Figures and data in Biophysical network modeling of temporal and stereotyped sequence propagation of neural activity in the premotor nucleus HVC

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13 figures, 1 table and 4 additional files

Figures

Figure 1

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Song system overview and general intrinsic and network properties of HVC neurons in vivo and in vitro.

(A) Schematic diagram showing a sagittal view of the male zebra finch song system. The vocal motor pathway (VMP, red color) contains circuits that directly pattern song output. The anterior forebrain loop (AFP, blue color) pathway contains circuits that are important for song learning and plasticity. (B) HVC includes multiple classes of neurons; HVC_X neurons that project to area X (blue), HVC_RA neurons that project to nucleus RA (red), and HVC interneurons (HVC_INT, black). HVC_X and HVC_RA excite HVC_INT via AMPA and NMDA synapses (green arrow), while HVC_INT neurons inhibit both classes of projecting neurons via GABA synapses (brown arrows with circle heads). Each class of HVC neurons is characterized by its own family of ionic currents (Daou et al., 2013). (C) HVC_RA neurons exhibit a very sparse activity during singing eliciting a single 4–6 ms burst at a single and exact moment in time during each rendition of the song. On the contrary, HVC interneurons burst densely throughout the song (Adapted from Hahnloser et al., 2002). (D) Similar to HVC_RA, HVC_X neurons generate 1–4 bursts that are time-locked and highly stereotyped from one rendition of the song to another (Adapted from Kozhevnikov and Fee, 2007).

© 2002, Nature. Figure 1C is reprinted from Figure 2B from Hahnloser et al., 2002, with permission from Nature. It is not covered by the CC-BY 4.0 licence and further reproduction of this panel would need permission from the copyright holder.

© 2007, American Physiological Society. Figure 1D is reprinted from Figure 2A from Kozhevnikov and Fee, 2007, with permission from American Physiological Society. It is not covered by the CC-BY 4.0 licence and further reproduction of this panel would need permission from the copyright holder.

Figure 2

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Model output compared to experimental results obtained by Mooney and Prather, 2005.

DC-evoked action potentials in HVC_RA neurons trigger iPSPs in HVC_X neurons (A1) as well as fast dPSPs in HVC_INT neurons (B1). Brief (~10 ms) depolarizing current pulses (0.5 nA) applied to model HVC_RA neuron (same values used as by Mooney and Prather, 2005) evoke similar responses in the corresponding model HVC_X (A2) and model HVC_INT (B2) neurons. DC-evoked action potentials in HVC_INT neurons generate fast iPSPs in HVC_X neurons (**C1, D1**). Similar responses were elicited in model HVC_X neurons when model HVC_INT neuron was stimulated by brief (10 ms) depolarizing pulses (0.5 nA) (C2) or when it was given a DC-pulse of 0.5 nA for 500ms (D2). Finally, HVC_INT neurons elicit fast dPSPs when HVC_X neurons are injected with 10 ms pulses of 0.5 nA current (E1), which was simulated in the model (E2). In this and subsequent figures (unless otherwise specified), HVC_X neurons’ traces are represented in blue, HVC_RA neurons in red, and HVC_INT neurons in black. Panels in the left column are adapted from Mooney and Prather, 2005.

© 2005, Society for Neuroscience. Panels A1, B1, C1, D1 and E1 are reproduced from Mooney and Prather, 2005, with permission. It is not covered by the CC-BY 4.0 licence.

Figure 2—source code 1 MATLAB code that contains the underlying ODEs and parameters for the individual neurons connected in the network.: https://cdn.elifesciences.org/articles/105526/elife-105526-fig2-code1-v1.zip
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Figure 2—source code 2 MATLAB code that contains all underlying ODEs, equations and corresponding parameters for the interneurons used to simulate this network.: https://cdn.elifesciences.org/articles/105526/elife-105526-fig2-code2-v1.zip
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Figure 2—source code 3 MATLAB code that contains all underlying ODEs, equations and corresponding parameters for the X-projecting neuron used to simulate this network.: https://cdn.elifesciences.org/articles/105526/elife-105526-fig2-code3-v1.zip
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Figure 2—source code 4 MATLAB code that contains all underlying ODEs, equations and corresponding parameters for the RA-projecting neuron used to simulate this network.: https://cdn.elifesciences.org/articles/105526/elife-105526-fig2-code4-v1.zip
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Figure 3

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Cartoon diagram illustrating the network architecture configuration.

(A) Each gray oval represents a microcircuit encoding for a sub-syllabic segment (SSS). The number of microcircuits is envisioned to be equal to the number of SSSs representing the song. Each microcircuit contains a number of HVC_RA, HVC_X, and HVC_INT neurons selected randomly from the total pool of neurons (see text). (B) Within each microcircuit, HVC_RA neurons are connected to each other in a chain-like mode and they send excitatory afferents to HVC_INT neurons in the same and other microcircuits, selected randomly. HVC_INT neurons send GABAergic synapses onto HVC_X neurons in the same microcircuit only as well as to HVC_RA neurons in any other random microcircuit except the microcircuit they belong to. Finally, HVC_X neurons send excitatory afferents to HVC_INT neurons in the same microcircuit. Activity starts by a small DC pulse to the first HVC_RA neuron in the first microcircuit. Activity propagates from one microcircuit to another by excitatory coupling between the last HVC_RA neuron in microcircuit i and the first HVC_RA neuron in microcircuit i+1. (C) During singing, the propagation of activity unfolds across the chain of microcircuits, such that neurons belonging to microcircuit x get activated and encode for SSS_x.

Figure 4

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Spiking patterns of 120 HVC_RA neurons (labeled with numbers) showing the propagation of sequential activity.

The neural traces are aligned by the acoustic elements of a spectrogram from an exemplar bird’s song illustrating the firing of HVC_RA neurons with respect to ongoing part of a song. The inset shows a zoomed version of two subsequent HVC_RA neurons firing patterns illustrating the delay between their individual bursts.

Figure 5

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Effects of the AMPA synaptic conductance and A-type K⁺ conductance on the delay between two successive HVC_RA bursts.

(A) Presynaptic model HVC_RA neuron (top, black) is connected to a postsynaptic model HVC_RA neuron and the corresponding AMPA excitatory conductance ( $g_{A M P A}$ ) was increased from 10 nS (bottom, red) to 12 nS (bottom, blue), while keeping all other parameters fixed. Increasing $g_{A M P A}$ reduces the delay between the peaks of the pre- and post- HVC_RA’s first spikes and increases the number of spikes in the postsynaptic neuron. (B) Larger magnitudes of the A-type K⁺ conductance ( $g_{A}$ ) leads to longer delays to spiking. While keeping all intrinsic and synaptic parameters fixed ( $g_{A M P A}$ +10), increasing $g_{A}$ from 10 nS (bottom, red) to 13 nS (bottom, blue) delayed the onset to spiking and reduced the number of spikes. Bars on the top show the duration in ms between the peak of the first action potential in the presynaptic neuron to the peak of the first action potential in the postsynaptic neuron.

Figure 6

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Intrinsic changes in HVC_RA halt the propagation of sequential activity.

Up-regulating the A-type K⁺ current (A) or the Ca²⁺ - dependent K⁺ current (B) in exemplar neurons ( ${H V C}_{R A}^{60}$ , A) or ( ${H V C}_{R A}^{80}$ *, B*), by increasing $g_{A}$ + (A), or $g_{S K}$ from 15-fold nS (B), reduces the excitability of corresponding HVC_RA neuron markedly, eliminating its corresponding burst and breaking the sequence.

Figure 7

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Activity patterns for 10 HVC_INT and 10 HVC_X neurons are illustrated.

(A) HVC interneurons display dense spiking and bursting throughout the song, due to the dense HVC_RA –_-HVC_INT and HVC_X – HVC_INT excitatory coupling (Figure 3). (B) HVC_X neurons display 2–4 rebound bursts that vary in their strength and duration due to HVC_INT – HVC_X inhibitory coupling as well as intrinsic properties (Figure 8).

Figure 8

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Patterned activity of HVC interneurons illustrated for an exemplar interneuron ( ${H V C}_{I N T}^{10}$ ).

For this neuron, ${H V C}_{R A}^{11}$ , ${H V C}_{R A}^{32}$ , ${H V C}_{R A}^{83}$ , ${H V C}_{R A}^{120}$ , ${H V C}_{X}^{7}$ , ${H V C}_{X}^{8}$ *and* $H V C_{X}^{9}$ were selected randomly from the pool of HVC_RA’s and HVC_X’s to form excitatory coupling. The number of bursts in ${H V C}_{I N T}^{10}$ is controlled by the number of bursts that each of the HVC_RA and HVC_X neurons that connect to it exhibit. The strength of each of the ${H V C}_{I N T}^{10}$ bursts depends on the magnitude of $g_{A M P A}$ from the corresponding neuron(s) they cause it as well as the simultaneous bursting of any of the projecting neurons. For example, the asterisk (*) shows a region of dense firing in ${H V C}_{I N T}^{10}$ because ${H V C}_{R A}^{83}$ , ${H V C}_{X}^{7}$ , ${H V C}_{X}^{8}$ *and* ${H V C}_{X}^{9}$ neurons elicit their spikes at similar times causing a potentiated response in ${H V C}_{I N T}^{10}$ . HVC_X neurons exhibit multiple sags and rebounds because they’re receiving inhibition from several interneurons (not shown here).

Figure 9

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Intrinsic changes in HVC_INT halt the propagation of sequential activity.

Up-regulating the T-type Ca²⁺ current conductance (A) or the hyperpolarization-activated inward current conductance (B) in exemplar HVC_INT neurons eliminates sequence propagation. Increasing $g_{C a T}$ in $H V C_{I N T}^{23}$ 10-fold results in dense bursting and firing in $H V C_{I N T}^{23}$ , which in its turn blocks the bursting of ${H V C}_{R A}^{45}$ due to the inhibitory GABA coupling between $H V C_{I N T}^{23}$ and ${H V C}_{R A}^{45}$ (A). Similarly, increasing $g_{H}$ in ${H V C}_{I N T}^{41}$ 10-fold results in increased firing in $H V C_{I N T}^{41}$ , which in its turn blocks the bursting of ${H V C}_{R A}^{76}$ due to the inhibitory GABA coupling between $H V C_{I N T}^{41}$ and $H V C_{R A}^{76}$ (*B).* Sequence of HVC_RA bursts truncated at the level of $H V C_{R A}^{80}$ for better visualization purposes.

Figure 10

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Activity patterns illustrating the interplay between HVC interneurons and X-projecting neurons.

${H V C}_{X}^{3}$ (blue) is an exemplar projecting neuron that receives inhibition from $H V C_{I N T}^{5}$ *(black) and* $H V C_{I N T}^{6}$ (green) due to the random inhibitory coupling (A). $H V C_{X}^{3}$ is inhibited whenever ${H V C}_{I N T}^{5}$ and $H V C_{I N T}^{6}$ are firing, eventually escaping inhibition at some intervals and eliciting rebound bursts due to the activation of $I_{H}$ and $I_{C a T}$ . HVC interneurons can inhibit multiple HVC_X neurons. ${H V C}_{I N T}^{5}$ is an exemplar from the network that inhibits $H V C_{X}^{3}$ and $H V C_{X}^{5}$ (orange) (C). Bursts in $H V C_{I N T}^{5}$ elicit subsequent bursts in $H V C_{X}^{3}$ and $H V C_{X}^{5}$ unless silenced by other HVC_INT neurons that connect to them. Zoomed versions of (A) and (C) are shown in (B) and (D).

Figure 11

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Intrinsic changes in HVC_X halt the propagation of sequential activity.

(A) Up-regulating the hyperpolarization-activated inward current conductance in a sample HVC_X neuron ( ${H V C}_{x}^{36}$ , by increasing its $g_{H}$ 10-fold) leads to increased firing in all HVC_INT neurons it connects to (for example, ${H V C}_{I N T}^{34}$ ), which in its turn inhibits all HVC_RA neurons it connects to (for example, ${H V C}_{R A}^{70}$ , being first in the pool that it inhibits) breaking the sequence at the level of ${H V C}_{R A}^{70}$ . (B) Up-regulating the T-type Ca2²⁺ current conductance in a sample HVC_X neuron ( ${H V C}_{x}^{12}$ , by increasing its $g_{C a T}$ 15-fold) leads to stronger rebound bursts in ${H V C}_{x}^{12}$ which leads to increased firing in all HVC_INT neurons it connects to (e.g. ${H V C}_{I N T}^{16}$ ), which in its turn inhibits all HVC_RA neurons it connects to (for example, ${H V C}_{R A}^{80}$ , being first in the pool that it inhibits) breaking the sequence at the level of ${H V C}_{R A}^{80}$ . (C) Finally, down-regulating the Ca²⁺ - dependent K⁺ current conductance in a sample HVC_X neuron ( ${H V C}_{x}^{27}$ , by setting its $g_{S K}$ to zero) leads to stronger rebound bursts in ${H V C}_{x}^{27}$ which leads to increased firing in all HVC_INT neurons it connects to (e.g. ${H V C}_{I N T}^{30}$ )*, w*hich in its turn inhibits all HVC_RA neurons it connects to (for example, ${H V C}_{R A}^{15}$ , being first in the pool that it inhibits) breaking the sequence at the level of ${H V C}_{R A}^{15}$ . Sequence of HVC_RA bursts truncated at the level of ${H V C}_{R A}^{85}$ for better visualization purposes.

Figure 12

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Altering the intrinsic properties of HVCx neurons disrupts network activity in 59% of the cases (out of 100 simulations) where the maximal conductances (gCaT, gSK, and gH) of HVCx neurons are randomly varied within their allowed ranges (Figure 13A).

As a result, some HVC interneurons (B) and X-projecting neurons (C) generated biologically unrealistic firing patterns, halting the propagation of sequential activity in RA-projectors (A).

Figure 13

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Box plots showing the ranges of ionic and synaptic currents that were allowed to vary while maintaining robust network propagation and biologically realistic in vivo behavior of all HVC neuronal classes.

(A) The ionic conductances that were varied are g_A of HVC_RA, g_SK of HVC_RA and HVC_X, g_h and g_CaT of HVC_INT and HVC_X. The shown ranges reflect values whereby each neuron class is able to maintain realism in terms of electrophysiological behavior and network properties. (B) Ranges of values of synaptic conductances that connect two classes of HVC neurons while conserving sequential activity propagation and the general network activity.

Tables

Appendix 1—table 1

Fixed parameter values used in all simulations.

Parameter	Value	Parameter	Value
$V_{L}$	−70 mV	$θ_{a_{T}}$	−64 mV
$V_{K}$	−90 mV	$θ_{b}$	0.4 mV
$V_{N a}$	50 mV	$θ_{r_{T}}$	−67 mV
$V_{C a}$	−85 mV	$θ_{r_{r_{T}}}$	68 mV
$V_{H}$	−30 mV	$σ_{m}$	−5 mV
$g_{L}$	2 nS	$σ_{n}$	−5 mV
$g_{C a}$	19 nS	$σ_{s}$	−0.05 mV
$τ_{n}$	10 ms	$σ_{a}$	−10 mV
$τ_{h p}$	1000 ms	$σ_{e}$	5 mV
$τ_{e}$	20 ms	$σ_{r_{f}}$	5 mV
$τ_{h}$	1 ms	$σ_{r_{s}}$	25 mV
$τ_{r_{s}}$	1500 ms	$σ_{a_{T}}$	7.8 mV
$τ_{r_{0}}$	200 ms	$σ_{b}$	−0.1 mV
$τ_{r_{1}}$	87.5 ms	$σ_{r_{T}}$	2 mV
$θ_{m}$	−35 mV	$σ_{r_{r_{T}}}$	2.2 mV
$θ_{n}$	−30 mV	$f$	0.1
$θ_{s}$	−20 mV	$ε$	$0.0015 p A^{- 1} m i c r o M$ ${m s e c}^{- 1}$
$θ_{a}$	−20 mV	$k_{C a}$	$0.3 m s e c^{- 1}$
$θ_{e}$	−60 mV	$b_{C a}$	$0.1 m i c r o M$
$θ_{r_{f}}$	−105 mV	$k_{s}$	$0.5 m i c r o M$
$θ_{r_{s}}$	−105 mV	$p_{r_{f}}$	100
$g_{{N a}_{{H V C}_{R A}}}$	300 nS	$g_{K_{{H V C}_{R A}}}$	150 nS
$g_{{N a}_{{H V C}_{X}}}$	450 nS	$g_{K_{{H V C}_{X}}}$	80 nS
$g_{{N a}_{{H V C}_{I N T}}}$	800 nS	$g_{K_{{H V C}_{I N T}}}$	1200 nS

Additional files

MDAR checklist: https://cdn.elifesciences.org/articles/105526/elife-105526-mdarchecklist1-v1.docx
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Source code 1 This code connects all neurons in the network via the corresponding ionic currents.: https://cdn.elifesciences.org/articles/105526/elife-105526-code1-v1.zip
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Source code 2 This function builds the template of projecting neurons as well as interneurons to be simulated in the network.: https://cdn.elifesciences.org/articles/105526/elife-105526-code2-v1.zip
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Source code 3 This is the differential equations file containing the corresponding equations, this file is called in all simulations.: https://cdn.elifesciences.org/articles/105526/elife-105526-code3-v1.zip
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Zeina Bou Diab
Marc Chammas
Arij Daou

(2025)

Biophysical network modeling of temporal and stereotyped sequence propagation of neural activity in the premotor nucleus HVC

eLife 14:RP105526.

https://doi.org/10.7554/eLife.105526.3

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Song system overview and general intrinsic and network properties of HVC neurons in vivo and in vitro.

Model output compared to experimental results obtained by Mooney and Prather, 2005.

Figure 2—source code 1

Figure 2—source code 2

Figure 2—source code 3

Figure 2—source code 4

Cartoon diagram illustrating the network architecture configuration.

Spiking patterns of 120 HVCRA neurons (labeled with numbers) showing the propagation of sequential activity.

Effects of the AMPA synaptic conductance and A-type K+ conductance on the delay between two successive HVCRA bursts.

Intrinsic changes in HVCRA halt the propagation of sequential activity.

Activity patterns for 10 HVCINT and 10 HVCX neurons are illustrated.

Patterned activity of HVC interneurons illustrated for an exemplar interneuron (HVCINT10).

Intrinsic changes in HVCINT halt the propagation of sequential activity.

Activity patterns illustrating the interplay between HVC interneurons and X-projecting neurons.

Intrinsic changes in HVCX halt the propagation of sequential activity.

Altering the intrinsic properties of HVCx neurons disrupts network activity in 59% of the cases (out of 100 simulations) where the maximal conductances (gCaT, gSK, and gH) of HVCx neurons are randomly varied within their allowed ranges (Figure 13A).

Box plots showing the ranges of ionic and synaptic currents that were allowed to vary while maintaining robust network propagation and biologically realistic in vivo behavior of all HVC neuronal classes.

Fixed parameter values used in all simulations.

MDAR checklist

Source code 1

Source code 2

Source code 3

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Spiking patterns of 120 HVC_RA neurons (labeled with numbers) showing the propagation of sequential activity.

Effects of the AMPA synaptic conductance and A-type K⁺ conductance on the delay between two successive HVC_RA bursts.

Intrinsic changes in HVC_RA halt the propagation of sequential activity.

Activity patterns for 10 HVC_INT and 10 HVC_X neurons are illustrated.

Patterned activity of HVC interneurons illustrated for an exemplar interneuron ( ${H V C}_{I N T}^{10}$ ).

Intrinsic changes in HVC_INT halt the propagation of sequential activity.

Intrinsic changes in HVC_X halt the propagation of sequential activity.