Autocorrelation Structure in the Macaque Dorsolateral, But not Orbital or Polar, Prefrontal Cortex Predicts Response-Coding Strength in a Visually Cued Strategy Task

Abstract

In previous work, we studied the activity of neurons in the dorsolateral (PFdl), orbital (PFo), and polar (PFp) prefrontal cortex while monkeys performed a strategy task with 2 spatial goals. A cue instructed 1 of 2 strategies in each trial: stay with the previous goal or shift to the alternative goal. Each trial started with a fixation period, followed by a cue. Subsequently, a delay period was followed by a "go" signal that instructed the monkeys to choose one goal. After each choice, feedback was provided. In this study, we focused on the temporal receptive fields of the neurons, as measured by the decay in autocorrelation (time constant) during the fixation period, and examined the relationship with response and strategy coding. The temporal receptive field in PFdl correlated with the response-related but not with the strategy-related modulation in the delay and the feedback periods: neurons with longer time constants in PFdl tended to show stronger and more prolonged response coding. No such correlation was found in PFp or PFo. These findings demonstrate that the temporal specialization of neurons for temporally extended computations is predictive of response coding, and neurons in PFdl, but not PFp or PFo, develop such predictive properties.

Figure 1.

Behavioral task, cues, and recording sites. (A) Sequence of task events for the visually cued strategy task, temporally ordered from left to right. Each dark gray rectangle represents the video screen as viewed by the monkey. Dashed lines indicate the target of the monkey’s gaze. (B) Strategy cues presented to the monkey. Each colored shape instructed the strategy to be applied. (C–E) Recording zones for PFp (C), PFdl (D), and PFo (E). Fb, feedback; LOS, lateral orbital sulcus; MOS, medial orbital sulcus; PS, principal sulcus; AS, arcuate sulcus.

Figure 2.

Spike count autocorrelation decay computed using 50-ms time bins in a 1-s time window of the fixation period (mean ± standard error of the mean [SEM]). The solid lines are the exponential fits. The autocorrelation value of PFp for the shortest time lag of 50 ms shows refractory adaptation and it has been excluded from the fit procedure. The intrinsic timescale obtained from the exponential fit is shown for each brain area.

Figure 3.

Example of 2 neurons with long and short intrinsic timescales and population analysis. (A–D) Example neuron with long intrinsic timescale encoding the response in the delay period and long intrinsic timescale population analysis. (A) Autocorrelation decay in the fixation period. The red line is the exponential fit with timescale τ = 451 ms. (B) Example of high activity maintenance during the fixation period by dividing high and low trials activity. Trials were subdivided in 2 groups according to the spike count computed in the 100-ms interval from 400 to 500 ms after fixation onset, as indicated by vertical dashed lines. The difference in activity was maintained for nearly the entire fixation period—not merely in the 100-ms interval. Raster plot for low (purple) and high (green) activity trials aligned to fixation onset. Each dot indicates when a spike occurred. Spike density averages are shown on top of the raster. (C) Raster plot for right (blue) and left trials (red) aligned to cue onset. Spike density averages are shown on top of the raster. The ROC values for the response are shown at the bottom. (D) Difference between high and low activity groups for the population of long timescales neurons. Dashed lines indicate the 100-ms interval from 400 to 500 ms after fixation onset. The decay constant (β) from the exponential fit is reported with the 95% C.L. (E–H) Example neuron with short intrinsic timescale and no response-related activity in the delay period and population analysis. (E) Same analysis as in A. The red line is the exponential fit with timescale τ = 69 ms. (F) Example of low activity maintenance during the fixation period. The low and high activity trials were defined as in B. The difference in activity between the 2 groups of trials did not extend beyond the period after the 100-ms interval. (G) Same analysis as in C. (H) Same analysis as in D for the population of short timescales neurons.

Figure 4.

Intrinsic timescale during fixation predicts neuronal response selectivity in PFdl. (A) Time course of the ROC for the spatial response aligned on the cue (left) and feedback (right) onsets. The population of neurons with long intrinsic timescale (blue line, mean ± SEM) has higher ROC values for the response than those with short intrinsic timescale (red line, mean ± SEM). The black lines indicate a significant difference in the late–delay and feedback periods (Kruskal–Wallis, P < 0.05). (B) Schematic of cross-temporal correlation between ROC values. Each pixel in the triangle is the correlation coefficient between the ROC values computed for 2 time bins for the neuronal population. Sustained population response coding is reflected by high positive correlation, a transient population response is indicated by low correlation, and an inversion of population coding is manifested by negative correlation. (C) Cross-correlation between non-normalized ROC values at time t and t + Δ for PFdl neurons with long intrinsic timescale. Each pixel represents the correlation coefficient between two 50-ms time bins. The population code for the response was maintained for nearly the entire delay but was more transient in the feedback period. (D) Significant P values of each pixel for Pearson’s correlation coefficient in C (black pixel, P < 0.001).The green triangles identify the early–delay and late–delay periods. (E) Cross-correlation between non-normalized ROC values at time t and t + Δ for PFdl neurons with short intrinsic timescale. The population code for the response is inconsistent through the late–delay period. The correlations lose significance in the late–delay period. (F) Significant P-values of each pixel for Pearson’s correlation coefficient in E (black pixel, P < 0.001). The green triangles identify the early–delay and late–delay periods. (G) Comparison between the correlation coefficients at each corresponding pixel (50 ms pixel length) of the short and long timescale populations of Fig 4C,E using Fisher's r-to-z transformation. All pixels with correlation coefficients significantly different between the 2 populations (P < 0.001) are highlighted in black. It is evident that a significant difference emerged in the late–delay period (green triangle), in which the response coding of the neurons with long intrinsic timescale was consistent and sustained to a greater extent than in neurons with short intrinsic timescales.