitzer and Malenka, 2008). Songbirds exhibit patterns of dopamine receptor expression in cortex and Area X (Kubikova et al., 2010), and responses to dopamine stimulation (Ding and Perkel, 2002) that parallel those found in mammals. Finally, another possibility is that impoverished vocal imitation after lesions of AIV results from the loss of a reinforcement signal that carries information about recent song performance. The short-latency response of AIV neurons to disrupted auditory feedback is consistent with the possibility that AIV may play a role in computing or transmitting a fast online signal to VTA/SNc.

Previous studies have shown that neurons in the vicinity of RA, and potentially within AIV, have several sub-telencephalic targets other than VTA/SNc, including a higher-order auditory thalamic nucleus Ov-shell and parts of the auditory midbrain (Vates et al., 1996). Thus, deficits in vocal learning following lesions targeted to AIV could potentially arise from loss of signaling in these other pathways. Further studies will be required to elucidate the relation between neurons projecting to these different downstream targets, and to determine the role of these different pathways in vocal learning.

One key to understanding the function of AIV is to characterize its cortical afferents, and several candidates have been identified. Güntürkün et al. have described a projection in pigeons from the lateral caudal nidopallium (NCL) to the ventral arcopallium (Kroner and Gunturkun, 1999). The projection we describe here in the zebra finch—from the caudal nidopallium (NC) posterior to HVC—terminates primarily in the posterior ‘stem’ part of AIV. This projection may be related to the previously described nidopallium caudolaterale (NCL) projection, although NCL appears to be more lateral than NC. NCL was recently found to be involved in error and reward processing (Starosta et al., 2013), and based on several lines of evidence, it has been suggested that NCL may be analogous to mammalian prefrontal cortex (Kroner and Gunturkun, 1999). Notably, in mammals the major cortical projection to VTA arises from prefrontal cortex, a key structure for cognitive control and goal-directed actions (Murase et al., 1993).

Previous studies in the zebra finch have also identified inputs to the intermediate arcopallium from auditory cortical regions, including primary auditory cortical field L1 and HVC-shelf in the nidopallium (Kelley and Nottebohm, 1979; Vates et al., 1996; Mello et al., 1998). These projections were found to terminate near RA, in a region termed RA-cup, and are likely homologous to projections in the pigeon from the dorsal nidopallium (Nd) to the ventromedial intermediate arcopallium (AIvm) (Wild et al., 1993). We have confirmed the existence of these projections, and using anatomical tracing and electrophysiological techniques, we have demonstrated that these inputs directly or indirectly innervate AIV neurons projecting to the dopaminergic midbrain. Inputs from L1 and anterior parts of HVC shelf appear localized to the parts of AIV anteroventral to RA. Inputs from posterior HVC shelf appear to terminate in both the anterior and posterior parts of AIV, and show significant overlap with inputs from the more posterior caudal nidopallium described above.

Our anatomical studies have also revealed a novel projection to AIV from the caudal mesopallium (CM), an avian auditory area previously identified as homologous to higher-order auditory cortex (Bauer et al., 2008) or to upper layers of primary auditory cortex (Karten, 2013). The projection from CM terminates within AIV, and electrical stimulation in CM causes spiking activity in VTA/SNc-projecting AIV neurons, likely through orthodromic activation. The existence of a projection to AIV from CM and L1 may be important for two reasons. The cluster of neurons in CM retrogradely labeled from AIV was overlapped with the region of CM, also known as Avalanche (Av), that forms bidirectional synaptic interactions with both HVC and NIf (Bauer et al., 2008; Akutagawa and Konishi, 2010), two song control nuclei also involved in vocal learning (Roberts et al., 2012). Furthermore, a small subpopulation of neurons in CM and L1 have been shown to respond to noise bursts presented during singing, but not to noise bursts presented during playback of the birds own song (Keller and Hahnloser, 2009). These responses, highly reminiscent of responses in AIV, have led to the suggestion that one computational function of CM and L1 may be to compare actual auditory feedback with the song template (Keller and Hahnloser, 2009). Further studies are required to determine if the hypothesized error-related responses of AIV neurons derive from activity in this population of CM and L1 neurons.

One can speculate about the potential role for AIV in computing or transmitting a fast online signal to VTA/SNc that potentially carries information about recent song performance. Most VTA/SNc-projecting AIV neurons responded to disruptive auditory feedback with a latency of less than 25 ms and response duration of less than 100 ms. This response may have a sufficiently fast temporal resolution to mediate vocal learning, assuming that the synaptic learning rules in Area X and the motor pathway employ a short-term synaptic memory such as an eligibility trace (Sutton and Barto, 1998; Fee and Goldberg, 2011; Redondo and Morris, 2011). Such a fast dopaminergic reinforcement signal could, in principle, be used in Area X to correlate vocal performance with an efference copy of vocal motor commands (Fee and Goldberg, 2011) and to drive corticostriatal plasticity at HVC inputs to medium spiny neurons (MSNs). The MSNs could then act through their downstream pallidothalamic pathway to (1) bias the motor system in favor of vocal commands that previously led to better song performance and (2) drive plasticity in the motor pathway such that the juvenile song gradually converges to the desired vocal output (Andalman and Fee, 2009; Fee and Goldberg, 2011; Warren et al., 2011; Charlesworth et al., 2012).

Additional studies would be required to establish the nature of auditory reinforcement signals in the pathway we have described here, or possibly through other downstream targets, and to establish a causal role for these circuits in vocal learning.

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Author details

1. Yael Mandelblat-Cerf

   Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   YM-C, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   Liora Las and Natalia Denisenko

   For correspondence

   ycerf@bidmc.harvard.edu

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-6356-5856

2. Liora Las

   Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   LL, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   Yael Mandelblat-Cerf and Natalia Denisenko

   Competing interests

   The authors declare that no competing interests exist.

3. Natalia Denisenko

   Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   ND, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Contributed equally with

   Yael Mandelblat-Cerf and Liora Las

   Competing interests

   The authors declare that no competing interests exist.

4. Michale S Fee

   Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   MF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

   Competing interests

   The authors declare that no competing interests exist.

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