Support for a synaptic chain model of neuronal sequence generation

Abstract

In songbirds, the remarkable temporal precision of song is generated by a sparse sequence of bursts in the premotor nucleus HVC. To distinguish between two possible classes of models of neural sequence generation, we carried out intracellular recordings of HVC neurons in singing zebra finches (Taeniopygia guttata). We found that the subthreshold membrane potential is characterized by a large, rapid depolarization 5-10 ms before burst onset, consistent with a synaptically connected chain of neurons in HVC. We found no evidence for the slow membrane potential modulation predicted by models in which burst timing is controlled by subthreshold dynamics. Furthermore, bursts ride on an underlying depolarization of ∼10-ms duration, probably the result of a regenerative calcium spike within HVC neurons that could facilitate the propagation of activity through a chain network with high temporal precision. Our results provide insight into the fundamental mechanisms by which neural circuits can generate complex sequential behaviours.

Figure 1

Two broad classes of models for a sequence generating circuit. a, Neurons could form a feed-forward synaptically-connected chain within HVC such that activity propagates from one group of neurons to the next. b, Alternatively, sequential activity could occur in the absence of directed connections between neurons, from temporal and spatial gradients of excitability. For example, the network could receive a global and gradual ramping-down of an inhibitory input over time (red synapses), producing a sequential activation. The order of activation would be determined by neuronal excitability. In the example model shown here, neurons receive different levels of constant excitatory input (green synapses). The neuron with the largest excitatory input (neuron 1) would be most depolarized and would be the first to reach spiking threshold. The neuron with the smallest constant excitatory input (neuron 8) would be the last to reach threshold. In the model depicted here, the timescale of the sequence produced corresponds to one song syllable (shown above).

Figure 2

A microdrive for sharp intracellular recording in the singing bird. a, The intracellular microdrive incorporates a motor that rotates a threaded rod and advances a shuttle that holds the electrode. b, A schematic of the zebra finch brain, highlighting three cell types in HVC defined by their projections: local circuit interneurons (in black), neurons that project to RA (in red), and neurons that project to basal ganglia-homologue area X (in blue). c, Examples of intracellular records from a putative local circuit interneuron, d, a putative X-projecting neuron, and e, an antidromically-identified RA-projecting neuron. Asterisk indicates the region magnified in the panels at right.

Figure 3

Intracellular membrane potential of identified HVC_(RA) neurons during singing. a–d, Examples of the membrane potential of four HVC_(RA) neurons recorded during singing. For each cell, activity from three motif renditions is shown aligned to the song (top). Also shown is an overlay of the membrane potential traces (expanded vertical scale, bottom of each panel) e, Expanded view of a burst from another neuron during singing showing the flat membrane potential prior to burst onset (arrow). f, Average membrane potential of seven HVC_(RA) neurons prior to the first spike in the burst (time zero). The population average is shown in red. g–i, The membrane potential of three HVC_(RA) neurons during singing with different holding currents. g, One neuron was held long enough to record with injected currents of +0.5 nA, 0 nA, −0.5 nA and −1.0 nA. h,i, Two other neurons recorded with 0 nA and −0.5 nA hyperpolarizing current. Note that injected current had little effect on burst timing, inconsistent with the predictions of the ramp-to-threshold model.

Figure 4

Evidence that calcium channels contribute to burst events in HVC_(RA) neurons. a, Response of an HVC_(RA) neuron in brain slice to somatically injected current steps (black bar) of different size. b, relationship between injected current and evoked firing rate in a population of 7 HVC_(RA) neurons. Note that somatic current injection does not elicit an all-or-none burst. c, In the presence of intracellular sodium and potassium channel blocker QX-314 (5mM), calcium spikes appear as an all-or-none depolarizing event. d, The amplitude of the depolarizing event (threshold to maximum point) as a function of injected current reveals an all-or-none response (n=8/8 neurons). e,f, HVC_(RA) neurons treated with the L-type calcium channel agonist BAY K 8644 (5–10μM) generate all-or-none spike bursts in response to somatic current injection. g, Segment of a whole-cell recording in a head-fixed bird during natural sleep showing three spontaneous bursts. h,i, Spontaneous bursting activity recorded during sleep following localized injection of L-type calcium channel antagonist Nifedipine (h) or agonist BAY K 8644 (i). Asterisk indicates expanded view below. j, k, Cumulative distribution of burst durations and inter-burst intervals for control, Nifedipine and BAY K 8644 conditions, m, Standard deviation of membrane potential fluctuations is not affected by Nifedipine or Bay K 8644, suggesting that synaptic transmission is not affected by these drugs .

Figure 5

A simple biophysical model to examine the implications of neuronal bursting on the robustness of HVC network propagation. Two models of a synaptically-connected chain network were compared: one with non-bursting neurons (a,b), the other with bursting neurons (c,d). a, non-bursting model: Spike raster plot for all neurons in the network showing activity as a function of time for two different levels of network connection probability (P=0.1 and 0.5). b, Spike raster of a single neuron during different runs of the network. Note the non-stationarity of propagation and large variability across runs. c, Bursting model: Spike raster plot for all neurons in the network. d, Spike raster of a single neuron during different runs of the network. Note the highly uniform propagation and stereotyped response across runs. e,f, Runtime jitter, plotted as a function of network connectivity and synaptic conductance, is consistently lower in the bursting model than in the non-bursting model. (See Supplementary Figures and Table for further quantification, and Supplementary Methods for model details.)