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Multiplexing using synchrony in the zebrafish olfactory bulb

Nature Neuroscience volume 7, pages 862–871 (2004)Cite this article

Abstract

In the olfactory bulb (OB) of zebrafish and other species, odors evoke fast oscillatory population activity and specific firing rate patterns across mitral cells (MCs). This activity evolves over a few hundred milliseconds from the onset of the odor stimulus. Action potentials of odor-specific MC subsets phase-lock to the oscillation, defining small and distributed ensembles within the MC population output. We found that oscillatory field potentials in the zebrafish OB propagate across the OB in waves. Phase-locked MC action potentials, however, were synchronized without a time lag. Firing rate patterns across MCs analyzed with low temporal resolution were informative about odor identity. When the sensitivity for phase-locked spiking was increased, activity patterns became progressively more informative about odor category. Hence, information about complementary stimulus features is conveyed simultaneously by the same population of neurons and can be retrieved selectively by biologically plausible mechanisms, indicating that seemingly alternative coding strategies operating on different time scales may coexist.

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Figure 1: Odor-evoked oscillatory synchronization in the OB.
Figure 2: Spatio-temporal properties of odor-evoked oscillatory activity.
Figure 3: Phase shifts between LFP oscillations, but not MC spike trains.
Figure 4: Phase relationships between LFPs and MC spike trains.
Figure 5: Phase-locking of odor-specific subsets of MC action potentials.
Figure 6: Comparison of patterns of phase-locked and residual spikes.
Figure 7: Relationships between activity patterns depend on the sensitivity of analysis for phase-locked spiking.

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Acknowledgements

We thank A. Schäfer and members of the Friedrich and Laurent labs for helpful discussions and/or comments on the manuscript. This work was supported by the Max-Planck Society, the Deutsche Forschungsgemeinschaft (DFG), the National Institutes for Deafness and other Communication Disorders (NIDCD), the Keck Foundation and the McKnight Foundation.

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Authors and Affiliations

  1. Department of Biomedical Optics, Max-Planck-Institute for Medical Research, Jahnstr. 29, Heidelberg, 69120, Germany

    Rainer W Friedrich & Christopher J Habermann

  2. Division of Biology 139–74, California Institute of Technology, Pasadena, 91125, California, USA

    Rainer W Friedrich & Gilles Laurent

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  1. Rainer W Friedrich
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  2. Christopher J Habermann
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  3. Gilles Laurent
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Correspondence to Rainer W Friedrich.

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Supplementary information

Supplementary Fig. 1

Comparison of phase shifts between LFPs and MC spike trains. Phase shifts between LFPs were compared to phase shifts between MC spike trains recorded simultaneously on the same electrodes in all recorded pairs. Phase shifts were determined from cross-correlograms during the oscillatory part of the odor response (0.5-2.4 s after onset), averaged over all odors and trials for a given pair. For this analysis, absolute phase shifts were used, i. e., the sign of the phase shift was not considered (see Supplementary Methods). On average, the absolute phase shifts between spike trains were significantly smaller (17 ± 16°) than absolute phase shifts between the corresponding LFPs (26 ± 22°; P < 0.05; Wilcoxon rank-sum test). (a) all absolute phase shifts between LFP pairs and the spike trains recorded on the same electrodes. Each circle represents the absolute phase shift from one pair of LFPs (left) or spike trains (right). Box plots show the median (red line), lower and upper quartiles (black lines delimiting box), and 1.5 times the interquartile range (error bars). For both LFPs and spike trains, the majority of phase shifts ranged between 0° and 30°. However, some phase shifts between LFPs reached ∼90°. Large phase shifts between spike trains, in contrast, were rarer and never exceeded ∼60°. The smaller mean phase shift between spike trains may therefore be primarily due to the rare occurrence of large phase shifts. (PDF 14 kb)

To test this, the phase shifts between LFPs and spike trains were compared for recorded pairs in which the phase shifts between LFPs was > 40° (n = 8). The absolute phase shifts between these LFP pairs and the phase shifts between the simultaneously recorded MC spike trains are shown in (b) using the same conventions as in a. The mean absolute phase shift (± SD) between these LFP pairs was 66 ± 18°, while the mean absolute phase shift between the spike trains was much smaller (20 ± 19°; P < 0.001; Wilcoxon rank-sum test). Hence, large phase shifts between LFPs are not accompanied by similarly large phase shifts between MC spike trains recorded on the same electrodes.

Supplementary Fig. 2

The predicted relationship between the phase difference of the LFPs and the difference in the mean spike time of the simultaneously recorded MC spike trains was tested on a spike-by-spike basis using spikes known to co-occur with spikes from another MC. From all paired recordings, all pairs of spikes occuring on the two electrodes within a time window of ±10 ms during the oscillatory part of the odor response (0.5-2.4 s after onset) were selected. For each spike in a pair, the mean spike phase was subtracted from the actual spike phase, yielding a "compensated phase" that represents the deviation from the mean phase (Fig. 4a, gray axes). Compensated phases of the two spikes were subtracted from each other. If spikes occuring at the mean phase of each MC are synchronous, the resulting "compensated phase difference" of the spike pair should, on average, be zero for simultaneously occurring spikes and increase with the time difference between spikes in a pair. Indeed, the compensated phase difference was distributed around zero and significantly correlated with the time difference between spikes in a pair (P < 10−10): (a) Scatter plot of time difference versus compensated phase difference for all individual spike pairs occurring within a time window of ±10 ms. Each dot represents one spike pair. Note that time difference and compensated phase difference are highly correlated, and that most dots occur around zero time difference and zero compensated phase difference. (b) Three-dimensional histogram of the data in a, illustrating the narrow distribution. These results show that spikes occuring around the preferred phase of each MC tend to be synchronized with spikes of other phase-locking MCs with zero time shift. (PDF 315 kb)

Supplementary Fig. 3

Example of patterns of phase-locked and residual spiking, demonstrating convergence and decorrelation, respectively, over time. Color-coded pixels in the grid depict the firing rate patterns of the same 55 MCs in response to two chemically similar stimuli, Phe and Trp, at different times after response onset (two pixels in each corner are not used; 200 ms time windows, centered on times indicated above). The position of each MC in the grid is fixed in all panels of each set ("Phase-locked" and "Residual"). MCs are arranged such that firing rates decrease from the center out for the activity pattern evoked by Phe in the time window when clustering is most prominent (700 ms for synchronized spikes; 200 ms for residual spikes). The similarity between activity patterns evoked by the two odors at each time point is measured by the correlation coefficient, r. Patterns of phase-locked spikes converged over time and, thus, contained information about features common to both odors ("aromatic neutral"). Patterns of residual spikes, in contrast, diverged over time and became easier to discriminate. (PDF 123 kb)

Supplementary Fig. 4

Factor analysis. An activity pattern is represented by a 58-dimensional vector, where each dimension describes the firing rate of one MC. Vectors representing similar patterns are close in this coding space and form clusters. Factor analysis extracts elementary activity patterns (factors) that represent cluster centers in coding space. The association of each activity pattern with each cluster is measured by factor loadings. If clustering of activity patterns is tight, each variable (odor) has a high loading on one factor and low loadings on the other factors, indicating that each pattern is associated with an individual cluster. In the absence of clusters, each variable has moderate loadings on multiple factors. Thus, the dominance of single factors on variables is a measure of clustering. For patterns of residual spikes, most odors were initially dominated by single factors. Subsequently, however, factor dominance became much less pronounced, indicating the disappearance of clustering. For patterns of phase-locked spikes, in contrast, most odors became dominated by single factors over time, indicating the emergence of clusters. (PDF 42 kb)

Supplementary Fig. 5

Retrieval of category information from MC activity patterns by a simple model. (a) Probability distributions for phase-locked and residual spikes during an oscillation cycle used for model (see Supplementary Note). (b) Schematic illustration of connectivity between MCs (left) and target neuron in the model. Curve depicts impulse response function to an input spike in the target neuron. (c) Suprathreshold activity of a target neuron to 40 trials of each of 16 odors during one oscillation cycle (50 ms), 1,000 ms after response onset. The connectivity between MCs and the target neuron was selected manually, based on plots of the mean response of each MC to the selected odor group divided by the mean response to the other odors. The impulse response caused by each input spike in the target neuron decayed exponentially with a time constant of 3 ms and was truncated after 5 ms. Membrane potential (arbitrary units) is represented by grayscale; subthreshold values are white. Histogram (right) indicates average suprathreshold response to each odor. (d) Average suprathreshold responses of the same target neuron at different times during odor stimulation. Each pixel represents the suprathreshold activity during an oscillation cycle, averaged over 40 trials (as depicted by bars in c), at a different time. Histogram shows the suprathreshold responses averaged over 2 s. (PDF 61 kb)

Supplementary Fig. 6

Detection of synchronized spikes by their phase-locking. Phase-locked spikes were detected in the data obtained in paired recordings in order to test whether spikes selected by phase-locking criteria were indeed synchronized with spikes from other neurons. (PDF 26 kb)

First, pairs of synchronized spikes were detected as those spikes that occurred on the two electrodes within a 10 ms time window (as in Supplementary Fig. 5). For each of these spikes it was then determined whether it was phase-locked to the LFP oscillation recorded on the same electrode by the three criteria. Using the conservative parameter set of Fig. 6 (thrLFP = 3; thrLFP = 90°; phase window = ±90°), 96 % of the synchronized spikes were also phase-locked, indicating that the selection of spikes by phase-locking detects most synchronized spikes even when selection criteria are stringent.

Second, we tested whether selection of phase-locked spikes also selects synchronized spikes. In a, the black line histogram shows the distribution of time differences between spikes recorded on the two electrodes within a time window of ±25 ms (about one oscillation cycle length; bin width, 2 ms). The distribution had a sharp peak at Δt = 0, indicating that most spikes in this data set were synchronized (this is due to the fact that paired recordings are biased towards phase-locked responses, unlike the unbiased population of spikes recorded in response to amino acid odors; see text and compare Figs. 4b, 6a). Nevertheless, pre-selection of phase-locked spikes further increased the synchrony of spikes recorded on different electrodes. The gray histogram shows the distribution of spike time differences after selecting only those spikes that were phase-locked to the LFP, as determined by the conservative criteria of Fig. 6 (thrLFP = 3; thrLFP = 90°; phase window = ±90°). Selection of phase-locked spikes reduced the fraction of spike pairs with larger time differences and, thus, effectively detected synchronized spikes.

Third, we asked whether increasing the stringency of the criteria for phase-locking leads to the selection of spikes that are synchronized with increasing precision in the data obtained by paired recordings. The precision of synchrony was assessed by two different measures. First, it was quantified by the SD of a Gaussian fit to the distribution of spike time differences (e. g., histograms in a). Second, precision of synchrony was quantified as the fraction of absolute spike time differences < 8 ms. In b-c, the precision of spike synchrony is plotted as a function of the stringency of phase-locked spike detection using the same parameter sets as in Fig. 7. For both measures, the precision of synchrony clearly increased when spikes were selected for phase-locking with increasing stringency. See legend to Fig. 7 for explanation of symbols. Hence, the precision of phase-locking as determined by our criteria co-varies with the precision of spike synchrony. Varying the stringency of phase-locked spike selection therefore mimics the variation of the sensitivity of readout for spike synchrony.

Supplementary Methods (PDF 28 kb)

Supplementary Note

Readout of category information by coincidence detection. (PDF 24 kb)

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Friedrich, R., Habermann, C. & Laurent, G. Multiplexing using synchrony in the zebrafish olfactory bulb. Nat Neurosci 7, 862–871 (2004). https://doi.org/10.1038/nn1292

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