Representation of interval timing by temporally scalable firing patterns in rat prefrontal cortex

Abstract

Perception of time interval on the order of seconds is an essential component of cognition, but the underlying neural mechanism remains largely unknown. In rats trained to estimate time intervals, we found that many neurons in the medial prefrontal cortex (PFC) exhibited sustained spiking activity with diverse temporal profiles of firing-rate modulation during the time-estimation period. Interestingly, in tasks involving different intervals, each neuron exhibited firing-rate modulation with the same profile that was temporally scaled by a factor linearly proportional to the instructed intervals. The behavioral variability across trials within each task also correlated with the intertrial variability of the temporal scaling factor. Local cooling of the medial PFC, which affects neural circuit dynamics, significantly delayed behavioral responses. Thus, PFC neuronal activity contributes to time perception, and temporally scalable firing-rate modulation may reflect a general mechanism for neural representation of interval timing.

Fig. 1.

Two-duration time-estimation task. (A) Schematic diagrams for the behavioral tasks. To initiate a trial, the rat poked its nose into the waiting port and held it at the port for a pseudorandom delay of 0.5–1.5 s before the sound signal was given. In instructive trials, a standard sound (of 1.5- or 2.5-s duration) was played. Rats were rewarded with a small drop of water delivered in the reward port only when they exited from the waiting port within the reward window (from 0.15 or 0.25 s before to 1 s after the sound offset for the 1.5- and 2.5-s tasks, respectively) and then poked into the reward port immediately after exit. During test trials, which were randomly interleaved with instructive trials in each session, a prolonged sound was played and reward was given only if the rat exited the waiting port within the same reward window as in the instructed trials. (B) Performance from one experimental session in 1.5-s instructive trials and test trials. The histogram shows distribution of exit times. Shadow area indicates the reward window. (C) Performance of the same rat in the same experimental session as shown in B in 2.5-s blocks. (D and E) Performance of four rats in the two-interval time-estimation task. Histograms and scatter plots show the distribution of mean exit time from four rats in 77 sessions (13–30 sessions per rat) of performing the two-duration timing task. The bin size in all histograms is 100 ms.

Fig. 2.

Spiking activities of three neurons (A–C) in mPFC during the task. (A–C, Upper) Raster plots of spiking activity for 15 consecutive trials, with each row corresponding to one trial (tick mark, single spike) and the time aligned by the time of waiting port entry (Left), sound onset (Center), or waiting port exit (Right), respectively. (A–C, Lower) PSTH from the same session as the raster plot.

Fig. 3.

Temporal scaling of firing patterns in mPFC neurons. (A) Example of spiking activity of one mPFC neuron in a rat performing 1.5-s trials. (A, Upper) The raster plot of spiking activity for 25 consecutive trials, with each row corresponding to one trial (tick mark: single spike). Trials are aligned by the sound-onset time (zero). (Orange squares, port-entry time; cyan squares, port-exit time.) (A, Lower) PSTH (Materials and Methods) from the same session as the raster plot. The green curve shows the PSTH of neural activity in 1.5-s test trials, and the red dashed curve is the temporally scaled PSTH of 2.5-s test trials. The green vertical line shows the mean exit time. (B) Example of neural activity from the same neuron as shown in A during the same experimental session in 2.5-s test trials. (C) Examples of neuron showing the temporally scalable activity during time-estimation period. The green curve is PSTH of neural activity in 1.5-s test trials, and the blue curve is PSTH of neural activity in 2.5-s test trials. The red dashed curve is PSTH of neural activity in 2.5-s test trials scaled by the ratio between the mean exit times in 1.5-s test trials and that in 2.5-s test trials. Green vertical line, the mean exit time in 1.5-s test trials; blue vertical line, the mean exit time in the 2.5-s test trials. (D) Calculation of the best scaling factor, based on the minimum MSEs (Materials and Methods). (E) Distribution of the best scaling factor during time-estimation period (Materials and Methods); 45% (111/247) of the best scaling factors fell within ±10% from the mean exit-time ratio of 0.67 (colored histogram). (F) Distribution of best scaling factor during the random-delay period from two-interval time-estimation task. Data consist of 247 neurons from 77 behavioral sessions of four rats.

Fig. 4.

Behavioral variability and temporal scaling of neural activity in mPFC. (A and B) Two examples of neural activity in 2.5-s test trials. Trials are grouped based on the exit times into the early-exit group and late-exit group. (A and B, Top and Middle) Raster plots of spiking activity in early-exit (Top) and late-exit (Middle) trials separately are shown. (Orange squares, port-entry time; cyan squares, port-exit time.) (A and B, Bottom) The green and blue curves depict PSTH of early- and late-exit trials, respectively, and the red dashed curve represents temporally scaled PSTH of the late-exit trials. (C) Distribution of the best scaling factor between early- and late-exit trials in 2.5-s testing trials (Materials and Methods); 83% (206/247) of the scaling factors fell within ±5% from the mean exit time ratio 0.81 (colored histogram). Data consist of 247 neurons from 77 behavioral sessions of four rats. (D) Correlation between exit-time ratios and best scaling factors. Trails were divided into eight groups according the exit-time values, with the same number of trials in each group. Histograms show the distribution of best scaling factor between seven later exit groups and the first fastest exit group. Black circles mark the value of mean exit-time ratio and best scaling factor, both determined by the Gaussian fit of individual distributions. Black line, best linear fit of the black circles (r² = 0.99).

Fig. 5.

Cooling of mPFC slows the time-estimation behavior. (A) Measurement of local brain cooling. (A, Upper) Temperature changes at ∼250 μm from cooling probe using different cooling currents. (Blue and red squares, average data during the cooling and noncooling periods; star, cooling current used for behavioral experiments.) (A, Lower) Temperature changes following the onset and offset of the cooling current. (B, Left) Schematic diagram of the cooling device and position of cooling probes. (B, Center) Cumulative percentage plot of exit times with and without cooling for example behavioral session. (B, Right) Histograms summarizing results of mean exit times in 55 sessions from six rats. (C) The same cooling treatment at the motor cortices resulted in no effect on exit times. Data are from four rats in 40 sessions.