A Distributed Recurrent Network Contributes to Temporally Precise Vocalizations

Abstract

How do forebrain and brainstem circuits interact to produce temporally precise and reproducible behaviors? Birdsong is an elaborate, temporally precise, and stereotyped vocal behavior controlled by a network of forebrain and brainstem nuclei. An influential idea is that song premotor neurons in a forebrain nucleus (HVC) form a synaptic chain that dictates song timing in a top-down manner. Here we combine physiological, dynamical, and computational methods to show that song timing is not generated solely by a mechanism localized to HVC but instead is the product of a distributed and recurrent synaptic network spanning the forebrain and brainstem, of which HVC is a component.

Figure 1. Conceptual models of song timing generation

A, A localized model in which song timing is generated solely within HVC (left) contrasts with one in which song timing is generated by a recurrent, distributed including HVC and motor-thalamic loop (RA, brainstem vocal respiratory group and the thalamic song nucleus Uva (right). B, Predicted effect of HVC cooling on song timing employing a local mechanism. The physiological Q₁₀ (~2) and behavioral Q₁₀ are predicted to match. C, Predicted effect of HVC cooling on song timing for the distributed mechanism. The behavioral Q₁₀ will be much smaller than the physiological Q₁₀ measured for activity propagation in HVC.

Figure 2. Temperature exerts markedly different effects on synaptic transmission in HVC and on song timing

A, Schematic diagram of HVC surface cooling. B, A typical example of the effects of bilateral HVC cooling on song timing. C, Typical examples of temperature dependence of syllable and gap durations measured from one adult male zebra finch. Both syllables and gaps slow 20-40% per 10 °C (Q ₁₀ = 1.2-1.4). D, Motif, the typical syllable sequence sang by a zebra finch, also stretches to the similar extent (n = 3 birds, Q₁₀ = 1.25 ± 0.03). E, Schematic diagram of the experimental setup to measure local HVC_RA - HVC_RA interactions. F, An example of the effect of HVC surface cooling on synaptic transmission between HVC_RA cells (ΔT = 0 °C (red), ΔT = - 4 °C (black), ΔT = -9 °C (blue)). Thick solid lines are trial aver aged membrane potential trace, thin lines are individual traces; arrowheads mark average synaptic potential onsets at different temperatures. G, Cooling HVC slows synaptic transmission between HVC_RA neurons by 75-300% per 10 °C. Synaptic onset laten cies (n=9 HVC_RA): 2.9 ± 0.5 ms at ~40 °C versus 5.7 ± 0.6 ms at ~32 °C). Note the difference of y-axis scales in Figure 2C and 2F, 2I. H, Schematic diagram of the experimental setup to measure orthordomically evoked latencies. I, Examples of typical dilation effect of HVC surface cooling on the action potential responses to stimulation of HVC afferents (Uva, n = 6; NIf, n = 1). J, Manipulating HVC temperature increases action potential latencies (n = 3 HVC_RA, n = 4 HVC_X); 5.8 ± 0.8 ms at ~40 °C versus 8.6 ± 0.8 ms at ~3 2 °C, mean ± SEM. Orange hatched region represents the range of Q₁₀ of syllables and gaps; 1.27 ± 0.14 (mean ± SD).

Figure 3. Temperature exerts highly similar effects on synaptic transmission through a distributed recurrent network and on song tempo

A, Schematic diagram of the experimental setup to measure recurrent network interactions. The minimum number of synapses mediating inter-hemispheric (red pathway) and intra-hemispheric HVC interactions (arrows enclosed within a single hemisphere) is the same (four). B, Typical examples of the effects of bilateral HVC surface cooling on the action potential (Top) and synaptic responses (Bottom) evoked in HVC by contralateral HVC stimulation. Scale bar in the inset is 5 ms × 5 mV. C, Manipulating HVC temperature bilaterally slows inter-hemispheric activity transmission. Synaptic latencies (circles): 18.5 ± 0.7 ms at ~38 °C versus 19.2 ± 0.6 ms at ~32 °C. Action p otential (AP) latencies (asterisks): 23.3 ± 2.3 ms at ~38 °C versus 25.6 ± 2.0 ms at 32 °C. (n= 8 HVC RA, n = 5 HVC_X neurons; n = 13 synaptic onsets, n = 5 APs). Orange hatched region; same as Figure 2C,F,I. Synaptic latencies (circles); action potentials latencies (asterisks). D, Q₁₀ values of song timing (syllables (circles); gaps (asterisks)) are significantly different from Q₁₀ values of HVC local interactions (P < 0.01), but not significantly different from Q₁₀ values of recurrent network interaction (P = 0.25). t-test, ** P < 0.01.

Figure 4. Cooling the thalamic nucleus Uva slows song tempo in a manner consistent with a distributed timing mechanism

A, Effect of HVC cooling on other song nuclei; HVC cooling did not change Uva temperature. B, Schematic diagram of deep brain cooling targeting Uva. C, An example of a histological reconstruction of the probe tip position near Uva, labeled with retrograde tracer injection into HVC. D, Temperature changes measured in HVC and near Uva caused by Uva cooling (filled/open circles, with/without extended heat sink) and by HVC cooling (squares). E, Effect of cooling Uva bilaterally on song timing using the simple Peltier device. F, G, Two examples showing the effect on song timing of cooling Uva bilaterally using the modified Peltier device in which an extended heat sink offsets the cooling effects of the Uva cooling probe on HVC temperature. H, Dilation effects plotted as a function of ΔT(HVC). The dilation effects induced by Uva cooling largely fall outside of the range calculated from HVC cooling (red dashed lines: 1SD region). Same symbols are used as in D. I, Dilation effects plotted as a function of ΔT(Uva) showing that changes in Uva temperature also significantly affected song timing.

Figure 5. Linear regression analysis revealed significant contribution from Uva on song timing control

A, Linear regression analysis of the motif dilation. The slope of the regression plane is indicated by a vector (black arrow). B, The angle θ of the slope vector represents the contributions from HVC and Uva. C, The slope vectors for motif, syllables, and gaps, which are shown with equivalent lengths, were well separated from both the HVC and Uva axes (p<0.01), indicating that both HVC and Uva contribute significantly to song timing. (Motif: θ = -0.65 π ± 0.02, ratio (HVC/Uva contribution) = 1.90 ± 0.33; Syllable: θ = -0.67 π ± 0.09, ratio = 1.80 ± 1.05, Gap: θ = -0.70 π ± 0.07, ratio = 1.36 ± 0.71 (mean ± 3SD). The slope vectors were closer to the HVC temperature axis (vertical dashed line) than the Uva temperature axis (horizontal dashed line), suggesting that the contribution to song timing from HVC is relatively stronger than that from Uva. Gray shaded region; 2SD region calculated using a bootstrapping method.

Figure 6. Simulations of local and distributed chain models can generate sparse sequential activity, but with different levels of correlated synaptic activity in HVC

A, Schematic showing the structure of the distributed chain network: The excitatory neurons are grouped into four pools, representing HVC, RA, VRG, and Uva. Excitatory neurons form feedforward connections to the next group. The excitatory neurons and inhibitory neurons also form random recurrent connections within the group. F is the ratio of active neurons engaged in the chain of action potential activity. C_E (βC_E) is the number of presynaptic excitatory (inhibitory) neurons per single neuron in the form of random connections. B, C, Spike rastergrams (B) and synaptic onset rastergrams (C) of 20 randomly selected neurons, constructed from 5 runs of simulations. Only HVC neurons are shown. D, Population averaged synaptic onset activity calculated from half of the population (red, n=10) and from the other half of the population (blue, n=10). In the distributed chain model, the temporal profiles of population synaptic activity are highly similar. E, Local chain model: Groups of excitatory neurons form feedforward connections (FF) and excitatory and inhibitory neurons also form random recurrent connections. F, G, same as B, C. H, Population averaged synaptic onset activity calculated from each half of the randomly selected 20 neurons. In the local chain model, there is no obvious temporal structure common in the two groups of neurons. I, Left: Synaptic correlation (the correlation coefficient of the simulated synaptic onset patterns) of pairs of neurons revealed significant non-zero correlations in the distributed chain model (P < 0.001), but not in the local chain model (P = 0.66). Right: Population synaptic correlation (the correlation of population averaged synaptic event rates) of two randomly selected groups of neurons in either the distributed chain model (P < 0.001) or the local chain model (P = 0.73). Data were generated from the average of 5 realizations of networks, each containing 5 runs of simulations. t-test. J, Magnitude-squared coherence spectrum of the synaptic event rate calculated from pairs of simulated neurons (n = 10 cells). Neurons in the distributed chain model exhibited a clear peak around ~40 - 50 Hz reflecting the periodic occurrence of synaptic inputs at ~20 - 25 msec intervals. In contrast, a coherence analysis of neurons in the local chain model revealed no clear indication of periodicity within the 0 - 100 Hz range.

Figure 7. Examples of intracellular recordings of HVC PNs in singing birds

A, Schematic of sharp intracellular recordings in singing birds. B, Left: Schematic diagram of song motor pathway (red) and a part of the basal ganglia pathway (blue; Area X). Right: An example of antidromic identification of an HVC_RA neuron using a spike collision test. C, An example of synaptic and action potential activity of an identified HVC_RA neuron. Top: sonogram, Middle: membrane potential, Bottom: enlarged and overlaid membrane potential traces from two consecutive motifs reveal highly stereotyped synaptic activity. D, An example of synaptic and action potential activity of a putative HVC_X neuron recorded in another bird; overlaid membrane potential traces from two consecutive motifs also reveal stereotyped synaptic activity.

Figure 8. Correlated, high frequency synaptic activity underlies temporally sparse, sequential action potential patterns in HVC

A, Top: sonogram of a single motif. Bottom: Examples of precisely timed membrane potential traces from different HVC PNs and a cell outside of HVC (HVC shelf) during singing; membrane potential records from two motif renditions are shown (light versus dark color), with different colors corresponding to different cells (HVC_RA (3) and HVC_RA (6) corresponds to cell #3 and #6 in Figure 8B). Occurrences of correlated synaptic onset timings are highlighted with vertical dashed lines. B, Motif-aligned sequential action potentials (middle) and synaptic onsets (bottom) in six identified HVC_RA neurons recorded from the same zebra finch; neurons with at least ten motif renditions are selected, with different colors depicting different neurons. C, Synaptic event timings between individual HVC projection neurons are all correlated (significantly non-zero); HVC_RA - HVC_RA pairs (red; Correlation Coefficient (CC) = 0.17 ± 0.04; n = 25 pairs, P < 0.001, t-test compared with shuffled data); HVC_X - HVC_X pairs (blue; CC = 0.11 ± 0.01; n = 177 pairs, P < 0.001); HVC_RA – HVC_X pairs (purple; CC = 0.14 ± 0.02; n = 137 pairs, t-test, P < 0.001). No significant difference was detected between different PN pairs (P > 0.15). No significant correlation in synaptic activity was detected between cell pairs involving HVC_RA cells and cells outside of HVC (Shelf, gray; CC = -0.02 ± 0.03; n = 15 pairs, P = 0.32). Dashed lines: 3SD from shuffled data. Error bars: SE. D, Averaged synaptic event rates obtained from the same bird showed striking similarity of input timings between two the projection neuron classes (HVC_RA and HVC_X cells). E, The temporal structure of synaptic input timings are highly similar in HVC_RA and HVC_X populations (purple; HVC_RA – HVC_X groups; n = 4 birds, CC = 0.41 ± 0.13, P < 0.001) but not in HVC_RA and Shelf populations (gray; HVC_RA – HVC shelf groups; n = 4 birds, CC = 0.01 ± 0.07, P = 0.95). Dashed lines: 3SD generated from shuffled data. F, Left: the magnitude-squared coherence spectrum calculated from pairs of HVC neurons (same color codes as in C) showed a broad peak ~ 50 Hz, which was not evident in HVC_RA - HVC shelf neuron pairs. Right: grouped average of magnitude-squared coherence calculated within HVC projection neurons (red) and between HVC projection neurons and HVC shelf neurons (black). G,H, Synaptic events precede sound onsets but not offsets in both HVC_RA and HVC_X cell types. dPSP peak at -37.6 ± 0.48 ms (n=21 HVC_RA), -33.1 ± 0.15 ms (n=95 HVC_X). Dashed lines are 2 SD of dPSP rate. A subset of HVC_X cell data was presented in [Hamaguchi and Mooney 2014].