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Synaptic diversity enables temporal coding of coincident multisensory inputs in single neurons

Nature Neuroscience volume 18, pages 718–727 (2015)Cite this article

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Abstract

The ability of the brain to rapidly process information from multiple pathways is critical for reliable execution of complex sensory-motor behaviors, yet the cellular mechanisms underlying a neuronal representation of multimodal stimuli are poorly understood. Here we explored the possibility that the physiological diversity of mossy fiber (MF) to granule cell (GC) synapses in the mouse vestibulocerebellum may contribute to the processing of coincident multisensory information at the level of individual GCs. We found that the strength and short-term dynamics of individual MF-GC synapses can act as biophysical signatures for primary vestibular, secondary vestibular and visual input pathways. Most GCs receive inputs from different modalities, which, when coactivated, produced enhanced GC firing rates and distinct first spike latencies. Thus, pathway-specific synaptic response properties permit temporal coding of correlated multisensory inputs by single GCs, thereby enriching sensory representation and facilitating pattern separation.

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Figure 1: Identification of MF-GC input types using k-means clustering analysis of EPSC properties.
Figure 2: Multiple probability fluctuation analysis reveals quantal parameters contributing to synaptic diversity.
Figure 3: Pathway-specific MF-GC synaptic properties.
Figure 4: Sensory-specific MF-GC synaptic properties recorded in vivo.
Figure 5: Multimodal convergence onto single GCs.
Figure 6: Input-specific synaptic integration in single GCs.
Figure 7: Model GCs respond to coactivation of multiple sensory pathways with specific onset delays.
Figure 8: Delay coding enhances pattern decorrelation.

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Acknowledgements

We thank G. Lepousez and N. Torquet for their advice on performing stereotaxic injections of viral vectors and the laboratory of U. Maskos for access to the stereotaxic equipment. We thank P. Caroni (Friedrich Miescher Institute for Biomedical Research, Basel) for providing the transgenic mouse line Thy1-mGFP (line 17) and Jonathan Bradley for establishing the colony and initial characterization of GFP expression in the cerebellar vermis. We thank C. Saint-Cloment for managing the mouse colonies and performing some stereotaxic injections. We thank J. Rothman (University College London) for providing routines for the synaptic and integrate-and-fire computational models. Finally, we would like to thank M. Albert, B. Barbour, D. Brody, L. Cathala, J. Dittman, D. Feldman, T. Mrsic-Flogel, C. Lena, G. Lepousez, A. Nissant, N. Rebola, A. Silver and A. Tran-Van-Minh for comments on the manuscript. This work was supported by grants from the French National Agency for Research (ANR-2010-BLANC-1411, ANR-13-SAMA-0006-05, ANR-07-BSYS-010 VESTICODE, ANR-13-BSV4-0016) Fondation de Recherche Medicale (Team grant) and the European Union FP6 specific targeted project (LSHM-CT-2007-037765). T.W.M. is a Wellcome Trust investigator and is supported by the UK Medical Research Council (MC_U1175975156).

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Authors and Affiliations

  1. Institut Pasteur, Unit of Dynamic Neuronal Imaging, Paris, France

    François P Chabrol & David A DiGregorio

  2. CNRS UMR 3571, Genes, Synapses and Cognition, Institut Pasteur, Paris, France

    François P Chabrol, Martin T Wiechert & David A DiGregorio

  3. Division of Neurophysiology, MRC National Institute for Medical Research, Mill Hill, London, UK

    Alexander Arenz & Troy W Margrie

  4. Department Circuits - Computation - Models, Max Planck Institute of Neurobiology, Martinsried, Germany

    Alexander Arenz

  5. Laboratory for Perception and Memory, Institut Pasteur, Paris, France

    Martin T Wiechert

  6. Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK

    Troy W Margrie

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Contributions

F.P.C., A.A., T.W.M. and D.A.D. conceived and designed experiments. F.P.C. performed in vitro experiments and analysis and A.A. performed in vivo experiments and analysis. M.T.W. performed pattern decorrelation and linear classifier modeling. F.P.C., A.A., M.T.W., T.W.M. and D.A.D. wrote the manuscript.

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Integrated supplementary information

Supplementary Figure 1 MF-GC EPSC metrics vary greatly between lobule X MF-GC synaptic connections but are correlated with each other.

(a) Broad distributions of EPSC mean peak amplitude, coefficient of variation of the peak amplitude (CV) and paired-pulse ratio (PPR) at 100 Hz stimulation from 83 MF-GC synaptic connections using the blind stimulation protocol. Mean values are displayed with standard deviation. (b) Significant correlations were observed between all parameters (red line represents linear fit; p values calculated from Pearson correlation).

Supplementary Figure 2 Determination of the number of clusters for k-means cluster analysis.

(a) A K-Means clustering (KMC) analysis of EPSC metrics from 259 MF-GC synaptic connections when varying the number of clusters. The first principal component results from the negative correlation between peak amplitude vs. coefficient of variation (CV) and the paired-pulse ratio (PPR). The second principal component results from a positive correlation between peak amplitude and PPR. (b) Proportion of the total variance accounted for by progressively increasing numbers of clusters for peak amplitude (Amp), CV, PPR, and the average of all curves (gray). Using this curve we selected “bye eye” the optimal number of clusters to be five (red dot), corresponding to the number of clusters beyond which there was no substantial increase in the accounted variance. The lack of increased slope was estimated using the slope (tangent lines) of the curve beyond 5 input groups.

Supplementary Figure 3 The vast majority of fluorescent MFs in lobule X of Thy1-mGFP (line 17) transgenic mice arise from vestibular ganglion.

(a) 2PLSM image of a parasagittal slice of the cerebellum showing GFP expression in the GC layer. Image is a montage of tiled z-projection images. Magenta dotted region indicates lobule X of the vestibulo-cerebellum (VC). (b) Higher magnification image of the GC layer of VC showing that GFP is expressed in a subset of MFs (maximal intensity projection over 10 µm). (c) Dodt contrast (top) and fluorescence (bottom) images of brainstem nuclei known to project to VC. MVe: medial vestibular nucleus, PrH: nucleus prepositus hypoglossi, SpVe: spinal vestibular nucleus. Fluorescently labeled somata were observed only in the SpVe, but only comprised 30% of cell bodies. (d) 2PLSM image showing robust GFP expression in vestibular ganglion (VG) neurons (animal age: 7-10 days postnatal).

Supplementary Figure 4 Stereotaxic injections of AAV9-TurboRFP vectors in the medial vestibular and prepositus hypoglossi nuclei.

(a) Schematic drawing of a coronal section of cerebellum and brainstem showing the site of viral transduction, in this case the medial vestibular nucleus (MVe) which receives primary vestibular afferents from the vestibular ganglion (VG) and sends secondary vestibular MFs to lobule X of VC. (b) Confocal images of coronal brain slices showing RFP expression restricted to brainstem regions below the 4th ventricle. The brain slice is outlined with a white dashed line. (c) RFP expression from precerebellar afferents is restricted to lobule IX and X of the cerebellum. (d) Higher magnification view of brainstem regions at the floor of the 4th ventricle. Neurons expressing RFP are restricted to the MVe. LVe: lateral vestibular nucleus, and PrH: nucleus prepositus hypoglossi. (e,f) Neurons in MVe and their MF afferents (maximal intensity projection over 10 µm) in VC, respectively. (g-l) Same as for a-f for viral injections performed in PrH.

Supplementary Figure 5 Two-photon fluorescence-targeted stimulation of individual MFs.

(a) To position the stimulating electrode near labeled MFs, two-photon laser scanning microscopy images of a fluorescent MFs (green) were superimposed with Dodt-contrast. (Left) The stimulation pipette is initially placed close to the MF axon (star 1). The pipette is then placed progressively further away. (Right) Last position (star 4) of the stimulation pipette. (b) Superposition of 2PLSM images of the same fluorescent MF and connected GC filled with Alexa 594 (magenta). The distance from the tip of the stimulation pipette to the axon was measured and used as the radius value. The circles represent the different areas of stimulation with corresponding minimal voltages values (+5 volts to insure reliable stimulation). (c) Amplitude of evoked EPSCs did not change with either increased distance from the axon or increasing voltage stimulations, demonstrating that no additional MFs connected to the patched GC were stimulated. (d) The stimulus voltage was always positively correlated with distance from the targeted axon (values from 3 different recordings in gray and their average in black), supporting stimulation of the targeted axon. The gray box corresponds to the range of voltages used in 2PLSM-targeted experiments, indicating an average stimulation radius below 5 μm, sufficiently small to avoid stimulation of more than one MF connected to the same GC.

Supplementary Figure 6 Alterations in EPSC amplitude during visual stimuli are not detectable.

Plot of the peak amplitudes normalized to the peak of the 1st EPSC for the first 20 successive EPSCs evoked during the presentation of visual gratings in the preferred direction. Values from individual cells (n = 10) are represented by gray traces (corresponding to the left axis, note that in some cells EPSC trains contained < 20 single EPSCs), while the average values from consecutive EPSCs across cells are shown in black with SEM. Trends in EPSC amplitude during visual stimuli were assessed with Spearman’s rank order stability test. We observed no detectable trend (red line, p > 0.05). The green curve shows the median interval between the 1st EPSC and each subsequent event (ms, right axis), averaged over all cells.

Supplementary Figure 7 Synaptic and integrate-and-fire model replicates input type-specific GCs responses.

(a) Synaptic and integrate-and-fire (sIAF) model simulations of voltage responses to synapses modeling each input type are similar to experimental current-clamp traces. Synapses were stimulated 30 times at 100 Hz for each input type in both simulations and experiments. Each input type was modeled by adjusting the synaptic conductance parameters to fit the synaptic current dynamics observed experimentally (EPSC kinetics and short-term plasticity; see Supplementary Table 1). (b) Simulated (dashed line) and experimental (solid line) input/output (I/O) relationships for each input group. (c) Simulated delays between synaptic stimulation and the first spike (dashed lines) match experimental delays (solid lines) for different input groups. (d) The simulation also predicts GC responses to simultaneous double input stimulation. We show driver (group 2) and supporting input (group 4) with in vivo-like tonic firing, stimulated individually and simultaneously. Stimulation frequencies are indicated in the underlying colored boxes. (e) Combination-specific first spike latencies are well matched between data and simulations. (f) Simulated I/Os from individual input groups (dashed gray curves) and from the driver inputs (G1, 2, or 5) combined with the supporting input (G4) driven at different frequencies (indicated on the right hand side). Note the prominent multisensory enhancement in GC output.

Supplementary Figure 8 Delay coding model outperforms thresholding model when input patterns are strongly correlated.

Graph shows the difference in output correlation between thresholding and delay coding models for different delays as a function of input correlation. Positive values indicate better performance of delay coding. For more similar patterns (higher input correlation), the delay coding model shows a stronger decorrelation relative to the thresholding model.

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Chabrol, F., Arenz, A., Wiechert, M. et al. Synaptic diversity enables temporal coding of coincident multisensory inputs in single neurons. Nat Neurosci 18, 718–727 (2015). https://doi.org/10.1038/nn.3974

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