Spontaneous integration of temporal information: implications for representational/computational capacity of animals

Temporal scaling and computing time in neural circuits: Should we stop watching the clock and look for its gears?

Timing underlies a variety of functions, from walking to perceiving causality. Neural timing models typically fall into one of two categories-"ramping" and "population-clock" theories. According to ramping models, individual neurons track time by gradually increasing or decreasing their activity as an event approaches. To time different intervals, ramping neurons adjust their slopes, ramping steeply for short intervals and vice versa. In contrast, according to "population-clock" models, multiple neurons track time as a group, and each neuron can fire nonlinearly. As each neuron changes its rate at each point in time, a distinct pattern of activity emerges across the population. To time different intervals, the brain learns the population patterns that coincide with key events. Both model categories have empirical support. However, they often differ in plausibility when applied to certain behavioral effects. Specifically, behavioral data indicate that the timing system has a rich computational capacity, allowing observers to spontaneously compute novel intervals from previously learned ones. In population-clock theories, population patterns map to time arbitrarily, making it difficult to explain how different patterns can be computationally combined. Ramping models are viewed as more plausible, assuming upstream circuits can set the slope of ramping neurons according to a given computation. Critically, recent studies suggest that neurons with nonlinear firing profiles often scale to time different intervals-compressing for shorter intervals and stretching for longer ones. This "temporal scaling" effect has led to a hybrid-theory where, like a population-clock model, population patterns encode time, yet like a ramping neuron adjusting its slope, the speed of each neuron's firing adapts to different intervals. Here, we argue that these "relative" population-clock models are as computationally plausible as ramping theories, viewing population-speed and ramp-slope adjustments as equivalent. Therefore, we view identifying these "speed-control" circuits as a key direction for evaluating how the timing system performs computations. Furthermore, temporal scaling highlights that a key distinction between different neural models is whether they propose an absolute or relative time-representation. However, we note that several behavioral studies suggest the brain processes both scales, cautioning against a dichotomy.

Mice make temporal inferences about novel locations based on previously learned spatiotemporal contingencies

Animals learn multiple spatiotemporal contingencies and organize their anticipatory responses accordingly. The representational/computational capacity that underlies such spatiotemporally guided behaviors is not fully understood. To this end, we investigated whether mice make temporal inferences of novel locations based on previously learned spatiotemporal contingencies. We trained 18 C57BL/6J mice to anticipate reward after three different intervals at three different locations and tested their temporal expectations of a reward at five locations simultaneously, including two locations that were not previously associated with reward delivery but adjacent to the previously trained locations. If mice made spatiotemporal inferences, they were expected to interpolate between duration pairs associated with previously reinforced hoppers surrounding the novel hopper. We found that the maximal response rate at the novel locations indeed fell between the two intervals reinforced at the surrounding hoppers. We argue that this pattern of responding might be underlain by spatially constrained Bayesian computations.

The dorsal hippocampus' role in context-based timing in rodents

To act proactively, we must predict when future events will occur. Individuals generate temporal predictions using cues that indicate an event will happen after a certain duration elapses. Neural models of timing focus on how the brain represents these cue-duration associations. However, these models often overlook the fact that situational factors frequently modulate temporal expectations. For example, in realistic environments, the intervals associated with different cues will often covary due to a common underlying cause. According to the 'common cause hypothesis,' observers anticipate this covariance such that, when one cue's interval changes, temporal expectations for other cues shift in the same direction. Furthermore, as conditions will often differ across environments, the same cue can mean different things in different contexts. Therefore, updates to temporal expectations should be context-specific. Behavioral work supports these predictions, yet their underlying neural mechanisms are unclear. Here, we asked whether the dorsal hippocampus mediates context-based timing, given its broad role in context-conditioning. Specifically, we trained rats with either hippocampal or sham lesions that two cues predicted reward after either a short or long duration elapsed (e.g., tone-8 s/light-16 s). Then, we moved rats to a new context and extended the long cue's interval (e.g., light-32 s). This caused rats to respond later to the short cue, despite never being trained to do so. Importantly, when returned to the initial training context, sham rats shifted back toward both cues' original intervals. In contrast, lesion rats continued to respond at the long cue's newer interval. Surprisingly, they still showed contextual modulation for the short cue, responding earlier like shams. These data suggest the hippocampus only mediates context-based timing if a cue is explicitly paired and/or rewarded across distinct contexts. Furthermore, as lesions did not impact timing measures at baseline or acquisition for the long cue's new interval, our data suggests that the hippocampus only modulates timing when context is relevant.

Numerical averaging in mice

Rodents can be trained to associate different durations with different stimuli (e.g., light/sound). When the associated stimuli are presented together, maximal responding is observed around the average of individual durations (akin to averaging). The current study investigated whether mice can also average independently trained numerosities. Mice were initially trained to make 10 or 20 lever presses on a single (run) lever to obtain a reward and each fixed-ratio schedule was signaled either with an auditory or visual stimulus. Then, mice were trained to press another lever to obtain the reward after they responded on the run lever for the minimum number of presses [Fixed Consecutive Number (FCN)-10 or -20 trials] signaled by the corresponding discriminative stimulus. Following this training, FCN trials with the compound stimulus were introduced to test the counting behavior of mice when they encountered conflicting information regarding the number of responses required to obtain the reward. Our results showed that the numbers of responses on these compound test trials were around the average of the number of responses in FCN-10 and FCN-20 trials particularly when the auditory stimulus was associated with a fewer number of required responses. The counting strategy explained the behavior of the majority of the mice in the FCN-Compound test trials (as opposed to the timing strategy). The number of responses in FCN-Compound trials was accounted for equally well by the arithmetic, geometric, and Bayesian averages of the number of responses observed in FCN-10 and FCN-20 trials.

Behavior Stability and Individual Differences in Pavlovian Extended Conditioning

How stable and general is behavior once maximum learning is reached? To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends associative models of Pavlovian conditioning to a dynamical oscillatory model where subjects have a greater memory capacity than usually postulated, but with greater forecast uncertainty. This results in a greater resistance to learning in the first few sessions followed by an over-optimal response peak and a sequence of progressively damped response oscillations. We detected the first peak and trough of the new learning curve in our data, but their dispersion was too large to also check the presence of oscillations with smaller amplitude. We ran an unusually long experiment with 32 rats over 3,960 trials, where we excluded habituation and other well-known phenomena as sources of variability in the subjects' performance. Using the data of this and another Pavlovian experiment by Harris et al. (2015), as an illustration of the principle we tested the theory against the basic associative single-cue Rescorla–Wagner (RW) model. We found evidence that the RW model is the best non-linear regression to data only for a minority of the subjects, while its dynamical extension can explain the almost totality of data with strong to very strong evidence. Finally, an analysis of short-scale fluctuations of individual responses showed that they are described by random white noise, in contrast with the colored-noise findings in human performance.

Probabilistic Information Modulates the Timed Response Inhibition Deficit in Aging Mice

How interval timing is affected by aging constitutes one of the contemporary research questions. There is however a limited number of studies that investigate this research question in animal models of aging. The current study investigated how temporal decision-making is affected by aging. Initially, we trained young (2–3 month-old) and old C57BL/6J male mice (18–19 month-old) independently with short (3 s) and long (9 s) intervals by signaling, in each trial, the hopper associated with the interval that is in effect in that trial. The probability of short and long trials was manipulated (0.25 or 0.75) for different animals in each age group. During testing, both hoppers were illuminated, and thus active trial type was not differentiated. We expected mice to spontaneously combine the independently acquired time interval-location-probability information to adaptively guide their timing behavior in test trials. This adaptive ability and the resultant timing behavior were analyzed and compared between the age groups. Both young and old mice indeed adjusted their timing behavior in an abrupt fashion based on the independently acquired temporal-spatial-probabilistic information. The core timing ability of old mice was also intact. However, old mice had difficulty in terminating an ongoing timed response when the probability for the short trial was higher and this difference disappeared in the group that was exposed to a lower probability of short trials. These results suggest an inhibition problem in old mice as reflected through the threshold modulation process in timed decisions, which is cognitively penetrable to the probabilistic information.

Dynamics of Spatio-Temporal Binding in Rats

Time and space are commonly approached as two distinct dimensions, and rarely combined together in a single task, preventing a comparison of their interaction. In this project, using a version of a timing task with a spatial component, we investigate the learning of a spatio-temporal rule in animals. To do so, rats were placed in front of a five-hole nose-poke wall in a Peak Interval (PI) procedure to obtain a reward, with two spatio-temporal combination rules associated with different to-be-timed cues and lighting contexts. We report that, after successful learning of the discriminative task, a single Pavlovian session was sufficient for the animals to learn a new spatio-temporal association. This was seen as evidence for a beneficial transfer to the new spatio-temporal rule, as compared to control animals that did not experience the new spatio-temporal association during the Pavlovian session. The benefit was observed until nine days later. The results are discussed within the framework of adaptation to a change of a complex associative rule involving interval timing processes.