Timescales of inference in visual adaptation

Abstract

Adaptation is a hallmark of sensory function. Adapting optimally requires matching the dynamics of adaptation to those of changes in the stimulus distribution. Here we show that the dynamics of adaptation in the responses of mouse retinal ganglion cells depend on stimulus history. We hypothesized that the accumulation of evidence for a change in the stimulus distribution controls the dynamics of adaptation, and developed a model for adaptation as an ongoing inference problem. Guided by predictions of this model, we found that the dynamics of adaptation depend on the discriminability of the change in stimulus distribution and that the retina exploits information contained in properties of the stimulus beyond the mean and variance to adapt more quickly when possible.

Figure 1

The time course of adaptation following an increase in temporal contrast depends on the period between contrast switches. A,B, Inhibitory synaptic current to an OFF-transient RGC (holding potential 10mV) in response to a single switch in stimulus contrast (6%-36%, mean ∼400 R*/rod/s; red). The switching period was 16 s in A and 32 s in B. C,D, Mean synaptic currents from approximately 100 trials as in A and B. Exponential fits to the response following an increase in contrast are shown in red. E, Population-averaged (n≈10 for each period) time constant (mean±sem) of the exponential fit to the response following an increase in contrast (6-36%) for all RGC types (ON, OFF-sustained, OFF-transient and ON-OFF) as a function of stimulus switching period.

Figure 2

The time course of adaptation following an increase in luminance depends on the period between luminance switches. A,B, Excitatory synaptic current to an ON RGC (holding potential -60mV; black) in response to a single switch in stimulus luminance (∼40-80 R*/rod/s with 6% contrast superimposed; red). The switching period was 3.2 s in A and 6.4 s in B. C,D, Mean synaptic input currents from approximately 50 trials as in A and B. The mean current shows a stereotyped component followed by a slow adaptation component. The exponential fit to the slower of these two components is shown in red. E, Population-average (n≈10 for each period) time constant (mean±sem) of adaptation in synaptic input currents to ON RGCs (black) recorded in whole-cell configuration or spike rate of ON RGCs (grey) recorded from different cells in the on-cell configuration as a function of stimulus switching period. F, The normalized mean responses to different switching periods diverge following the stereotyped component of the step response (shown for an ON RGC).

Figure 3

Operation of the inference model. The model is schematized for estimating the mean of the stimulus distribution (shown by the green arrow in all panels) with a Gaussian likelihood and Gaussian prior distribution. At the first time step (t=0), the average stimulus value produces an average likelihood function indicated by the green arrow. Multiplied by a large variance prior, the first posterior distribution is shown on the left after appropriate normalization. At the following time step (t=1), the previous time step's posterior distribution is substituted for the current prior and the Bayesian calculation is repeated. In this example, the stimulus ensemble is constant; thus the posterior at time t=1 is more peaked around the stimulus mean than in the previous time step. Similarly, at time t=2, the posterior becomes further peaked around the stimulus mean. At each time step the model chooses the maximum a posteriori value as its current estimate of the stimulus ensemble's parameters.

Figure 4

The inference model captures the observed dependence of time course of adaptation on stimulus switching period. A, Immediately preceding a periodic increase in luminance from 4.8 to 9.6 (arbitrary units), the model's a priori distribution of stimulus contrasts is more peaked during long switching periods (T=60; black, bottom) than during shorter switching periods (T=30; black, top). The width of the a priori distribution reflects the uncertainty about the current stimulus parameter. When the a priori distribution is narrower, more evidence is required to shift this distribution towards the new luminance following a change in the stimulus ensemble. Thus the expected first a posteriori distribution (dark gray) after observing a sample from the new stimulus ensemble is shifted farther towards the new stimulus mean during short periods (top) than long periods (bottom). Light gray traces show the expected a posteriori distribution at equally spaced time points to t=15. Normalized average model output (gain) following a periodic increase in contrast (B) or luminance (C) shows qualitative agreement with observed data (Figures 1-2). The rate of adaptation decreases with increasing switching period. Simulation results are the average of 100 trials.

Figure 5

Quantitative model predictions. For Gaussian stimuli, the inference model can be solved analytically for periodic switching stimuli (see Experimental Procedures). Model predictions were insensitive to the initial prior mean and standard deviation for standard deviations much larger than the stimulus standard deviation. Fixing the initial prior standard deviation at approx. 10 times the stimulus standard deviation, we fit the initial slope of the adaptation transient predicted by the inference model for periodic luminance steps (dashed lines) to the observed initial slope of adaptation in the synaptic input currents to ON RGCs (circles; see Supplemental Figure S4). Free parameters in the fit were effective stimulus noise contrast at each luminance level and integration time of the adapting mechanism. In correspondence with previous results, we decrease the integration time by 50% between 40 and 400 R*/rod/s. Fitted parameters were: λ ≅ 0.92, τ ≅ 0.80s, σ₄ ≅ .73, σ₄₀ ≅ 4.73, and σ₄₀₀ ≅ 47.32, where effective noise is given in arbitrary model stimulus units and λ is the scale factor that relates physical stimulus units (R*/rod/s) to arbitrary model units.

Figure 6

The time course of model adaptation depends on the discriminability of the change in stimulus ensemble. A, The rate of model adaptation following a periodic change in contrast increases as the size of the contrast step increases. Stimulus distributions for a 12% (blue) and 36% (black) contrast step from starting contrast (gray) are shown above. B, The rate of model adaptation following an increase in stimulus luminance decreases as the signal to noise ratio of the step relative the added noise (i.e. contrast) decreases. Stimulus distributions for 30% (blue) and 15% (black) contrast at the low mean are shown above. Thin lines show the starting (low luminance) distributions. C (top), Following an increase in variance of a Gaussian distribution, many random samples from the high contrast distribution (thick red) are nearly equally likely in the low contrast (thin red) and high contrast distributions. Only rarely will a random sample from the high contrast distribution be highly unlikely in the low contrast distribution. In this case, it is difficult for an observer to infer that the contrast of the distribution has changed. The same contrast change in a bimodal distribution is easily detectable, however. If the width of the peaks is small relative to the change in their separation, as shown, random samples from the high contrast distribution (thick blue) are unlikely in the previous low contrast distribution (thin blue), and thus, a contrast change is easy to detect. C (bottom), The rate of model adaptation following a matched increase in stimulus contrast is greater for a bimodal stimulus ensemble than for a Gaussian ensemble. The model used a Gaussian likelihood function in both conditions. All traces are the average of 100 simulated trials.

Figure 7

The time course of adaptation following an increase in stimulus contrast or luminance depends on the size of the contrast step or the signal to noise ratio of the luminance step. A, The time constant of an exponential fit to the mean inhibitory synaptic input current to an OFF RGC in response to a periodic (T=16s) 12-24% contrast switch (red) is larger than that (blue) for a 12-36% contrast switch. Example stimuli are shown above each trace on equal vertical scales. B, In a population of ON, OFF-transient, OFF-sustained, and ON-OFF RGCs (n=9), the timescale of adaptation to a periodic contrast step is longer when the contrast step is smaller (3.6 ± 0.6 vs. 1.6 ± 0.4 s, mean±sem; p = 0.002, paired t-test). Solid line denotes equality. Filled circles (open diamonds) denote cells comparing a 12-24% (12-17%) vs. 12-36% contrast step. The filled square marks the population average. Error bars denote standard error in the mean. C, The fitted time constant of the mean excitatory input to an ON RGC in response to a periodic (T=3.2 s) luminance step (∼40-80 R*/rod/s) when the stimulus variance was high (red) is longer than the time constant when the variance was low (blue). Stimulus variance is evaluated in the 500 ms (black bar) following the increase in luminance. Example stimuli are shown above each trace on equal vertical scales. The standard deviation of the samples in the red (low SNR) trace is approximately 1.5× the standard deviation of the blue (high SNR) trace in the 500ms immediately following the luminance step (∼18.4 vs. ∼11.2 R*/rod/s) D, In a population of ON, OFF-transient, and OFF-sustained RGCs (n=9), the timescale of adaptation to a periodic luminance step with low SNR is slower than adaptation to a luminance step with high SNR (0.24 ± 0.03 vs. 0.16 ± 0.02 s, mean±sem; p = 0.003, paired t-test). Solid line marks equality. Filled circles represent individual cells. The open square marks the population average.

Figure 8

Higher-order moments of the stimulus distribution affect the time course of adaptation to changes in stimulus contrast. A, Example record (black trace) of the excitatory synaptic input current to an ON RGC in response to a periodic (T=16s) switch in stimulus distribution from binary to Gaussian (mean and variance held constant) (top). B, The mean and r.m.s. synaptic input current during ∼50 periods like that shown (C) (top). Although there may be a change in the structure of the response distribution, there is no obvious adaptation. Adaptation ratio (a.r.) for each condition was defined as the ratio of mean synaptic current in the second and last 500ms of each condition (indicated by red and blue bars). Combining results of measurements from T=8s and T=16s periods, the adaptation ratio for stair-step Gaussian and binary conditions are not significantly different from 1 (bottom; 1.01 ± 0.02 vs. 1.00 ± 0.01 (mean±sem); p=0.91 and p=0.99, respectively). The black circle shows the population mean. Error bars show standard error of the mean. C, The time constant of an exponential fit (red) to the mean excitatory input to an ON RGC to a periodic 6-36% contrast step in a stair-step Gaussian distribution (top) is longer than the time constant of the exponential fit (blue) to the mean response to a periodic 6-36% contrast step in a binary distribution (bottom). Stimuli were updated at either 11 or 19Hz and results were averaged across all trials. Example stimuli for the Gaussian (red) and binary (blue) conditions are shown above each trace on equal vertical scales. D, Across a population of ON RGCs (n=6), the timescale of adaptation is longer for periodic contrast steps in a stair-step Gaussian distribution than for similar contrast steps in a binary distribution (2.6 ± 0.6 vs. 1.6 ± 0.4 s mean±sem; p = 0.015, paired t-test). The solid line marks equality. Each filled circle represents one cell and the open circle marks the population mean.