Synaptic connectivity of sensorimotor circuits for vocal imitation in the songbird

Patterns of synaptic connectivity anchor how activity flows within sensory and motor networks in the brain. Consequently, these synaptic connections delimit and help define how sensory and premotor circuits interact. Mapping synaptic pathways between these circuits is therefore fundamental to understanding the neural computations involved in learning and producing behaviors. Great strides have been made in developing technologies for large-scale recording of neuronal activity across brain regions (Steinmetz et al., 2021; Demas et al., 2021; Manley et al., 2024) and in building functional connectomes of local (<1 mm) synaptic circuits (Bae et al., 2021). Less progress has been made at intermediate levels, which aim to build wiring diagrams for long-range synaptic connections in the brain (Petreanu et al., 2009). Moreover, while most evidence for long-range synaptic connectivity has been provided piecemeal, we now have the opportunity to systematically apply circuit dissection methods to other animal models in which we still know relatively little about patterns of long-range synaptic connectivity.

We were inspired to understand the wiring diagram for sensorimotor circuits critical for birdsong, a complex and volitionally produced skilled behavior that, like speech and language, is dependent on forebrain circuits for its fluent production (Konopka and Roberts, 2016; Doupe and Kuhl, 1999). The dedicated neural circuits associated with song provide a powerful model in which to study how synaptically linked sensory and motor networks of neurons control a complex behavior. The courtship song of male zebra finches is one of the better studied naturally learned behaviors (Immelmann, 1969; Price, 1979; Sossinka and Bohner, 1980; Böhner, 1983; Zann, 1984; Clayton, 1987; Böhner, 1990; Zann, 1996; Tchernichovski et al., 1999; Tchernichovski et al., 2001; Funabiki and Konishi, 2003; Goller and Cooper, 2004; Alam et al., 2024). Zebra finch song is controlled by a relatively discrete set of interconnected forebrain regions located in the dorsal ventricular ridge (DVR; Nottebohm et al., 1976; Konishi, 1989; Schmidt and Goller, 2016; Schmidt et al., 2004; Mooney, 2009; Fee et al., 2004; Roberts et al., 2017; Aronov et al., 2008; Moll et al., 2023; Daliparthi et al., 2019). The DVR is the avian analogue of the mammalian neocortex, and like the neocortex, it contains parcellated regions that (i) receive sensory information from the thalamus, (ii) regions that process information connected by mostly ipsilaterally projecting intratelencephalic projection neurons (PNs), and (iii) output motor circuits projecting back to the thalamus and to motor regions throughout the brainstem (Stacho et al., 2020; Colquitt et al., 2021). The vocal premotor nucleus HVC (proper name) is a central hub in the DVR song network. HVC is necessary for juvenile song learning and adult song production. It also serves as the primary synaptic interface between sensory pathways and the premotor circuits involved in learning and controlling singing behavior (Nottebohm et al., 1976; Roberts et al., 2017; Aronov et al., 2008; Ikeda et al., 2020; Zhao et al., 2019; Bauer et al., 2008; Garcia-Oscos et al., 2021).

Although anatomical evidence delineated the main input and output pathways of HVC decades ago (Nottebohm et al., 1976; Roberts et al., 2017; Nottebohm et al., 1982; Vates et al., 1996; Akutagawa and Konishi, 2010; Roberts et al., 2008), a cell-type-specific synaptic wiring diagram of this core circuitry has remained out of reach. HVC receives intratelencephalic input from at least four sensory and sensorimotor regions in the DVR (NIf, Av, mMAN, and RA) and one thalamic brain region, Uva (Figure 1A and B; see Figure 1 legend for anatomical descriptions). Several lines of evidence support the idea that song learning and vocal motor control involve some of these pathways projecting into HVC (Roberts et al., 2017; Zhao et al., 2019; Garcia-Oscos et al., 2021; Roberts et al., 2008; Foster and Bottjer, 2001; Danish et al., 2017; Coleman and Vu, 2005; Hamaguchi and Mooney, 2012; Hamaguchi et al., 2016; Roberts et al., 2012). In addition, HVC has three distinct classes of intratelencephalic projecting neurons that function as the output pathways important for song learning and song motor control (Roberts et al., 2017). HVC’s projection onto RA through HVC_RA neurons forms the descending song motor pathway necessary for song production. HVC’s projection onto Area X through HVC_X neurons forms a DVR-striatal circuit that is important for song plasticity. HVC’s projection onto Avalanche through HVC_Av neurons forms the song circuit’s projection back to the auditory DVR, a pathway that is important for evaluating motor performance during song learning (Roberts et al., 2017).

Figure 1

Download asset Open asset

Mapping the synaptic connectivity between HVC’s input and output pathways has been hindered by the lack of tools for robustly manipulating these circuits. We have overcome this issue by employing better-performing optogenetic channels packaged in an adeno-associated virus (AAV) that we optimized for expression throughout various portions of the song circuitry. Specifically, we made use of the axonal expression of the opsin eGtACR1 (Mardinly et al., 2018; Govorunova et al., 2015). GtACRs are blue light-driven Cl^- channels that, while hyperpolarizing at the soma and dendrites, are depolarizing at axon terminals due to the shifted internal Cl^- concentration in axonal compartments (Malyshev et al., 2017; Messier et al., 2018; Khirug et al., 2008). Here, we use viral expression of eGtACR1 to achieve independent control of synaptic release from NIf, Uva, mMAN, and Av. We paired this with whole-cell patch-clamp recordings from visually targeted HVC projection neurons (HVC-PNs; HVC_X, HVC_RA, HVC_Av), identified using retrograde tracer injections into each efferent region (Figure 1C). This permitted the first large-scale cell-type-specific synaptic connectivity mapping, using gold-standard electrophysiological methods, in songbirds. In ex vivo brain slices from adult male zebra finches, we mapped the properties of synaptic transmission at the 12 afferent-HVC-PN combinations. Via recordings from hundreds of HVC PNs, we provide a comprehensive polysynaptic and monosynaptic mapping of the connectivity between these afferent sensory pathways and the output pathways of HVC. Through this research, we provide the first long-range cellular resolution synaptic connectivity map in the avian DVR, a fundamental step in the effort to identify common themes and differences in synaptic connectivity between the DVR and mammalian neocortical circuits.

To conduct opsin-assisted circuit mapping in zebra finches, we expressed eGtACR1 in NIf, Uva, mMAN, or Av using AAVs (Figure 1C; a cocktail of AAV2.9-Cbh-FLP and AAV2.9-Cbh-fDIO-eGtACR1 injected in HVC afferent areas). 4–6 weeks later, we injected retrograde tracers in HVC projection targets (RA, Area X, and Av) to label HVC-PN classes in different fluorescent channels. After 2–7 days, we obtained acute brain slices and performed whole-cell patch-clamp recordings from visually identified HVC-PNs to examine optically evoked post-synaptic currents (oPSCs). We confirmed that oPSCs measured while holding the membrane voltage at –70 mV were excitatory (oEPSCs) and were mediated by AMPA receptors (current suppressed by bath application of DNQX; Figure 1C). Holding the membrane potential to +10 mV allowed us to measure inhibitory currents (oIPSCs) mediated by GABAa receptors (current suppressed by bath application of gabazine; Figure 1C).

For each pathway, we report the likelihood of observing oEPSCs and oIPSCs, the amplitude of oEPSCs and oIPSCs, and the excitatory-inhibitory ratio (oEPSC/oIPSC). Although we calculated the paired-pulse ratio, the slow kinetics of eGtACR1 make interpretation of these results difficult; therefore, it is not being reported. We also report whether observed synaptic currents have a monosynaptic component by applying the voltage-gated Na⁺ channel blocker tetrodotoxin (TTX) followed by the K⁺ channel blocker 4-aminopyridine (4AP) (Petreanu et al., 2009; Linders et al., 2022; Figure 1C). TTX blocks action potentials and the addition of 4AP blocks the rapid K+-dependent repolarization of the axon. This allows local opsin-driven depolarizations to reach threshold for calcium-dependent vesicle docking and release, thus revealing optically evoked monosynaptic currents (Linders et al., 2022). We consistently observed that, while oEPSCs registered in ACSF displayed multiple peaks consistent with a polysynaptic source, currents suppressed by TTX that returned following bath application of 4AP had a monotonic rising slope and a single peak, consistent with a monosynaptic origin of the current (Figure 1C).

Uva monosynaptically innervates HVC_RA neurons

Nucleus Uvaeformis (Uva) is a small polysensory nucleus in the caudal thalamus that is reported to be necessary for song production. Uva is a recipient of somatosensory information from the trigeminal system, visual input from the optic tectum, auditory information from the lateral lemniscus, cholinergic input from the medial habenula, and information about respiratory timing from the ventral respiratory column (Coleman et al., 2007; Faunes and Wild, 2017; Wild and Gaede, 2016; Wild et al., 2010; Wild, 1994; Akutagawa and Konishi, 2005). Its inputs to HVC are thought to be pivotal in song motor control (Moll et al., 2023; Danish et al., 2017; Coleman and Vu, 2005). However, how Uva synaptically influences HVC activity is still poorly understood. We found that Uva neurons are robustly transduced by our AAV expressing eGtACR1 (Figure 2A). Thalamic regions around Uva do not project to HVC, allowing us to selectively map Uva’s input to the three classes of HVC-PNs.

Figure 2 with 1 supplement see all

Download asset Open asset

In acute brain slices we observed glutamatergic oEPSCs in all three classes of retrogradely identified HVC-PNs (27/66 HVC_X, 43/51 HVC_RA, 14/28 HVC_Av neurons; Figure 2B, Figure 2—figure supplement 1). Optical stimulation of Uva axons resulted in significantly lower probability of observing oEPSCs in HVC_X and HVC_Av than in HVC_RA neurons (Figure 2B, Figure 2—figure supplement 1). When we held the membrane voltage to +10 mV, we also observed relatively delayed GABAergic currents (53.93% probability, Figure 2—figure supplement 1), consistent with disynaptic inhibition. Uva axon stimulation elicited oIPSCs mainly in cells where we observed oEPSCs (oIPSC contingent with oEPSC: HVC_X, 100.0%, HVC_RA 96.6%, HVC_Av, 95.8%; Figure 2B).

Despite the markedly higher probability of eliciting currents in HVC_RA neurons, oEPSC and oIPSC amplitudes were comparable across all the HVC-PN classes (Figure 2C, D, Figure 2—figure supplement 1). The excitation/inhibition ratio revealed that Uva terminal stimulation generally led to higher amplitude oIPSCs than oEPSCs (Figure 2E, Figure 2—figure supplement 1).

We next examined if HVC-PN classes receive monosynaptic input from Uva. Recent studies have suggested different views for how Uva may influence the propagation of activity in HVC. Uva has been proposed to provide high-frequency (30–60 Hz) synchronous synaptic inputs that facilitate activity propagation across HVC_RA premotor neurons (Hamaguchi et al., 2016). In another proposal, Uva selectively drives activity in only a fraction,~16%, of HVC_RA neurons which are active during syllable transitions (Moll et al., 2023). Consistent with the higher likelihood of polysynaptic excitation in HVC_RA neurons, we found that oEPSC returned after application of TTX +4 AP in all HVC_RA neurons tested (HVC_RA: 6/6 neurons) while HVC_X and HVC_Av were infrequently found to receive monosynaptic input from Uva (HVC_X: 1/5 cells, HVC_Av: 2/5 cells, Figure 2F). Our synaptic circuit mapping provides the first conclusive evidence that Uva makes monosynaptic connections with HVC_RA neurons. Given that we observed oEPSCs in 84.3% of recorded HVC_RA and found monosynaptic connections from Uva onto 100% of the HVC_RA neurons tested, our data further suggests that Uva is unlikely to make monosynaptic connections with only a small percentage of HVC_RA neurons.

Previous studies have been equivocal to the excitatory, inhibitory, or neuromodulatory nature of the Uva to HVC circuitry (Moll et al., 2023; Coleman et al., 2007). We did not observe monosynaptic oIPSCs in any cell class, suggesting that Uva provides only a glutamatergic input to HVC (HVC_X: 0/3, HVC_RA: 0/5, HVC_Av: 0/4, data not shown). We combined retrograde labeling from HVC with in-situ hybridization labeling on brain slices. This revealed that Uva neurons projecting to HVC (Uva_HVC) selectively expressed the glutamatergic marker SLC17A6, but not the GABAergic marker GAD1 (Colquitt et al., 2021; Figure 2G). Interestingly, our labeling did not identify any GABAergic neurons within Uva, indicating that Uva_HVC may function as an excitatory relay of diverse polysensory input pathways. Although we cannot exclude the possibility that Uva neurons may also co-release neuropeptides, our results define the excitatory synaptic connectivity between Uva and HVC PNs.

NIf provides excitatory monosynaptic input to all HVC-PNs

We next examined the synaptic transmission between the nucleus NIf and the three classes of HVC-PNs. NIf is a higher order polysensory DVR nucleus located adjacent to the primary auditory DVR (Field L2 - region that receives thalamic input from the avian homologue of the medial geniculate nucleus). NIf is the recipient of afferents from Uva and multiple areas of the auditory DVR and has an essential role in relaying auditory signals to HVC (Coleman and Mooney, 2004). The synaptic connection between NIf and HVC is obligatory in a young male zebra finch’s ability to form a memory of their father’s song (Zhao et al., 2019; Roberts et al., 2012). This memory is used as birds evaluate their song performances, permitting vocal imitation of song (Ikeda et al., 2020). Optogenetic manipulations of NIf inputs to HVC are also sufficient to implant song memories that guide song syllable imitation (Zhao et al., 2019). Although NIf is selectively active during singing, it is not necessary for producing song in adulthood or the song imitation process once the memory of a father’s song is acquired in juvenile birds (Zhao et al., 2019; Roberts et al., 2012; Mackevicius et al., 2020; Otchy et al., 2015).

To map synaptic connections from NIf to HVC, we expressed eGtACR1 in NIf. While the surrounding area, Field L, has been described to project to the ventral border of HVC (Vates et al., 1996; Fortune and Margoliash, 1995; Kelley and Nottebohm, 1979), we recorded from visually identified, retrogradely labeled HVC projection neurons. Area Avalanche is the only other nearby brain region that also sends projections to HVC, and we carefully checked for a lack of expression in this region in all brain hemispheres used in this study (Figure 3A). This provides confidence that the currents we measured are evoked by stimulating neurotransmission from NIf terminals. Light stimulation (1ms) reliably elicited glutamatergic oEPSCs in all three classes of HVC projection neurons (78.6% probability, Figure 3B, Figure 3—figure supplement 1). We examined oIPSCs in a subset of neurons and found strong GABAergic currents evoked by optical stimulation in all three classes of projection neurons (65.9% probability, Figure 3B, Figure 3—figure supplement 1). oIPSCs were predominantly found in cells in which we also observed oEPSCs (oIPSC contingent with oEPSC: HVC_X 90.0%; HVC_RA 76.5%, HVC_Av 94.1%; Figure 3B).

Figure 3 with 1 supplement see all

Download asset Open asset

We found oEPSC and oIPSC amplitudes to be comparable between HVC_X and HVC_Av PNs (Figure 3C and D, Figure 3—figure supplement 1). However, HVC_RA neurons had lower amplitude currents than the other HVC-PN classes (Figure 3C and D, Figure 3—figure supplement 1). When we computed the amplitude of oEPSCs and oIPSCs for each cell to calculate the excitation/inhibition, we observed that optogenetic stimulation of NIf axonal terminals evoked relatively higher amplitude oIPSCs than oEPSCs in all HVC-PN classes (Figure 3E, Figure 3—figure supplement 1).

We next examined monosynaptic connectivity from NIf to each HVC-PN. We found that application of TTX followed by 4AP reliably returned oEPSCs in all three HVC projection subtypes (monosynaptic oEPSCs: HVC_X: 5/5, HVC_RA: 4/5, HVC_Av: 5/5, Figure 3F). This widespread monosynaptic connectivity is in stark contrast to thalamic excitatory inputs from Uva that preferentially target HVC_RA neurons. Similar to Uva projections, we did not observe monosynaptic oIPSCs (HVC_X: 0/5, HVC_RA: 0/2, HVC_Av: 0/3, data not shown), suggestive of a glutamatergic projection from NIf to HVC. We combined retrograde labeling from HVC with in situ hybridization labeling on brain slices. NIf neurons projecting to HVC (NIf_HVC) selectively expressed the glutamatergic marker SLC17A6, but not the GABAergic marker GAD1 (Colquitt et al., 2021; Figure 3G). This finding is consistent with previous studies indicating that NIf projections result in disynaptic inhibition onto HVC projection neurons (Coleman and Mooney, 2004).

Together, these results indicate that intratelencephalic DVR circuits are excitatory and that NIf provides excitatory glutamatergic monosynaptic projections onto all three known classes of HVC-PNs. Given that monosynaptic connectivity across regions of the DVR has not previously been examined, this suggests the possibility that, in contrast to the thalamo-DVR pathways, projections between regions of the DVR might not have cell type selectivity.

Novel connection from mMAN to Av and selective monosynaptic connections with HVC

To better define the specificity of regional DVR projections, we next mapped synaptic connectivity of two other HVC afferents, mMAN and Av. The DVR is comprised of three large pallial fields separated by distinct lamina: the nidopallium, mesopallium, and the arcopallium. HVC, NIf, and mMAN reside in the nidopallium, and Av is in the mesopallium. HVC and NIf are in caudal regions of the DVR’s nidopallium, while mMAN is located at anterior portions of the DVR, some 4–5 mm from HVC. Av, on the other hand, is only ~1.5 mm from HVC but is in a distinct pallial field.

Cell-type-specific synaptic connectivity between nidopallial regions and between mesopallial and nidopallial regions is poorly understood; however, the connectivity of mMAN and Av is better described than other portions of these pallial fields. mMAN receives synaptic input from the thalamic nucleus DMP and is part of a vocal motor DVR-thalamo-DVR loop (mMAN >HVC > RA>DMP > mMAN), a circuit architecture similar to premotor and motor control-related cortico-thalamo-cortical loops in mammals. Although the synaptic connectivity between mMAN and HVC has not been well studied, lesions of mMAN in juvenile zebra finches result in song imitation deficits and lesions in adult Bengalese finches increase song syntax variability, suggesting a role in selection and planning of vocal motor sequencing (Foster and Bottjer, 2001; Koparkar et al., 2024). Av is one of the more recently described song-related regions, and unlike Uva, NIf, and mMAN, it is reciprocally connected to HVC. The role of Av is still poorly explored, but it is understood to be embedded in a higher order auditory portion of the DVR and thought to be an important node in the circuit comparing efferent copies of motor commands from HVC to auditory feedback (Bauer et al., 2008; Akutagawa and Konishi, 2010).

While evaluating connectivity between HVC, mMAN, and Av, we discovered that mMAN also provides a strong projection to Av (Figure 4). Approximately half of the identified projection neurons in mMAN project to Av, and our tracing experiments reveal that there are only partially overlapping classes of projection neurons in mMAN: mMAN_HVC PNs, mMAN_Av PNs, and mMAN_HVC/AV PNs. This newly identified pathway appears positioned to provide the auditory system with information about song motor commands via a DVR-thalamo-DVR loop (RA >DMP > mMAN>Av). It also opens the potential for synaptic loops relaying through HVC between these regions.

Figure 4

Download asset Open asset

Brain regions nearby mMAN do not send projections to HVC, and we were able to achieve robust expression of eGtACR1 in mMAN (Figure 5A), allowing us to selectively examine the synaptic connectivity between mMAN and HVC-PNs. Optical stimulation of mMAN axon terminals in brain slices revealed glutamatergic oEPSCs in all retrogradely identified projection neuron classes, albeit with significantly lower success rate compared to what we observed when stimulating NIf terminals (48.15% probability; mMAN vs. NIf oEPSC probability: Fisher’s exact test p<0.001). In addition, we found that oEPSCs were more likely evoked in recordings from HVC_X and HVC_Av than from HVC_RA neurons (Figure 5B, Figure 5—figure supplement 1).

Figure 5 with 1 supplement see all

Download asset Open asset

Optogenetic stimulation of mMAN axon terminals also revealed GABAergic currents in all three HVC-PN classes (48.24% probability). Similar to NIf and Uva inputs, oIPSCs elicited by mMAN axons were most often found in cells where oEPSCs were also observed (oIPSC contingent with oEPSC: HVC_X: 93.5%, HVC_RA: 96.4%, HVC_Av: 90.9%; Figure 5B).

While oEPSCs amplitudes across HVC-PN classes were statistically indistinguishable (Figure 5C), we found that oIPSCs in HVC_Av had significantly higher amplitude than those evoked in HVC_RA neurons (Figure 5D, Figure 5—figure supplement 1). Despite this difference, when computing the E/I ratio, we did not see any significant difference across HVC-PNs, and like the other inputs examined, GABAergic currents had higher amplitude than glutamatergic currents (Figure 5E, Figure 5—figure supplement 1).

We next asked whether mMAN monosynaptically contacted any class of HVC-PNs. We found evidence that mMAN monosynaptically contacts all three classes of HVC PNs. The most reliable monosynaptic transmission is between mMAN and HVC_Av neurons and HVC_X neurons (HVC_RA: 2/5, HVC_X: 5/7, HVC_Av: 6/6, Figure 5F). We did not observe monosynaptic oIPSCs (HVC_X: 0/5, HVC_RA, 0/5, HVC_Av: 0/6, data not shown), and confirmed that mMAN neurons projecting to HVC are glutamatergic using in-situ hybridization combined with retrograde tracing from injection of tracer in HVC (Figure 5—figure supplement 1). Together, this data indicates that mMAN predominantly provides robust monosynaptic excitatory input to HVC_Av and HVC_X neurons. HVC_Av neurons only comprise ~3% of the neurons in HVC (Roberts et al., 2017). Therefore, finding that 100% of the HVC_Av neurons tested receive monosynaptic input from mMAN, suggesting that this is likely to be a highly selective synaptic target of mMAN inputs to HVC. Given that we have now also identified a projection from mMAN directly to Av, this supports a model in which mMAN is providing Av song-related signals directly and indirectly via projections onto HVC_Av neurons.

Lastly, we mapped the synaptic inputs from Av onto HVC-PNs. Avalanche lacks clear anatomical boundaries, and our viral expression did spread into surrounding brain regions, but only brain hemispheres in which we could verify a lack of expression in NIf were included in this study (Figure 6A). Whole-cell patch-clamp recordings from identified HVC-PN classes in brain slices revealed glutamatergic oEPSCs in all three cell classes, yet most of the cells were not excited by Av terminals (39.18% probability; Av vs. NIf oEPSC probability: Fisher’s exact test p<0.001). However, the probability of evoking oEPSCs was similar among HVC-PN classes (Figure 6B, Figure 6—figure supplement 1). We tested the presence of oIPSCs in a subset of cells and found GABAergic currents in all three projection subtypes (43.24% probability, Figure 6—figure supplement 1). As in other pathways already described, we observed oIPSCs mainly in HVC-PNs also showing oEPSCs (oIPSC contingent with oEPSC: HVC_X 100.0% HVC_RA 90.0%, HVC_Av 84.6%; Figure 6B).

Figure 6 with 1 supplement see all

Download asset Open asset

While oEPSCs amplitudes were broadly similar among HVC-PN classes (Figure 6C), oIPSCs had significantly smaller amplitude in HVC_RA compared to those evoked in HVC_X neurons (Figure 6D, Figure 6—figure supplement 1). Consistently, this was also reflected in a significantly higher E/I ratio in HVC_RA compared to HVC_X. Similar to the other afferents, Av axon terminal stimulation elicited stronger GABAergic currents than glutamatergic currents across HVC-PN classes (Figure 6E, Figure 6—figure supplement 1).

When examining which HVC-PN neuron classes received monosynaptic input from Av, we consistently observed monosynaptic transmission onto HVC_X neurons (HVC_X 4/5 cells monosynaptically contacted). Monosynaptic transmission onto HVC_RA and HVC_Av was less common (HVC_RA 2/5, HVC_Av 2/6; Figure 6F). Previous reports indicated that Av neurons projecting to HVC are excitatory (Roberts et al., 2017). Consistent with this, we didn’t observe any monosynaptic oIPSC across HVC-PNs (monosynaptic oIPSCs: HVC_X 0/4, HVC_RA 0/3, HVC_Av 0/3, data not shown) and further confirmed that Av_HVC neurons are glutamatergic by in situ hybridization labeling of retrogradely identified neurons (Figure 6—figure supplement 1). Our data shows that Av provides monosynaptic excitatory input to HVC_X neurons with high frequency and makes monosynaptic connections with HVC_RA and HVC_Av neurons less frequently.

Taken together, this research provides a rich dataset mapping the polysynaptic and monosynaptic connectivity of the input and output pathways of HVC – the best studied portions of the avian song circuitry – as well as a newly discovered pathway between DVR circuits that are important for vocal learning (Figure 7).

Figure 7 with 1 supplement see all

Download asset Open asset

Speech and language are controlled by interconnected neocortical and subcortical networks, including dorsal and ventral cortical pathways for articulation and lexical processing, and pathways looping through the thalamus, basal ganglia, and cerebellum (Konopka and Roberts, 2016; Hickok and Poeppel, 2007; Poeppel et al., 2012; Hertrich et al., 2020). The synaptic connectivity between specific populations of neurons among these circuits is going to be difficult to map. Yet, this will ultimately be necessary for understanding the neuroanatomical basis and circuit computations associated with speech and language. Although the avian DVR and mammalian neocortex have distinct developmental origins (ventral and dorsal pallium, respectively), HVC PNs neurons have similar gene expression and connectivity patterns to the intratelencephalic neurons described in layers 2–6 of the mammalian neocortex (Colquitt et al., 2021), suggesting evolutionary convergence in circuit computations needed for complex behaviors like vocal imitation. Thus, there is much to be gained by mapping the connectivity between the specific populations of neurons known to be essential in the sensory and sensorimotor process of song imitation.

Using large-scale, multi-pathway, and long-range functional synaptic circuit mapping, we report cell-type-specific synaptic connectivity across forebrain sensorimotor networks necessary for learning and producing learned vocalizations. The songbird motor nucleus HVC is crucial for song production. Its partial or complete lesion results in song disruption and permanent loss, respectively (Aronov et al., 2008; Simpson and Vicario, 1990; Basista et al., 2014). Electrical stimulation of HVC can halt song (Vu et al., 1994; Ashmore et al., 2005; Vu et al., 1998). Further, HVC is essential in juvenile song learning (Roberts et al., 2017; Garcia-Oscos et al., 2021; Roberts et al., 2012; Sánchez-Valpuesta et al., 2019; Tanaka et al., 2018). We show that long-range sensory pathways to HVC are excitatory and contact HVC intratelencephalic PNs. We find that these connections are not stochastic but are instead strongly biased to make synapses with premotor (HVC_RA), auditory (HVC_AV), or basal ganglia (HVC_X) projection pathways. Lastly, we find a remarkable correspondence in the probability of finding oEPSCs and oIPSCs in the same postsynaptic neurons, indicative of highly interconnected cell assemblies at postsynaptic targets of long-range connections within the DVR.

In addition to finding widespread polysynaptic transmission upon stimulation of any of the afferents, we endeavored to reveal the wiring diagram of HVC inputs by isolating the monosynaptic components of these currents (Figure 7). We found surprisingly specific and compartmentalized monosynaptic neurotransmission between each input and HVC-PN classes. NIf terminals monosynaptically contacted all three HVC-PN classes. Uva predominantly makes monosynaptic connections with HVC_RA neurons, while mMAN most frequently makes monosynaptic connections with HVC_Av and HVC_X PNs but appears to only sparsely project to HVC_RA neurons. Lastly, Av predominantly makes monosynaptic connections with HVC_X neurons and connects less frequently with HVC_RA and HVC_Av neurons. When combined with our discovery of a new pathway from mMAN to Av, our data draws a scenario of multiple intermingled loops coexisting across HVC input and output circuits.

Thalamic inputs from Uva may directly affect the song motor pathway through monosynaptic projections onto HVC_RA neurons. The potential relevance of this specific connection has been recently studied (Moll et al., 2023; Hamaguchi et al., 2016). Additionally, the seldom yet significant direct inputs to HVC_Av neurons we identify are interesting considering the proposed role of HVC_Av neurons in providing a forward model of song to the auditory system (Roberts et al., 2017). Uva is also known to project to both NIf and Av. Uva’s monosynaptic connections to classes of HVC PNs may therefore directly integrate with the propagation of forward models of song timing to the auditory system, particularly during the learning of syllable transitions (Roberts et al., 2017).

mMAN has recently been found to contribute to the ordering of song syllables (Koparkar et al., 2024). This could be attributable to the strong connections identified here between mMAN and HVC_Av PNs and from mMAN directly to Av. HVC_Av neurons are important for learning the timing of elements within song. Lesions of HVC_Av neurons in juvenile birds disrupt learning of syllable syntax yet do not have a strong effect on learning the spectral features of syllables (Roberts et al., 2017). Further, lesions block deafening-induced degradation of songs’ temporal features and recovery of song timing following song-contingent feedback perturbations (Roberts et al., 2017). Thus, our analysis of synaptic connectivity provides important anatomical connections for a circuit critical for temporal ordering of vocalizations during learning and in adulthood.

mMAN and Av’s preferential connection to HVC_X PNs may create a bridge linking the thalamus, the auditory system, and the basal ganglia circuits important for song plasticity. mMAN receives projections from the nucleus DMP of the thalamus, which itself receives projections from RA. mMAN is therefore positioned to receive a copy of descending vocal motor commands transmitted through DMP, while Av may relay integrated information about auditory feedback and their correspondence with motor commands for song that it receives from mMAN and from HVC_Av neurons. Our findings place mMAN and Av in an ideal position to relay those signals to Area X, potentially contributing to song plasticity.

In the data shown in Figures 2, 3 and 5–6 we examined the probability and strength of oPSCs within each HVC-PNs class. We found that projection neurons in NIf, Uva, mMAN and Av consistently establish polysynaptic contact with all three HVC-PN classes. We reliably observed both glutamatergic and GABAergic polysynaptic neurotransmission upon afferent axonal terminal stimulation. Organizing our data by HVC PN types (Figure 7—figure supplement 1) reveals a bias in afferent inputs onto these different neuronal pathways. We find that HVC_X and HVC_AV neurons are most frequently excited by NIf afferents, while HVC_RA are most frequently excited by Uva, and unlikely to be excited by mMAN terminal stimulation. HVC_X and HVC_Av neurons are most strongly driven by NIf, while HVC_RA neurons receive relatively uniform amplitude oEPSCs from all four input pathways examined. Lastly, stimulation of NIf and mMAN terminals resulted in higher amplitude inhibition onto HVC_Av neurons than upon Uva or Av terminals stimulation (Figure 7—figure supplement 1).

This data points towards the existence of a rich interneuronal inhibitory network that is recruited by stimulating HVC’s afferents, evoking GABAergic transmission onto all HVC-PN classes. This is in line with previous reports that found HVC-PNs activity to be tightly regulated by interneurons (Kosche et al., 2015; Vallentin et al., 2016; Mooney and Prather, 2005). Interestingly, across all inputs studied, the likelihood of finding oIPSCs was tightly dependent on finding oEPSCs in the same cell. Previous studies have indicated that HVC PNs are disynaptically inhibited by other HVC-PNs, a feature of synaptic connectivity that is likely fundamental to the propagation of excitation in the network during song production and in the song learning process (Kosche et al., 2015; Mooney and Prather, 2005). When afferents cause excitation of HVC PNs this would therefore cause an immediate rise in the levels of disynaptic inhibition in the surrounding HVC PNs. Similar feedback inhibition dynamics have been extensively reported in cortical circuits, where this mechanism serves a crucial role of controlling the excitatory-inhibitory balance of local principal neurons (Tremblay et al., 2016; Berger et al., 2010; Kapfer et al., 2007; Silberberg and Markram, 2007). Afferents that excite HVC PNs may also contact neighboring interneurons. A similar feedforward inhibition has also been reported in the cortex, where both pyramidal neurons and interneurons projecting to them are recruited by long-range pathways (Anastasiades et al., 2018; Delevich et al., 2015). While feedback inhibition is poised to monitor and modulate the output of local principal neurons, feedforward inhibition is proposed to work as a coincidence detector improving the sensitivity of the network by filtering asynchronous inputs (Tremblay et al., 2016; Bruno and Sakmann, 2006; Bruno and Simons, 2002; Cardin et al., 2010; Pinto et al., 2000; Pinto et al., 2003).

Distinguishing among these potential feedback and feedforward circuit motifs, as well as fully characterizing the role of afferents for HVC local circuit dynamics, will require monosynaptic connectivity mapping of afferents onto HVC interneurons. The ability to do this systematically in HVC is limited by the lack of genetic methods for labeling interneurons (Dimidschstein et al., 2016). Colquitt et al., 2021 have recently identified multiple GABAergic cell subtypes in HVC, and a molecular handle is needed to experimentally approach the study of their synaptic connectivity. Nonetheless, our data suggests the existence of compartmentalized neuronal ensembles including both PNs and interneurons projecting to those PNs.

The synaptic organization of excitatory-inhibitory connections in HVC seems to allow the integration of afferent inputs within hyper-local networks or modules. This type of connectivity may make the circuit more robust to perturbations (Stauffer et al., 2012). A potential caveat is that we performed all our recordings in 230-µm-thick sagittal brain slices, and it is thought that HVC-PNs exhibit sequential activity that is organized in the rostrocaudal axis, while interneurons are speculated to be coordinated in the medio-lateral axis (Day et al., 2013). Our resection of this mediolateral organization may account for the lack of lateral inhibition found in our data. For example, our optogenetic stimulation elicits oIPSCs mostly in the same neurons where oEPSCs are observed, but not in the others, which may have received inhibition from interneurons sitting in adjacent brain slices. Lateral inhibition is normally observed in circuits displaying feedback inhibition, and previous reports indicate that HVC-PNs disynaptically inhibit other HVC-PNs (Kosche et al., 2015; Mooney and Prather, 2005). It is, however, possible that differences between neocortical and HVC microcircuitry may reduce the significance of lateral inhibition, explaining why we only seldomly observed it.

Our measurements of oPSCs onset delays from the light stimulation offer some insight into the possible wiring of afferents and intra-HVC connectivity. HVC_RA neurons displayed the fastest oEPSCs latency upon Uva axon terminal stimulation, consistent with their preferential monosynaptic connectivity. Likewise, HVC_X neurons display the fastest latency upon NIf axon terminal stimulation. However, cytoarchitecture, axonal transmission latency and synaptic delays may all play roles in determining this timing, and other monosynaptic connections are not readily reflected through polysynaptic oPSCs latency measurements. This highlights the importance of using opsin-assisted monosynaptic mapping rather than timing to identify the details of synaptic connectivity.

While our opsin-assisted circuit mapping provides us with a new level of insight into HVC synaptic circuitry, there are limitations to this research that should be considered. All circuit mapping in this study was carried out in brain slices from adult male zebra finches. Future studies will be needed to examine how this adult connectivity pattern relates to patterns of connectivity in juveniles during sensory or sensorimotor phases of vocal learning and connectivity patterns in female birds. Ex-vivo brain slices have historically been demonstrated to be a reliable window into neuronal and circuit function. However, intrinsic activity patterns of some neuronal subtypes are known to change in acute slices (Opitz et al., 2017). Moreover, the slicing procedure severs both neuromodulatory and neurotransmitter afferents, potentially attenuating or removing synaptic inputs to some circuits (Ballanyi and Ruangkittisakul, 2009). Our axonal terminal optical manipulation recruits all the fibers expressing eGtACR1 under the cone of light of the microscope objective. Therefore, our estimates of postsynaptic responses and likelihood of finding responses are based on the simultaneous release of neurotransmitter from all the afferent terminals expressing eGtACR1, and they should be understood in this context. Future studies will need to employ conditions of minimal stimulation that may further characterize the specific properties of monosynaptic and polysynaptic neurotransmission in the examined inputs. Lastly, the amplitude and likelihood of recording oPSCs directly depend on the expression rate and tropism of our viruses, and this may differ across the four afferent pathways studied here. We report low levels of viral expression in Av, which may skew our results. However, by testing oPSCs in all three HVC_PN classes in each bird help ensure that the relative contribution of Av afferent inputs onto different classes of HVC neurons is not simply an artifact associated with the efficiency of viral transduction.

Although a complete description of HVC circuitry will require the examination of other potential inputs (i.e. RA_HVC PNs, NCM, A11 glutamatergic neurons Roberts et al., 2008; Ben-Tov et al., 2023; Louder et al., 2024) and a characterization of interneuron synaptic connectivity; here, we provide a map of the synaptic connections between the four best described afferents to HVC and its three populations of projection neurons in adult birds. These results provide a view into the synaptic underpinnings of circuits fundamental for the learning and production of vocalizations. Moreover, they reveal essential connectivity patterns that can underlie song motor control and learning of syllable order. This picture of input-output organization spanning sensory, thalamic, and premotor areas may thus offer insights into the circuits for human language learning and production. Lastly, the opsin-assisted circuit mapping strategy we used provides an approach to fully characterize other long-range and local circuitry in the songbird brain, which will be critical for understanding and modeling the remarkable vocal imitation abilities of songbirds.

All data generated or analysed during this study are included in the manuscript and supporting files. The source data used in this study have been uploaded to UT Southwestern Research Data Repository (https://doi.org/10.18738/T8/FFAM6F).

1. 1. Trusel M

   (2025) UT Southwestern Research Data Repository

   Electrophysiology Data from Trusel et al., eLife 2025.

   https://doi.org/10.18738/T8/FFAM6F

1. 1. Akutagawa E
   2. Konishi M

   (2005) Connections of thalamic modulatory centers to the vocal control system of the zebra finch

   PNAS 102:14086–14091.

   https://doi.org/10.1073/pnas.0506774102

   • PubMed
   • Google Scholar
2. 1. Akutagawa E
   2. Konishi M

   (2010) New brain pathways found in the vocal control system of a songbird

   The Journal of Comparative Neurology 518:3086–3100.

   https://doi.org/10.1002/cne.22383

   • PubMed
   • Google Scholar
3. 1. Alam D
   2. Zia F
   3. Roberts TF

   (2024) The hidden fitness of the male zebra finch courtship song

   Nature 628:117–121.

   https://doi.org/10.1038/s41586-024-07207-4

   • PubMed
   • Google Scholar
4. 1. Anastasiades PG
   2. Marlin JJ
   3. Carter AG

   (2018) Cell-type specificity of callosally evoked excitation and feedforward inhibition in the prefrontal cortex

   Cell Reports 22:679–692.

   https://doi.org/10.1016/j.celrep.2017.12.073

   • PubMed
   • Google Scholar
5. 1. Aronov D
   2. Andalman AS
   3. Fee MS

   (2008) A specialized forebrain circuit for vocal babbling in the juvenile songbird

   Science 320:630–634.

   https://doi.org/10.1126/science.1155140

   • PubMed
   • Google Scholar
6. 1. Ashmore RC
   2. Wild JM
   3. Schmidt MF

   (2005) Brainstem and forebrain contributions to the generation of learned motor behaviors for song

   The Journal of Neuroscience 25:8543–8554.

   https://doi.org/10.1523/JNEUROSCI.1668-05.2005

   • PubMed
   • Google Scholar
7. Preprint

   1. Bae JA
   2. Baptiste M
   3. Bishop CA
   4. Bodor AL
   5. Brittain D
   6. Buchanan JA
   7. Bumbarger DJ
   8. Castro MA
   9. Celii B

   (2021) Functional connectomics spanning multiple areas of mouse visual cortex

   bioRxiv.

   https://doi.org/10.1101/2021.07.28.454025

   • Google Scholar
8. Book

   1. Ballanyi K
   2. Ruangkittisakul A

   (2009) Brain slices

   In: Binder MD, Hirokawa N, Windhorst U, editors. Encyclopedia of Neuroscience. Springer. pp. 483–490.

   https://doi.org/10.1007/978-3-540-29678-2_728

   • Google Scholar
9. 1. Basista MJ
   2. Elliott KC
   3. Wu W
   4. Hyson RL
   5. Bertram R
   6. Johnson F

   (2014) Independent premotor encoding of the sequence and structure of birdsong in avian cortex

   The Journal of Neuroscience 34:16821–16834.

   https://doi.org/10.1523/JNEUROSCI.1940-14.2014

   • PubMed
   • Google Scholar
10. 1. Bauer EE
    2. Coleman MJ
    3. Roberts TF
    4. Roy A
    5. Prather JF
    6. Mooney R

    (2008) A synaptic basis for auditory-vocal integration in the songbird

    The Journal of Neuroscience 28:1509–1522.

    https://doi.org/10.1523/JNEUROSCI.3838-07.2008

    • PubMed
    • Google Scholar
11. 1. Ben-Tov M
    2. Duarte F
    3. Mooney R

    (2023) A neural hub for holistic courtship displays

    Current Biology 33:1640–1653.

    https://doi.org/10.1016/j.cub.2023.02.072

    • PubMed
    • Google Scholar
12. 1. Berger TK
    2. Silberberg G
    3. Perin R
    4. Markram H

    (2010) Brief bursts self-inhibit and correlate the pyramidal network

    PLOS Biology 8:e1000473.

    https://doi.org/10.1371/journal.pbio.1000473

    • PubMed
    • Google Scholar
13. 1. Böhner J

    (1983) Song learning in the zebra finch (taeniopygia guttata): selectivity in the choice of a tutor and accuracy of song copies

    Animal Behaviour 31:231–237.

    https://doi.org/10.1016/S0003-3472(83)80193-6

    • Google Scholar
14. 1. Böhner J

    (1990) Early acquisition of song in the zebra finch, Taeniopygia guttata

    Animal Behaviour 39:369–374.

    https://doi.org/10.1016/S0003-3472(05)80883-8

    • Google Scholar
15. 1. Bruno RM
    2. Simons DJ

    (2002) Feedforward mechanisms of excitatory and inhibitory cortical receptive fields

    The Journal of Neuroscience 22:10966–10975.

    https://doi.org/10.1523/JNEUROSCI.22-24-10966.2002

    • PubMed
    • Google Scholar
16. 1. Bruno RM
    2. Sakmann B

    (2006) Cortex is driven by weak but synchronously active thalamocortical synapses

    Science 312:1622–1627.

    https://doi.org/10.1126/science.1124593

    • PubMed
    • Google Scholar
17. 1. Cardin JA
    2. Kumbhani RD
    3. Contreras D
    4. Palmer LA

    (2010) Cellular mechanisms of temporal sensitivity in visual cortex neurons

    The Journal of Neuroscience 30:3652–3662.

    https://doi.org/10.1523/JNEUROSCI.5279-09.2010

    • PubMed
    • Google Scholar
18. 1. Clayton NS

    (1987) Song learning in cross-fostered zebra finches: a re-examination of the sensitive phase

    Behaviour 102:67–81.

    https://doi.org/10.1163/156853986X00054

    • Google Scholar
19. 1. Coleman MJ
    2. Mooney R

    (2004) Synaptic transformations underlying highly selective auditory representations of learned birdsong

    The Journal of Neuroscience 24:7251–7265.

    https://doi.org/10.1523/JNEUROSCI.0947-04.2004

    • PubMed
    • Google Scholar
20. 1. Coleman MJ
    2. Vu ET

    (2005) Recovery of impaired songs following unilateral but not bilateral lesions of nucleus uvaeformis of adult zebra finches

    Journal of Neurobiology 63:70–89.

    https://doi.org/10.1002/neu.20122

    • PubMed
    • Google Scholar
21. 1. Coleman MJ
    2. Roy A
    3. Wild JM
    4. Mooney R

    (2007) Thalamic gating of auditory responses in telencephalic song control nuclei

    The Journal of Neuroscience 27:10024–10036.

    https://doi.org/10.1523/JNEUROSCI.2215-07.2007

    • PubMed
    • Google Scholar
22. 1. Colquitt BM
    2. Merullo DP
    3. Konopka G
    4. Roberts TF
    5. Brainard MS

    (2021) Cellular transcriptomics reveals evolutionary identities of songbird vocal circuits

    Science 371:eabd9704.

    https://doi.org/10.1126/science.abd9704

    • PubMed
    • Google Scholar
23. 1. Daliparthi VK
    2. Tachibana RO
    3. Cooper BG
    4. Hahnloser RH
    5. Kojima S
    6. Sober SJ
    7. Roberts TF

    (2019) Transitioning between preparatory and precisely sequenced neuronal activity in production of a skilled behavior

    eLife 8:e43732.

    https://doi.org/10.7554/eLife.43732

    • PubMed
    • Google Scholar
24. 1. Danish HH
    2. Aronov D
    3. Fee MS

    (2017) Rhythmic syllable-related activity in a songbird motor thalamic nucleus necessary for learned vocalizations

    PLOS ONE 12:e0169568.

    https://doi.org/10.1371/journal.pone.0169568

    • PubMed
    • Google Scholar
25. 1. Day NF
    2. Terleski KL
    3. Nykamp DQ
    4. Nick TA

    (2013) Directed functional connectivity matures with motor learning in a cortical pattern generator

    Journal of Neurophysiology 109:913–923.

    https://doi.org/10.1152/jn.00937.2012

    • PubMed
    • Google Scholar
26. 1. Delevich K
    2. Tucciarone J
    3. Huang ZJ
    4. Li B

    (2015) The mediodorsal thalamus drives feedforward inhibition in the anterior cingulate cortex via parvalbumin interneurons

    The Journal of Neuroscience 35:5743–5753.

    https://doi.org/10.1523/JNEUROSCI.4565-14.2015

    • PubMed
    • Google Scholar
27. 1. Demas J
    2. Manley J
    3. Tejera F
    4. Barber K
    5. Kim H
    6. Traub FM
    7. Chen B
    8. Vaziri A

    (2021) High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy

    Nature Methods 18:1103–1111.

    https://doi.org/10.1038/s41592-021-01239-8

    • PubMed
    • Google Scholar
28. 1. Dimidschstein J
    2. Chen Q
    3. Tremblay R
    4. Rogers SL
    5. Saldi G-A
    6. Guo L
    7. Xu Q
    8. Liu R
    9. Lu C
    10. Chu J
    11. Grimley JS
    12. Krostag A-R
    13. Kaykas A
    14. Avery MC
    15. Rashid MS
    16. Baek M
    17. Jacob AL
    18. Smith GB
    19. Wilson DE
    20. Kosche G
    21. Kruglikov I
    22. Rusielewicz T
    23. Kotak VC
    24. Mowery TM
    25. Anderson SA
    26. Callaway EM
    27. Dasen JS
    28. Fitzpatrick D
    29. Fossati V
    30. Long MA
    31. Noggle S
    32. Reynolds JH
    33. Sanes DH
    34. Rudy B
    35. Feng G
    36. Fishell G

    (2016) A viral strategy for targeting and manipulating interneurons across vertebrate species

    Nature Neuroscience 19:1743–1749.

    https://doi.org/10.1038/nn.4430

    • PubMed
    • Google Scholar
29. 1. Doupe AJ
    2. Kuhl PK

    (1999) Birdsong and human speech: common themes and mechanisms

    Annual Review of Neuroscience 22:567–631.

    https://doi.org/10.1146/annurev.neuro.22.1.567

    • PubMed
    • Google Scholar
30. 1. Faunes M
    2. Wild JM

    (2017) The ascending projections of the nuclei of the descending trigeminal tract (nTTD) in the zebra finch (Taeniopygia guttata)

    The Journal of Comparative Neurology 525:2832–2846.

    https://doi.org/10.1002/cne.24247

    • PubMed
    • Google Scholar
31. 1. Fee MS
    2. Kozhevnikov AA
    3. Hahnloser RHR

    (2004) Neural mechanisms of vocal sequence generation in the songbird

    Annals of the New York Academy of Sciences 1016:153–170.

    https://doi.org/10.1196/annals.1298.022

    • PubMed
    • Google Scholar
32. 1. Fortune ES
    2. Margoliash D

    (1995) Parallel pathways and convergence onto HVc and adjacent neostriatum of adult zebra finches (Taeniopygia guttata)

    The Journal of Comparative Neurology 360:413–441.

    https://doi.org/10.1002/cne.903600305

    • PubMed
    • Google Scholar
33. 1. Foster EF
    2. Bottjer SW

    (2001) Lesions of a telencephalic nucleus in male zebra finches: Influences on vocal behavior in juveniles and adults

    Journal of Neurobiology 01:142–165.

    https://doi.org/10.1002/1097-4695(20010205)46:2<142::AID-NEU60>3.0.CO;2-R

    • Google Scholar
34. 1. Funabiki Y
    2. Konishi M

    (2003) Long memory in song learning by zebra finches

    The Journal of Neuroscience 23:6928–6935.

    https://doi.org/10.1523/JNEUROSCI.23-17-06928.2003

    • PubMed
    • Google Scholar
35. 1. Garcia-Oscos F
    2. Koch TMI
    3. Pancholi H
    4. Trusel M
    5. Daliparthi V
    6. Co M
    7. Park SE
    8. Ayhan F
    9. Alam DH
    10. Holdway JE
    11. Konopka G
    12. Roberts TF

    (2021) Autism-linked gene FoxP1 selectively regulates the cultural transmission of learned vocalizations

    Science Advances 7:eabd2827.

    https://doi.org/10.1126/sciadv.abd2827

    • PubMed
    • Google Scholar
36. 1. Goller F
    2. Cooper BG

    (2004) Peripheral motor dynamics of song production in the zebra finch

    Annals of the New York Academy of Sciences 1016:130–152.

    https://doi.org/10.1196/annals.1298.009

    • PubMed
    • Google Scholar
37. 1. Govorunova EG
    2. Sineshchekov OA
    3. Janz R
    4. Liu X
    5. Spudich J

    (2015) NEUROSCIENCE: Natural light-gated anion channels: A family of microbial rhodopsins for advanced optogenetics

    Science 349:647–650.

    https://doi.org/10.1126/science.aaa7484

    • PubMed
    • Google Scholar
38. 1. Hamaguchi K
    2. Mooney R

    (2012) Recurrent interactions between the input and output of a songbird cortico-basal ganglia pathway are implicated in vocal sequence variability

    The Journal of Neuroscience 32:11671–11687.

    https://doi.org/10.1523/JNEUROSCI.1666-12.2012

    • PubMed
    • Google Scholar
39. 1. Hamaguchi K
    2. Tanaka M
    3. Mooney R

    (2016) A distributed recurrent network contributes to temporally precise vocalizations

    Neuron 91:680–693.

    https://doi.org/10.1016/j.neuron.2016.06.019

    • PubMed
    • Google Scholar
40. 1. Hertrich I
    2. Dietrich S
    3. Ackermann H

    (2020) The margins of the language network in the brain

    Frontiers in Communication 5:e19955.

    https://doi.org/10.3389/fcomm.2020.519955

    • Google Scholar
41. 1. Hickok G
    2. Poeppel D

    (2007) The cortical organization of speech processing

    Nature Reviews. Neuroscience 8:393–402.

    https://doi.org/10.1038/nrn2113

    • PubMed
    • Google Scholar
42. 1. Ikeda MZ
    2. Trusel M
    3. Roberts TF

    (2020) Memory circuits for vocal imitation

    Current Opinion in Neurobiology 60:37–46.

    https://doi.org/10.1016/j.conb.2019.11.002

    • PubMed
    • Google Scholar
43. Book

    1. Immelmann K

    (1969)

    Song development in the zebra finch and other estrildid finches

    In: Hinde RA, editors. Bird Vocalisations. Cambridge University Press. pp. 61–74.

    • Google Scholar
44. 1. Kapfer C
    2. Glickfeld LL
    3. Atallah BV
    4. Scanziani M

    (2007) Supralinear increase of recurrent inhibition during sparse activity in the somatosensory cortex

    Nature Neuroscience 10:743–753.

    https://doi.org/10.1038/nn1909

    • PubMed
    • Google Scholar
45. 1. Kelley DB
    2. Nottebohm F

    (1979) Projections of a telencephalic auditory nucleus-field L-in the canary

    The Journal of Comparative Neurology 183:455–469.

    https://doi.org/10.1002/cne.901830302

    • PubMed
    • Google Scholar
46. 1. Khirug S
    2. Yamada J
    3. Afzalov R
    4. Voipio J
    5. Khiroug L
    6. Kaila K

    (2008) GABAergic depolarization of the axon initial segment in cortical principal neurons is caused by the Na-K-2Cl cotransporter NKCC1

    The Journal of Neuroscience 28:4635–4639.

    https://doi.org/10.1523/JNEUROSCI.0908-08.2008

    • PubMed
    • Google Scholar
47. 1. Konishi M

    (1989) Birdsong for neurobiologists

    Neuron 3:541–549.

    https://doi.org/10.1016/0896-6273(89)90264-x

    • PubMed
    • Google Scholar
48. 1. Konopka G
    2. Roberts TF

    (2016) Insights into the neural and genetic basis of vocal communication

    Cell 164:1269–1276.

    https://doi.org/10.1016/j.cell.2016.02.039

    • PubMed
    • Google Scholar
49. 1. Koparkar A
    2. Warren TL
    3. Charlesworth JD
    4. Shin S
    5. Brainard MS
    6. Veit L

    (2024) Lesions in a songbird vocal circuit increase variability in song syntax

    eLife 13:RP93272.

    https://doi.org/10.7554/eLife.93272

    • PubMed
    • Google Scholar
50. 1. Kosche G
    2. Vallentin D
    3. Long MA

    (2015) Interplay of inhibition and excitation shapes a premotor neural sequence

    The Journal of Neuroscience 35:1217–1227.

    https://doi.org/10.1523/JNEUROSCI.4346-14.2015

    • PubMed
    • Google Scholar
51. 1. Linders LE
    2. Supiot LF
    3. Du W
    4. D’Angelo R
    5. Adan RAH
    6. Riga D
    7. Meye FJ

    (2022) Studying synaptic connectivity and strength with optogenetics and patch-clamp electrophysiology

    International Journal of Molecular Sciences 23:11612.

    https://doi.org/10.3390/ijms231911612

    • PubMed
    • Google Scholar
52. 1. Louder MIM
    2. Kuroda M
    3. Taniguchi D
    4. Komorowska-Müller JA
    5. Morohashi Y
    6. Takahashi M
    7. Sánchez-Valpuesta M
    8. Wada K
    9. Okada Y
    10. Hioki H
    11. Yazaki-Sugiyama Y

    (2024) Transient sensorimotor projections in the developmental song learning period

    Cell Reports 43:114196.

    https://doi.org/10.1016/j.celrep.2024.114196

    • PubMed
    • Google Scholar
53. 1. Mackevicius EL
    2. Happ MTL
    3. Fee MS

    (2020) An avian cortical circuit for chunking tutor song syllables into simple vocal-motor units

    Nature Communications 11:5029.

    https://doi.org/10.1038/s41467-020-18732-x

    • PubMed
    • Google Scholar
54. 1. Malyshev AY
    2. Roshchin MV
    3. Smirnova GR
    4. Dolgikh DA
    5. Balaban PM
    6. Ostrovsky MA

    (2017) Chloride conducting light activated channel GtACR2 can produce both cessation of firing and generation of action potentials in cortical neurons in response to light

    Neuroscience Letters 640:76–80.

    https://doi.org/10.1016/j.neulet.2017.01.026

    • PubMed
    • Google Scholar
55. 1. Manley J
    2. Lu S
    3. Barber K
    4. Demas J
    5. Kim H
    6. Meyer D
    7. Traub FM
    8. Vaziri A

    (2024) Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number

    Neuron 112:1694–1709.

    https://doi.org/10.1016/j.neuron.2024.02.011

    • PubMed
    • Google Scholar
56. 1. Mardinly AR
    2. Oldenburg IA
    3. Pégard NC
    4. Sridharan S
    5. Lyall EH
    6. Chesnov K
    7. Brohawn SG
    8. Waller L
    9. Adesnik H

    (2018) Precise multimodal optical control of neural ensemble activity

    Nature Neuroscience 21:881–893.

    https://doi.org/10.1038/s41593-018-0139-8

    • PubMed
    • Google Scholar
57. 1. Messier JE
    2. Chen H
    3. Cai ZL
    4. Xue M

    (2018) Targeting light-gated chloride channels to neuronal somatodendritic domain reduces their excitatory effect in the axon

    eLife 7:e38506.

    https://doi.org/10.7554/eLife.38506

    • PubMed
    • Google Scholar
58. 1. Moll FW
    2. Kranz D
    3. Corredera Asensio A
    4. Elmaleh M
    5. Ackert-Smith LA
    6. Long MA

    (2023) Thalamus drives vocal onsets in the zebra finch courtship song

    Nature 616:132–136.

    https://doi.org/10.1038/s41586-023-05818-x

    • PubMed
    • Google Scholar
59. 1. Mooney R
    2. Prather JF

    (2005) The HVC microcircuit: the synaptic basis for interactions between song motor and vocal plasticity pathways

    The Journal of Neuroscience 25:1952–1964.

    https://doi.org/10.1523/JNEUROSCI.3726-04.2005

    • PubMed
    • Google Scholar
60. 1. Mooney R

    (2009) Neural mechanisms for learned birdsong

    Learning & Memory 16:655–669.

    https://doi.org/10.1101/lm.1065209

    • PubMed
    • Google Scholar
61. 1. Nottebohm F
    2. Stokes TM
    3. Leonard CM

    (1976) Central control of song in the canary, Serinus canarius

    The Journal of Comparative Neurology 165:457–486.

    https://doi.org/10.1002/cne.901650405

    • PubMed
    • Google Scholar
62. 1. Nottebohm F
    2. Kelley DB
    3. Paton JA

    (1982) Connections of vocal control nuclei in the canary telencephalon

    The Journal of Comparative Neurology 207:344–357.

    https://doi.org/10.1002/cne.902070406

    • PubMed
    • Google Scholar
63. 1. Opitz A
    2. Falchier A
    3. Linn GS
    4. Milham MP
    5. Schroeder CE

    (2017) Limitations of ex vivo measurements for in vivo neuroscience

    PNAS 114:5243–5246.

    https://doi.org/10.1073/pnas.1617024114

    • PubMed
    • Google Scholar
64. 1. Otchy TM
    2. Wolff SBE
    3. Rhee JY
    4. Pehlevan C
    5. Kawai R
    6. Kempf A
    7. Gobes SMH
    8. Ölveczky BP

    (2015) Acute off-target effects of neural circuit manipulations

    Nature 528:358–363.

    https://doi.org/10.1038/nature16442

    • PubMed
    • Google Scholar
65. 1. Petreanu L
    2. Mao T
    3. Sternson SM
    4. Svoboda K

    (2009) The subcellular organization of neocortical excitatory connections

    Nature 457:1142–1145.

    https://doi.org/10.1038/nature07709

    • PubMed
    • Google Scholar
66. 1. Pinto DJ
    2. Brumberg JC
    3. Simons DJ

    (2000) Circuit dynamics and coding strategies in rodent somatosensory cortex

    Journal of Neurophysiology 83:1158–1166.

    https://doi.org/10.1152/jn.2000.83.3.1158

    • PubMed
    • Google Scholar
67. 1. Pinto DJ
    2. Hartings JA
    3. Brumberg JC
    4. Simons DJ

    (2003) Cortical damping: analysis of thalamocortical response transformations in rodent barrel cortex

    Cerebral Cortex 13:33–44.

    https://doi.org/10.1093/cercor/13.1.33

    • PubMed
    • Google Scholar
68. 1. Poeppel D
    2. Emmorey K
    3. Hickok G
    4. Pylkkänen L

    (2012) Towards a new neurobiology of language

    The Journal of Neuroscience 32:14125–14131.

    https://doi.org/10.1523/JNEUROSCI.3244-12.2012

    • PubMed
    • Google Scholar
69. 1. Price PH

    (1979) Developmental determinants of structure in zebra finch song

    Journal of Comparative and Physiological Psychology 93:260–277.

    https://doi.org/10.1037/h0077553

    • Google Scholar
70. 1. Roberts TF
    2. Klein ME
    3. Kubke MF
    4. Wild JM
    5. Mooney R

    (2008) Telencephalic neurons monosynaptically link brainstem and forebrain premotor networks necessary for song

    The Journal of Neuroscience 28:3479–3489.

    https://doi.org/10.1523/JNEUROSCI.0177-08.2008

    • PubMed
    • Google Scholar
71. 1. Roberts TF
    2. Gobes SMH
    3. Murugan M
    4. Ölveczky BP
    5. Mooney R

    (2012) Motor circuits are required to encode a sensory model for imitative learning

    Nature Neuroscience 15:1454–1459.

    https://doi.org/10.1038/nn.3206

    • PubMed
    • Google Scholar
72. 1. Roberts TF
    2. Hisey E
    3. Tanaka M
    4. Kearney MG
    5. Chattree G
    6. Yang CF
    7. Shah NM
    8. Mooney R

    (2017) Identification of a motor-to-auditory pathway important for vocal learning

    Nature Neuroscience 20:978–986.

    https://doi.org/10.1038/nn.4563

    • PubMed
    • Google Scholar
73. 1. Sánchez-Valpuesta M
    2. Suzuki Y
    3. Shibata Y
    4. Toji N
    5. Ji Y
    6. Afrin N
    7. Asogwa CN
    8. Kojima I
    9. Mizuguchi D
    10. Kojima S
    11. Okanoya K
    12. Okado H
    13. Kobayashi K
    14. Wada K

    (2019) Corticobasal ganglia projecting neurons are required for juvenile vocal learning but not for adult vocal plasticity in songbirds

    PNAS 116:22833–22843.

    https://doi.org/10.1073/pnas.1913575116

    • PubMed
    • Google Scholar
74. 1. Schmidt MF
    2. Ashmore RC
    3. Vu ET

    (2004) Bilateral control and interhemispheric coordination in the avian song motor system

    Annals of the New York Academy of Sciences 1016:171–186.

    https://doi.org/10.1196/annals.1298.014

    • PubMed
    • Google Scholar
75. 1. Schmidt MF
    2. Goller F

    (2016) Breathtaking songs: coordinating the neural circuits for breathing and singing

    Physiology 31:442–451.

    https://doi.org/10.1152/physiol.00004.2016

    • PubMed
    • Google Scholar
76. 1. Silberberg G
    2. Markram H

    (2007) Disynaptic inhibition between neocortical pyramidal cells mediated by Martinotti cells

    Neuron 53:735–746.

    https://doi.org/10.1016/j.neuron.2007.02.012

    • PubMed
    • Google Scholar
77. 1. Simpson HB
    2. Vicario DS

    (1990) Brain pathways for learned and unlearned vocalizations differ in zebra finches

    The Journal of Neuroscience 10:1541–1556.

    https://doi.org/10.1523/JNEUROSCI.10-05-01541.1990

    • PubMed
    • Google Scholar
78. 1. Sossinka R
    2. Bohner J

    (1980) Song types in the zebra finch Poephila guttata castanotis

    Zeitschrift Fur Tierpsychologie 53:123–132.

    https://doi.org/10.1111/j.1439-0310.1980.tb01044.x

    • Google Scholar
79. 1. Stacho M
    2. Herold C
    3. Rook N
    4. Wagner H
    5. Axer M
    6. Amunts K
    7. Güntürkün O

    (2020) A cortex-like canonical circuit in the avian forebrain

    Science 369:eabc5534.

    https://doi.org/10.1126/science.abc5534

    • PubMed
    • Google Scholar
80. 1. Stauffer TR
    2. Elliott KC
    3. Ross MT
    4. Basista MJ
    5. Hyson RL
    6. Johnson F

    (2012) Axial organization of a brain region that sequences a learned pattern of behavior

    The Journal of Neuroscience 32:9312–9322.

    https://doi.org/10.1523/JNEUROSCI.0978-12.2012

    • PubMed
    • Google Scholar
81. 1. Steinmetz NA
    2. Aydin C
    3. Lebedeva A
    4. Okun M
    5. Pachitariu M
    6. Bauza M
    7. Beau M
    8. Bhagat J
    9. Böhm C
    10. Broux M
    11. Chen S
    12. Colonell J
    13. Gardner RJ
    14. Karsh B
    15. Kloosterman F
    16. Kostadinov D
    17. Mora-Lopez C
    18. O’Callaghan J
    19. Park J
    20. Putzeys J
    21. Sauerbrei B
    22. van Daal RJJ
    23. Vollan AZ
    24. Wang S
    25. Welkenhuysen M
    26. Ye Z
    27. Dudman JT
    28. Dutta B
    29. Hantman AW
    30. Harris KD
    31. Lee AK
    32. Moser EI
    33. O’Keefe J
    34. Renart A
    35. Svoboda K
    36. Häusser M
    37. Haesler S
    38. Carandini M
    39. Harris TD

    (2021) Neuropixels 2.0: a miniaturized high-density probe for stable, long-term brain recordings

    Science 372:eabf4588.

    https://doi.org/10.1126/science.abf4588

    • PubMed
    • Google Scholar
82. 1. Tanaka M
    2. Sun F
    3. Li Y
    4. Mooney R

    (2018) A mesocortical dopamine circuit enables the cultural transmission of vocal behaviour

    Nature 563:117–120.

    https://doi.org/10.1038/s41586-018-0636-7

    • PubMed
    • Google Scholar
83. 1. Tchernichovski O
    2. Lints T
    3. Mitra PP
    4. Nottebohm F

    (1999) Vocal imitation in zebra finches is inversely related to model abundance

    PNAS 96:12901–12904.

    https://doi.org/10.1073/pnas.96.22.12901

    • PubMed
    • Google Scholar
84. 1. Tchernichovski O
    2. Mitra PP
    3. Lints T
    4. Nottebohm F

    (2001) Dynamics of the vocal imitation process: how a zebra finch learns its song

    Science 291:2564–2569.

    https://doi.org/10.1126/science.1058522

    • PubMed
    • Google Scholar
85. 1. Tremblay R
    2. Lee S
    3. Rudy B

    (2016) GABAergic interneurons in the neocortex: from cellular properties to circuits

    Neuron 91:260–292.

    https://doi.org/10.1016/j.neuron.2016.06.033

    • PubMed
    • Google Scholar
86. 1. Vallentin D
    2. Kosche G
    3. Lipkind D
    4. Long MA

    (2016) Neural circuits: Inhibition protects acquired song segments during vocal learning in zebra finches

    Science 351:267–271.

    https://doi.org/10.1126/science.aad3023

    • PubMed
    • Google Scholar
87. 1. Vates GE
    2. Broome BM
    3. Mello CV
    4. Nottebohm F

    (1996) Auditory pathways of caudal telencephalon and their relation to the song system of adult male zebra finches

    The Journal of Comparative Neurology 366:613–642.

    https://doi.org/10.1002/(SICI)1096-9861(19960318)366:4<613::AID-CNE5>3.0.CO;2-7

    • PubMed
    • Google Scholar
88. 1. Vu ET
    2. Mazurek ME
    3. Kuo YC

    (1994) Identification of a forebrain motor programming network for the learned song of zebra finches

    The Journal of Neuroscience 14:6924–6934.

    https://doi.org/10.1523/JNEUROSCI.14-11-06924.1994

    • PubMed
    • Google Scholar
89. 1. Vu ET
    2. Schmidt MF
    3. Mazurek ME

    (1998) Interhemispheric coordination of premotor neural activity during singing in adult zebra finches

    The Journal of Neuroscience 18:9088–9098.

    https://doi.org/10.1523/JNEUROSCI.18-21-09088.1998

    • PubMed
    • Google Scholar
90. 1. Wild JM

    (1994) Visual and somatosensory inputs to the avian song system via nucleus uvaeformis (Uva) and a comparison with the projections of a similar thalamic nucleus in a nonsongbird, Columba livia

    The Journal of Comparative Neurology 349:512–535.

    https://doi.org/10.1002/cne.903490403

    • PubMed
    • Google Scholar
91. 1. Wild JM
    2. Krützfeldt NOE
    3. Kubke MF

    (2010) Connections of the auditory brainstem in a songbird, Taeniopygia guttata. III. Projections of the superior olive and lateral lemniscal nuclei

    The Journal of Comparative Neurology 518:2149–2167.

    https://doi.org/10.1002/cne.22325

    • PubMed
    • Google Scholar
92. 1. Wild JM
    2. Gaede AH

    (2016) Second tectofugal pathway in a songbird (Taeniopygia guttata) revisited: Tectal and lateral pontine projections to the posterior thalamus, thence to the intermediate nidopallium

    The Journal of Comparative Neurology 524:963–985.

    https://doi.org/10.1002/cne.23886

    • PubMed
    • Google Scholar
93. 1. Xiao L
    2. Chattree G
    3. Oscos FG
    4. Cao M
    5. Wanat MJ
    6. Roberts TF

    (2018) A basal ganglia circuit sufficient to guide birdsong learning

    Neuron 98:208–221.

    https://doi.org/10.1016/j.neuron.2018.02.020

    • PubMed
    • Google Scholar
94. 1. Zann RA

    (1984) Structural variation in the zebra finch distance call

    Zeitschrift Für Tierpsychologie 66:328–345.

    https://doi.org/10.1111/j.1439-0310.1984.tb01372.x

    • Google Scholar
95. Book

    1. Zann RA

    (1996) The Zebra Finch: A Synthesis of Field and Laboratory Studies

    Oxford University Press.

    https://doi.org/10.1093/oso/9780198540793.001.0001

    • Google Scholar
96. 1. Zhao W
    2. Garcia-Oscos F
    3. Dinh D
    4. Roberts TF

    (2019) Inception of memories that guide vocal learning in the songbird

    Science 366:83–89.

    https://doi.org/10.1126/science.aaw4226

    • PubMed
    • Google Scholar

Author details

1. Massimo Trusel

   Department of Neuroscience, UT Southwestern Medical Center, Dallas, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   Massimo.trusel@utsouthwestern.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6208-2476

2. Ziran Zhao

   Department of Neuroscience, UT Southwestern Medical Center, Dallas, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Danyal H Alam

   Department of Neuroscience, UT Southwestern Medical Center, Dallas, United States

   Contribution

   Resources, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Ethan S Marks

   Department of Neuroscience, UT Southwestern Medical Center, Dallas, United States

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

5. Maaya Z Ikeda

   Department of Neuroscience, UT Southwestern Medical Center, Dallas, United States

   Contribution

   Investigation, Visualization

   Competing interests

   No competing interests declared

6. Todd F Roberts

   Department of Neuroscience, UT Southwestern Medical Center, Dallas, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review and editing

   For correspondence

   todd.roberts@utsouthwestern.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0967-6598

• 648

  views

• 33

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.