Research ArticleNEUROSCIENCE

A cerebellar adaptation to uncertain inputs

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Science Advances  30 May 2018:
Vol. 4, no. 5, eaap9660
DOI: 10.1126/sciadv.aap9660
  • Fig. 1 Design of probe stimuli protocols.

    Schematics of eyelid conditioning training (A to C) and probe protocols (D to G). For (A) to (C), schematics of stimuli presentation is shown at the top, and eyelid position as a function of time is shown below. Upward deviation corresponds to eyelid closure. (A) Neutral trained input (CS) is paired with eyelid stimulation as US. In the naïve state, there is no eyelid response during CS, and the US evokes a reflexive noncerebellar eyelid closure (shown in gray). (B) Behavioral response after animal learned CS-US pairing. Predictive eyelid closure during CS and before US is a CR. (C) Example eyelid CR profiles on the CS-alone trial from subject trained to produce either a full-sized (black line) or half-sized eyelid closure (blue line). (D) Coronal histology demonstrating lesion marks from two stimulation electrodes implanted in middle cerebellar peduncle. (E to G) Schematics of probe inputs on three different probe protocols. In each case, input used for training (500-ms, 100-Hz pulse train) is shown in black. (E) Frequency probes. The length of stimulus was kept at 500 ms, but frequency was systematically decreased. (F) Short probes. Frequency was kept at 100 Hz, but only portion of the stimulus length was presented. (G) Competing stimulus. Two separate stimulating electrodes were implanted into middle cerebellar peduncle spaced 1 mm laterally. Only electrode A was used for training. During probe trials, neither frequency nor length of the stimulus was changed, but rather the current applied on electrode A was decreased and, correspondingly, the current on electrode B was increased from zero to keep the number of total activated mossy fibers approximately constant. This manipulation should result in a gradual shift of the overlap between mossy fibers activated by probe and trained stimulus. The area of current spread is illustrated as a gray circle for electrode A (used to deliver trained input) and a brown circle for electrode B (competing stimulus).

  • Fig. 2 Behavioral summary of binary choice.

    (A) Example eyelid responses (eyelid position as a function of time) and frequency distribution of response amplitudes to trained input (100 Hz, black, left) and to 70-Hz frequency probes (cyan, right). In about half of trials, 70-Hz probes resulted in non-CR, but CR amplitudes were all or none when they happened. (B) CR probability decreases as probe stimuli become more different from the trained input. Data are shown for all three protocols as follows: competing stimulus probes (brown), short probes (red), and frequency probes (cyan for animals trained to 6-mm CRs, blue to 3-mm CRs). (C) Mean CR amplitude as a function of CR probability remained constant in all three probe protocols, same color code as in (B). (D) Cumulative distribution functions (CDFs) of distributions of response amplitudes to each probe (color-coded) or trained input (black). (E) Same as (D), but with non-CRs removed from each distribution. (F) Same as (E) for rabbits trained to 3-mm target CR amplitude.

  • Fig. 3 In vivo recordings from PCs during binary choice sessions.

    (A) Sagittal view of histology with tetrode tracks in the cerebellar cortex. (B) Twenty overlaid waveforms of simple (gray, Ssp) and complex (black, Csp) spikes are shown on top, and peristimulus time histogram (PSTH) of US-evoked complex spikes from eyelid PC is shown below. (C) Behavior and raster plot of PC simple spikes during CS-alone frequency probe trials. Trials are sorted either in order of occurrence (top) or according to CR onset time (green dots, bottom). (D) Average firing rate of eyelid PCs on CR (cyan) or non-CR trials (black) during frequency probes. Gray region represents probe duration. Shaded regions indicate 95% confidence intervals. (E) Same as (D), but individual examples for short probes, using either mossy fiber (MF) stimulation (red) or 1-kHz tone (violet). (F) Average spike count of eyelid PC activity on CR (color-coded by probe protocol) and non-CR trials (black lines, darker-colored points to indicate the protocol).

  • Fig. 4 ROC analysis of PC activity.

    (A) Frequency distributions of PC spike counts on CR (color) and non-CR trials (black). (B) Choice probability calculated using ROC analysis for each probe type within three probe protocols. (C) Choice probability as a function of time, using 100-ms time bins. Here, for each protocol, we combined trials from three middle probes; identical results were also obtained in analyses of individual probes (fig. S6). Spike trains are aligned to probe onset. Points are plotted in the center of their corresponding bin. Distribution of CR onset times is shown on top. (D) ROC analysis of the same data but with trials aligned by behavioral CR onset (black vertical line). Error bars indicate 95% confidence intervals. Significance above chance at 0.05, 0.01, and 0.001 is depicted by one, two, or three asterisks, respectively.

  • Fig. 5 Simulation with DCNcol replicates binary choice phenomenon.

    (A) Dependence of CR amplitude on CR probability in simulation with DCNcol. Results are shown for the same three probe protocols used in experiments. Color-coding is preserved as in Fig. 1 and further explained in the legend on the right. Inset: Distribution of CR amplitudes in response to one probe type per protocol, indicated above distribution. Error bars indicate 95% confidence intervals. (B) Results of the same experiments performed in simulation without DCNcol. (C) Comparison of behavioral results using binarity index (BI). Results are shown for two types of simulations and experimental data (lighter bars). Error bars show 1 SD. MF, mossy fiber. (D) Decrease in simulation PC activity on probe trials with CRs as a function of CR probability. Insets: Average normalized firing rate of simulation PCs on CR trials, in response to trained CS (black) and probes (colored). (E) Same as (D), but for simulation without DCNcol. (F) Same metric and layout as in (C), but here eyelid PC activity was used to determine the degree of binarity of PC responses.

  • Fig. 6 Possible mechanism of DCNcol contribution to the binary choice phenomenon.

    The layout of all panels is the following: Eyelid position as a function of time is shown on top, with a region colored in black indicating the duration of CS. Vertical lines in the middle represent spike train inputs from CS-activated mossy fibers (MFs) (black) and DCNcol MFs (green) to the cerebellar cortex. Correspondingly, colored rectangles below show schematized PSTHs. The time of US delivery is shown by a gray arrow. (A) Initial acquisition. Since CRs have not developed yet, CS-activated MFs are the main input that the cerebellum is learning to. (B) Early expression. As small amplitude CRs start to happen, the cerebellum starts to get two kinds of inputs: (i) MF activated by CS (black) and (ii) DCNcol MFs (green). Initially, small CRs are associated only with CS, since that was the only input present during initial acquisition. (C) Late expression. As CR amplitude increases, so does DCN activity that drives CR and, therefore, also DCNcol input (green) to the cerebellar cortex. After acquisition is complete, only the early portion of CR is associated with CS (black portion of eyelid position profile), while the later portion of CR—which determines its amplitude (green portion)—is associated with DCNcol feedback input. Dashed black line represents a schematized CR profile that would be present if DCNcol feedback was disabled.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/5/eaap9660/DC1

    Supplementary Materials and Methods

    fig. S1. Distributions of CR amplitudes in response to different stimuli presentations.

    fig. S2. CR probability as a function of probe.

    fig. S3. CDF of CR amplitudes for subjects trained to produce half-sized CRs.

    fig. S4. Isolation of single units and eyelid PCs from tetrode recordings.

    fig. S5. Eyelid PC responses during sessions with short probes.

    fig. S6. PC choice probabilities for individual probe types.

    fig. S7. Acquisition of CRs in large-scale cerebellar simulations.

    fig. S8. Definition of BI and relative PC response.

    fig. S9. ROC analysis of PC activity from simulation with DCNcol.

    table S1. Two-sample Kolmogorov-Smirnov test, comparison between CR amplitude distributions to probe and trained inputs.

    table S2. Results of two-way ANOVA on PC spike counts to different probe inputs on CR and non-CR trials.

  • Supplementary Materials

    This PDF file includes:

    • Materials and Methods
    • fig. S1. Distributions of CR amplitudes in response to different stimuli presentations.
    • fig. S2. CR probability as a function of probe.
    • fig. S3. CDF of CR amplitudes for subjects trained to produce half-sized CRs.
    • fig. S4. Isolation of single units and eyelid PCs from tetrode recordings.
    • fig. S5. Eyelid PC responses during sessions with short probes.
    • fig. S6. PC choice probabilities for individual probe types.
    • fig. S7. Acquisition of CRs in large-scale cerebellar simulations.
    • fig. S8. Definition of BI and relative PC response.
    • fig. S9. ROC analysis of PC activity from simulation with DCNcol.
    • table S1. Two-sample Kolmogorov-Smirnov test, comparison between CR amplitude distributions to probe and trained inputs.
    • table S2. Results of two-way ANOVA on PC spike counts to different probe inputs on CR and non-CR trials.

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