The Timing of Reward-Seeking Action Tracks Visually Cued Theta Oscillations in Primary Visual Cortex

Abstract

An emerging body of work challenges the view that primary visual cortex (V1) represents the visual world faithfully. Theta oscillations in the local field potential (LFP) of V1 have been found to convey temporal expectations and, specifically, to express the delay between a visual stimulus and the reward that it portends. We extend this work by showing how these oscillatory states in male, wild-type rats can even relate to the timing of a visually cued reward-seeking behavior. In particular, we show that, with training, high precision and accuracy in behavioral timing tracks the power of these oscillations and the time of action execution covaries with their duration. These LFP oscillations are also intimately related to spiking responses at the single-unit level, which themselves carry predictive timing information. Together, these observations extend our understanding of the role of cortical oscillations in timing generally and the role of V1 in the timing of visually cued behaviors specifically.SIGNIFICANCE STATEMENT Traditionally, primary visual cortex (V1) has been regarded as playing a purely perceptual role in stimulus-driven behaviors. Recent work has challenged that view by showing that theta oscillations in rodent V1 may come to convey timed expectations. Here, we show that these theta oscillations carry predictive information about timed reward-seeking actions, thus elucidating a behavioral role for theta oscillations in V1 and extending our understanding of the role of V1 in decision making.

Keywords: decision making; intertemporal choice; primary visual cortex; sensory cortex; theta oscillations; timing.

Figure 1.

Oscillatory states are present in V1 during a visually guided timing task. a, Schematic of the task reward structure in which waiting longer to lick after a visual stimulus (time 0) results in a larger volume of water delivery at the lick tube. Maximum delivery occurs at 1.5 s and drops to 0 thereafter, so that animals must time their lick. b, Average voltage trace in the LFP taken from an electrode in an example session, with a green bar overlaid to indicate when the visual stimulus was on. The voltage values seem to oscillate for ∼1 s poststimulus. c, Voltage traces per trial for the example session. d, Average time–frequency representation of the trials in c. e, Concentrated energy through time of the trials in c. f, Empirical probability density function (PDF) for the log(mean concentrated energy) scores on each trial shown in e are shown in blue. The mean concentrated energy is calculated in a 200–700 ms window poststimulus. A unimodal Gaussian fit is shown in red (top) and a bimodal Gaussian fit is shown in green (bottom). g, The distribution of the difference in AIC values for each model across all sessions is left shifted, indicating an overall preference for the bimodal model. The dotted lines around 0 are the bounds at which the relative likelihood of a model compared with another model is 5%. h, Sorted concentrated energy scores for the example session with a dotted line indicating the threshold used for determining whether a trial has an oscillation. If the concentrated energy score crosses this threshold during the 200–700 ms window poststimulus, it is considered to have an oscillation. I, Raw voltage trace in c sorted by the mean concentrated energy in the analysis window on a given trial. Oscillations were detected for trials above the dotted line.

Figure 2.

Wait time precision is higher during oscillatory states. a, Concentrated energy values with first wait times (white squares) poststimulus overlaid for each trial of an example session in chronological order (top) and sorted by oscillation duration (bottom). The dashed black line is the threshold for being categorized as oscillatory. b, Empirical cumulative density functions for the first lick times (wait times) poststimulus on oscillation (black) and nonoscillation (green) trials in a. c, Histogram of the difference in lick variability on oscillation and nonoscillation trials for each session recorded on a given electrode. d, Differences in wait time variability on oscillatory and nonoscillatory trials for all sessions and channels of trained (blue) and naive (red, animals. e, Top, Lick variability decreases as the number of electrodes on which an oscillation was detected increases for a given trial. Standard error bars are shown in black, with the regression line in red. Bottom, Percentage of water obtained over baseline (defined as trials in which no oscillations were detected on any electrodes) increases as the number of electrodes showing an oscillation increases. Standard error bars are shown in black, with the regression line in red.

Figure 3.

Trial and session statistics do not account for differences in lick precision between oscillatory and nonoscillatory trials. a, Differences in wait time variability (blue) are considerably larger than differences in stimulus onset time (from nose poke entry) variability (red) on trials in which there was no licking prestimulus. b, Concentrated energy scores taken from a 50 ms window before the first lick on oscillation (blue) and nonoscillation (yellow) trials from all sessions and channels. c, Differences in lick variability between oscillatory and nonoscillatory trials for trials within a given range of times to stimulus onset (from nose-poke entry) from 0 to 2.5 s in 100 ms steps collapsed across all sessions and channels. d, Differences in lick variability between oscillatory and nonoscillatory trials for trials within a given range of intertrial intervals from 0 to 10 s in 100 ms steps collapsed across all sessions and channels. e, Differences in lick variability between oscillatory and nonoscillatory trials for a given trial number in a session collapsed across all sessions and channels.

Figure 4.

Wait time correlates with oscillation duration in trained animals. a, Concentrated energy values with first lick times (wait times) overlaid (pink squares) on trials sorted by oscillation duration. b, Scatter plot showing the relationship between oscillation duration and wait times for the trials in a with a regression line shown in orange. c, Distribution of the slopes of regression for each session recorded on a given channel. d, Empirical cumulative distribution of the slopes of regression for all sessions and channels from naive (red) and trained (blue) animals. e, Null distribution of slopes for the sessions taken from the trained cohort calculated by randomly shuffling the relationship between the wait time and oscillation duration 1000 times. The actual mean slope across session is shown by the black dotted line. f, Slope of regression decreases as the percentage of trials with the strongest oscillations is increased systematically. To do this systematic sweep, we sorted trials recorded on a given session/electrode by their mean concentrated energy and took the top x percentage of trials. Therefore, the x-axis ranges from 5% (in which only the trials in the top 5% of oscillation strength are included) to 100% (in which all trials are included).

Figure 5.

Wait time correlates with oscillation duration across a wide range of metrics and parameters. a, LFP trace from a single trial with a 250 ms gray bar overlaid to highlight the VEP. b, Percentage of variance explained by a regression of wait time against oscillation duration (brown) or VEP amplitude (green) relative to a model containing both variables.

Figure 6.

Neural oscillations occur during LFP oscillations. Spike rasters (top) for an example neuron on all trials (a), oscillation trials (b), and nonoscillation trials (c) of a session are shown. The PSTH for each group is shown below.

Figure 7.

Neurons spike at a consistent phase of the oscillations in the LFP. a, p-values for the null hypothesis that there is no difference in spike distributions between trials with and without an oscillation in the LFP for each neuron. The dotted red line indicates where p = 0.05. b, ADI, a measure of the difference in the level of autocorrelation between spike-separated oscillation and nonoscillation trials, is considerably higher in neurons for which the null hypothesis stated in a is rejected (blue) than in those for which it is not (red). c, Distribution of the ADI across all neurons is right shifted, indicating that the spike train autocorrelation is higher on LFP oscillation trials than nonoscillation trials. d, Heat maps showing the filtered LFP (top) and phase angle (bottom) on LFP oscillation trials, with spikes from the example neuron in Figure 7 overlaid (white squares). e, Polar plots indicating the distribution of LFP oscillation phase angles at which spikes occur for the example neuron (left) and the mean phase angle for each neuron in the population (right).

Figure 8.

Neural oscillations are predictive of timing performance. a, Empirical cumulative distribution functions for the difference in lick variance on spike-separated oscillation and nonoscillation trials (var[osc] − var[non-osc]) for individual neurons (blue) and neural ensembles (red). b, Relationship between neural ensemble size and difference in lick variance on each session (gray dots) shown with a regression line (dotted black line) and session means per ensemble size (pink dots).

Figure 9.

Oscillation prevalence is related to experienced reward rate. a, Distributions of t-statistics across sessions for several variables in a logistic regression model in which the dependent variable is the fraction of electrodes displaying an oscillation on a given trial (of six). Of the variables considered here, the distribution of t-statistics for the intertrial interval (red line), the time between exit on the previous trial to subsequent trial initiation, is the farthest shifted from zero. b, Relationship between the probability of oscillation and the intertrial interval (exit to poke time). Probabilities are calculated by taking the number of oscillations divided by the total number of observations (i.e., all analyzed channels and trials) falling within a range of intertrial intervals 500 ms wide, sweeping from 0.5 to 30 s. c, Empirical cumulative distribution functions (CDFs) for the ROC values across sessions associated with the difference in various behavioral rates (reward, trial, and photic between oscillation and nonoscillation trials). These CDFs correspond to the exponential filter (used to calculate the rates) yielding the maximal mean ROC (see Materials and Methods). d, Mean ROC values for each rate variable across sessions for each exponential filter size tested. Daggers denote where the mean ROC value associated with reward rate is significantly different from that associated with trial rate.