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Articles, Systems/Circuits

Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion

Elizabeth Zavitz, Hsin-Hao Yu, Elise G. Rowe, Marcello G.P. Rosa and Nicholas S. C. Price
Journal of Neuroscience 20 April 2016, 36 (16) 4579-4590; DOI: https://doi.org/10.1523/JNEUROSCI.4563-15.2016
Elizabeth Zavitz
1Department of Physiology,
2Neuroscience Program, Biomedicine Discovery Institute, and
3ARC Centre of Excellence for Integrative Brain Function, Monash University Node, Monash University, Clayton, VIC 3800, Australia
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  • ORCID record for Elizabeth Zavitz
Hsin-Hao Yu
1Department of Physiology,
2Neuroscience Program, Biomedicine Discovery Institute, and
3ARC Centre of Excellence for Integrative Brain Function, Monash University Node, Monash University, Clayton, VIC 3800, Australia
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Elise G. Rowe
1Department of Physiology,
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Marcello G.P. Rosa
1Department of Physiology,
2Neuroscience Program, Biomedicine Discovery Institute, and
3ARC Centre of Excellence for Integrative Brain Function, Monash University Node, Monash University, Clayton, VIC 3800, Australia
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Nicholas S. C. Price
1Department of Physiology,
2Neuroscience Program, Biomedicine Discovery Institute, and
3ARC Centre of Excellence for Integrative Brain Function, Monash University Node, Monash University, Clayton, VIC 3800, Australia
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  • Figure 1.
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    Figure 1.

    Firing rates evoked by a test stimulus depend on prior adaptation. a, A random sequence of directions was presented. Each motion period lasted 500 ms, and each direction was presented 600 times. A period of motion acted both as a test and an adaptor for the subsequent directions. Responses were sorted based on each adapt-test pairing (12 × 12 conditions). b, d, Responses of two units to 12 adapt-test pairings in which the test was always the preferred direction. Average responses to each adaptor are shown in the shaded region (0–500 ms), followed by responses to the preferred direction (500–1000 ms). Color represents the magnitude of the difference between the adaptor and the unit's preferred direction, where yellow is 0° (the adaptor is the preferred direction) and blue is 180° (the adaptor is the antipreferred direction; or control). Firing rate is affected in an adaptor-dependent way. c, e, von Mises fit to postadaptation tuning curves for the units shown in b, d, averaged from responses 50–550 ms after onset of the test stimulus. Change in tuning curves is mainly in response gain.

  • Figure 2.
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    Figure 2.

    Direction-dependent and persistent changes in encoding following adaptation. a–c, Gain, bandwidth (circular variance), and preferred direction measured following 500 ms of blank screen (No Adaptation) and 500 ms of Antipreferred Direction Adaptation. d, Changes in gain based on the angular distance between the adaptor and the preferred direction using a sliding 30° bin. Filled region represents the mean and 95% confidence interval of Gaussian fits to the raw data. Gain is normalized for each unit relative to the control (antipreferred) adaptor. Horizontal lines indicate bins where the normalized gain is significantly different from 100% (t test, p < 0.05). Arrows indicate the condition that is expanded in g–i. e, f, Same as in d, for bandwidth and preferred direction. g, After adaptation at the preferred direction, gain changes were evident throughout the test period. Gain changes are shown in 50 ms spike counting windows throughout the test period. Horizontal lines indicate significant post hoc tests. *Conditions that are significantly affected by adaptation, but not time. h, Same as in d for bandwidth. The cases varied in how the bandwidth of multiunits shifted following adaptation. i, Same as in d for preferred direction at following adaptation on the flank of the tuning curve (45°). j, Adapt-test pairings were constructed for 1-back to 8-back adapt-test pairs. d, g, The 1-back condition; a consecutive adapt-test pairing. Following preferred direction adaptation, gain was reduced for up to 4 motion periods (2 s). Horizontal lines indicate statistically significant gain reduction. Error bars indicate SE. k, l, Same as in j for bandwidth and preferred direction.

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    Figure 3.

    Stimulus dependence of spike count correlations between pairs of neurons. a, Histograms of correlations measured between pairs of recorded multiunits calculated for firing rates in all trials. Correlations measured in Animals 1 and 2 are similarly distributed; correlations measured in Animal 3 are higher and follow a narrower distribution. b, Correlations grouped according to the difference between the preferred directions of the neurons and the test direction for all 3 animals. Animals 1 and 2 show a strong relationship between correlation strength and the direction of the stimulus when the stimulus is near the preferred directions of neurons with similar preferences (dashed diagonal box). Data have been symmetrized and smoothed using a Gaussian kernel with SD 30°. c, Data from the diagonal indicated in b, with different cases superimposed. Animal 3 differs from Animals 1 and 2 in both the magnitude and stimulus sensitivity of the measured correlations.

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    Figure 4.

    Adaptation affects correlation structure in a direction-dependent way. a, Spike-count correlations (rsc) measured during the test period but grouped according to the relationship between the adapting direction and the preferred directions of the two units. The preferred directions of the two multiunits are noted as θPD1 and θPD2. Yellow diagonal running from bottom left to top right indicates that, when units prefer similar directions, they have higher correlations. The center of the diagonal, marked with a black circle, shows where two units with similar preferred directions are adapted with a stimulus near their preferred directions. b, A baseline correlation structure, analyzed in an identical manner to a, but taken from the adaptor 9-back from the test stimulus. c, Change in correlation structure immediately following adaptation calculated by subtracting b from a. Correlations are reduced when units with similar preferred directions have been strongly adapted. Correlations are also increased when the pair of neurons prefer directions that are >60° away from the adaptor and 120°–150° away from each other (off the main diagonal). d, ROI analysis of correlation changes for each animal. The mean correlation change for each animal in the central ROI illustrated in c is shown for each animal, from 1-back to 8-back. Schematic represents an example of a relationship between the adaptor (solid arrow) and the preferred directions of the two units (dashed arrows). For both animals, correlations are reduced for several seconds following adaptation. Horizontal lines indicate statistical significance (unpaired t test, p < 0.01). *Significant results with effect sizes >0.1. e, Same as in d for the off-center ROI shown in c. Correlations are sometimes significantly changed, but the effect size is <0.1. f, Correlation between firing rate and spike count correlation under adaptation (black, condition shown with black point in a) and at baseline (gray, gray point in b). Spike count correlation strength is correlated with firing rate in both cases, but this dependence is significantly reduced under adaptation, suggesting that the reduction in correlations after adaptation is not a simple consequence of the observed reduction in gain.

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    Figure 5.

    Decoding stimulus direction from population activity predicts the DAE. a, Decoding performance over time. The decoder is trained on 30 ms of spiking activity from a subpopulation of 20 units measured 400 ms after test onset and tested on a holdback set of 20% of trials using 30 ms of spiking activity from the same neurons at all other time points. Learning generalizes extremely well; decoding performance is the same for all stimuli displayed in 1500 ms (adaptor, 1-back test, 2-back test) (one-way ANOVA, p > 0.05). The brief period of poor performance following a stimulus change (t = 0, 500, 1000) reflects the neuronal response latency. Removing the pairwise correlations (dashed lines) has no substantial effect on absolute performance. Shaded area represents the response to the adaptor. Arrow indicates the time point at which the model was trained. ▾, 1-back and 2-back test times shown in b–f. b, The distribution of errors observed when the decoder estimates the test direction depends on the adapting direction. An adaptor at −60° relative to the test (black) has a positive shifted error distribution, whereas an adaptor 60° of the test (gray) has a negative shifted distribution. c, Mean error bias for all adapt-test pairs during the 1-back test period (tested at 800 ms after adaptor onset; Fig. 1a, ▾). We observe a repulsive DAE: predicted test directions are shifted away from the direction of the adapting stimulus. Shuffling the trials (dashed lines) did not affect the error bias. *Bias for the correlated and uncorrelated data was significantly different. Lines indicate conditions for which the error was significantly different from 0 (p < 0.01) for each animal. d, Same as in c, for the 2-back test period (tested at 1300 ms after adaptor onset; Fig. 1a, ▾). The error bias is reduced with an intervening stimulus period. Error bars indicate SEM. *Conditions where the 2-back bias was significantly different from the 1-back aftereffect. e, f, The overall decoding performance depends on the difference between the adapting and test directions, but the nature of this relationship is different from that observed for decoding bias: performance is lowest when the test and adaptor are most similar, and highest when they are most different. Horizontal lines indicate the results of a subset of the Bonferroni-corrected post hoc tests. The remainder of the post hoc tests are reported in Table 1.

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    Figure 6.

    Anisotropies in the training corpus determine population response and aftereffect magnitude. a, Population tuning curves for each animal, aligned based on the difference between the preferred direction and the stimulus, and fit with a Gaussian function. Units with preferred directions closest to the stimulus direction have the highest responses. a, c, Shaded area represents 95% confidence band of the fit. b, We biased the population tuning curves by separating trials where the test was clockwise (CW) or counterclockwise (CCW) of the adaptor. By taking the difference between the biased curves and the unbiased curve, we can see in the CW condition (dark traces) the population response curve shifts CCW, whereas in the CCW condition it is shifted CW (light traces). c, Summary of the decoder's weighting functions. The decoder produced 12 weights for each unit, one for each stimulus class. The shape of the weighting function mirrors the expected population activity to a given direction. d, We trained decoders on the biased subsets of trials and observed that the optimal weighting for each class followed the shift in population activity produced by adaptation.

Tables

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    Table 1.

    The effect of adaptation condition on decoding performancea

    Animal 1Animal 2Animal 3
    1-back2-back1-back2-back1-back2-back
    −180 vs −30−180 vs 0—−180 vs 0−150 vs 0—
    −180 vs 0−180 vs 30No significant main effect180 vs 0−150 vs 30No significant main effect
    −180 vs 60−150 vs 0——90 vs 0—
    −180 vs 90−120 vs 0——120 vs 0—
    −150 vs 0−120 vs 30————
    −120 vs 0120 vs −90————
    −90 vs 0120 vs −60————
    −60 vs 0120 vs −30————
    −30 vs 0120 vs 0————
    −30 vs 90120 vs 30————
    90 vs 0150 vs −30————
    120 vs 0150 vs 0————
    150 vs −30150 vs 30————
    150 vs 0180 vs 0————
    150 vs 30180 vs 30————
    150 vs 60————
    180 vs −30————
    180 vs 0————
    180 vs 30————
    180 vs 60————
    180 vs 90————
    • ↵aThe adaptor direction pairs listed here are those for which post hoc tests indicate significantly different decoding performances (p < 0.05).

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The Journal of Neuroscience: 36 (16)
Journal of Neuroscience
Vol. 36, Issue 16
20 Apr 2016
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Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion
Elizabeth Zavitz, Hsin-Hao Yu, Elise G. Rowe, Marcello G.P. Rosa, Nicholas S. C. Price
Journal of Neuroscience 20 April 2016, 36 (16) 4579-4590; DOI: 10.1523/JNEUROSCI.4563-15.2016

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Rapid Adaptation Induces Persistent Biases in Population Codes for Visual Motion
Elizabeth Zavitz, Hsin-Hao Yu, Elise G. Rowe, Marcello G.P. Rosa, Nicholas S. C. Price
Journal of Neuroscience 20 April 2016, 36 (16) 4579-4590; DOI: 10.1523/JNEUROSCI.4563-15.2016
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Keywords

  • adaptation
  • area MT
  • direction aftereffect
  • marmoset
  • middle temporal area
  • motion

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