How Event Timers Might Work

Given the hippocampal evidence, it seems likely that the hippocampus operates as a kind of ticking backdrop upon which episodic events can be hung until they are stored in a longer-term reference memory. Certainly hippocampal patterns are utilized in numerous ways, as previously assigned place fields do fire in new locations over the lifetime of a rat (Mizumori, et al., 1996).

This kind of hippocampal tagging has been modeled in detail for sequential spatial memory using a large-scale simulation of hippocampal function (Wallenstein and Hasselmo, 1997; Wallenstein et al., 1998). Multicompartmental pyramidal cells are shown to have synchronizing behavior over multitrial learning, and this is suggested as a mechanism for sequential learning. The pyramidal cells, which fire chaotically before the learning trial, use this prerandomization to settle into nonpre-dictable and location-specific patterns of firing. This is reminiscent of the kind of unsupervised self-organization exhibited by Kohonen networks (Haykin, 1994). It is different in that neighboring events in space share similar contextual patterns of hippocampal cell firing. In this way, when a sensory stimulus arises that is unfamiliar, the cells that fire in response to that stimulus partially stimulate the "memory" of familiar neighboring events.

Is it possible that temporal interval discrimination works by a similar mechanism? Coexisting with place fields, we would expect to find an analogous kind of "time field." By necessity, the time field could not operate exactly like the place field. In space, sensory input is constant and animals could run spatial information through the hippocampal filter persistently. This would perpetuate the synchronous cell firing in a way that time fields might be unable to do. For example, in order for a rat to learn the duration between signal and reward, it must have a sense of time. In space, the interval is exogenously applied — the space field moves and the sensory cells respond. In time, the interval must be measured by an internal clock.

What could the clock be if it is not related in some way to the circadian clock? The observation of high-frequency intracortical oscillations by electroencephalo-graph (EEG) provides a basis for a possible clock hand in vertebrates. It has been suggested before that composite cortical waveforms measured by EEG operate as a pacemaker in duration timing (see Artieda and Pastor, 1996; Pouthas, this volume; Sakata and Onoda, this volume; Treisman et al., 1994). Unfortunately, EEGs measure waveforms produced by populations of cells, and it is often very difficult to isolate particular areas of the brain for specific analyses. Nonetheless, evidence for the EEG pacemaker hypothesis has been provided by work showing interactions between auditory click rates, certain EEG components, and the simultaneous assessment of duration (Treisman et al., 1994).

A more specific neural pacemaker central to the hippocampus is provided by observations of theta and gamma oscillations from in vivo recordings of the hippocampus (Wallenstein and Hasselmo, 1997). In the sequential place field model referred to above, theta and gamma oscillations are produced by GABAergic receptor inhibition of recurrent collaterals among pyramidal cells and between pyramidal cells and nonpyramidal neurons. The effective nature of this system is to iterate and check at each time step, such that internal and external signals are integrated with the background hippocampal pattern in a meaningful way. Cells may oscillate at different or longer intervals and become associated with the duration when in specific states, such that a series of population patterns is gradually learned over progressive trials.

Iterate and check, however, may be only half of the story. Animals involved in temporal training tasks often behave in a peculiar but stereotypical way that might be further related to the spatio-temporal integration of the hippocampus. This behavior is characterized by seemingly unrelated activities between the stimulus and the reward. For example, a rat might chew its tail, a monkey might jump around its cage in a repetitive way, and a human might tap her finger or shake her head. It is also observed that animals engaging in these collateral behaviors are more efficient in their response time than animals that do not perform these behaviors (Richelle and Lejeune, 1980). These behaviors could act as a kind of context counting. Assuming the animal is unable to count (or asked not to, in the case of humans), it may, in the process of learning the interval, learn sequential behaviors associated with the particular sequence of population patterns in the hippocampus. This makes perfect sense in terms of the spatio-temporal aspects of hippocampal learning — it provides an efficient way to turn time into space, which can then be sensed continuously over the interval. It also provides a physiological basis for the behavioral theory of timing and multiple-timescale theory. Not only does the animal iterate and check, it reinforces the dynamic pattern of cellular events by engaging in context-specific behavior.

This contextual theory of timing may also explain why humans appear to have different timers for long and short durations — one temperature compensated and one not, respectively (Aschoff, 1998). The explanation may be that for short intervals, animals count either behaviors or some internally registered series, but for longer intervals, especially in humans, they do not keep track of time, but reflect on how much time should have passed given the events that have taken place in the interval. Whereas counting provides instantaneous and a more likely temperature-modulated mechanism for measuring time, reflection is initiated on a more variable schedule and is more a measurement of what the interval looked like after the fact than what it actually felt like while it was happening. That is to say, reflecting is a different kind of event timer than counting.

Evidence for an embedded, context-specific memory is supported by research on temporal memory in humans (Friedman, 1993). An appropriately reflective model for how events are recollected is the theory of reconstructive memory. Reconstruction of remembered events is based on recognition of an event with respect to extant cues during the event interval. Reconstructive memory explains otherwise anomalous characteristics of memory, like primacy (enhanced memory for initial events), scale effects (e.g., more accuracy for time of day than month or year), and facilitative effect of background temporal structure (Friedman, 1993). Subjects may be self-generating temporal structure through collateral behaviors. Reconstructive memory also supports a bias toward memory of events with more endogenous and external cues, as in 24-h and seasonal memories (see Section 4.3.4).

Because most organisms do not have a hippocampus, I would now like to discuss a smaller timer, one that is easily carried by individual cells. It shares some molecular features with that of the circadian clock, but it is linear in its response. It involves the activity of a single protein induced by a specific stimulus, which is then followed by a decay of the protein back to its original state. The protein activity could involve its production (as in circadian rhythms), its mobilization to a specific area of the cell (e.g., ion channels localized to the membrane of a neuron), or a structural change in the protein (e.g., exposing a protein binding domain). The metabolic nature of the decay timer also makes it agreeable with temperature effects on clock speed. These "clocks," for which I will use the general term decay timers, have been described in the control of countless molecular interactions (e.g., Ishijima and Yanagida, 2001; Takai et al., 2001) and are potentially our most primitive event timers.

An example of an ecological problem that is most feasibly solved by a decay timer is that of local search time in the absence of further resource acquisition. For example, if an animal in the presence of a reinforcing signal (e.g., food or mating pheromone) suddenly finds that the signal is reduced or absent, it must make a decision about how long to continue searching for the reinforcer in its present location before it decides to search elsewhere. This behavioral strategy of looking first locally and then globally is called an area-restricted search and has been observed in a wide variety of organisms (Kareiva and Odell, 1987; White et al., 1984). Underlying this strategy is a clock that keeps track of the time elapsed since the animal last encountered food. A decay timer would be appropriate for this behavior, as it could be reset by food and then directly control turning behavior by modulating proteins that control turning rates.

The run-and-tumble behavior of Escherichia coli bacteria follows this description, involving a phosphorylation cascade that begins with membrane proteins that bind to extracellular ligands. In the absence of a stimulating resource, these membrane proteins act through phosphorylation of downstream secondary messenger proteins, which then bind to the flagellar motor components to cause flagellar reversal and tumbly swimming (Stock and Surette, 1996). The phosphorylation schedule of these proteins is on the order of seconds, and the whole system works like clockwork to move the animals up concentration gradients.

While this is an example of a timed endogenous behavior, it is not an example of an interval timer, because the bacteria do not learn the duration of an external signal. If the animal could change the temporal dynamics of its turning in response to different resource environments, this would provide us with an understanding of how external events that are not necessarily timed with an event timer can lead to developmental changes in optimally timed behavior patterns. This developmental retiming of behaviors is likely to be a critical step in the evolution of interval timers. Promising organisms for this kind of study are Drosophila melanogaster and the nematode Caenorhabditis elegans, which are convenient organisms for molecular and genetic study that also have the wherewithal to search for food when it is no longer around. I do not believe that we are likely to find fully formed interval timers in these animals, but instead the molecular machinery from which interval timers are constructed.

Experiments designed to distinguish between decay timers and time fields, without taking into account the molecular machinery involved, are probably bound to fail. The reason is that the time field model is perfectly capable of acting like a decay timer and, in fact, undoubtedly consists of numerous decay timers that set the context for contiguous spatial and temporal phenomenon (Young and McNaughton, 2000). However, there is a rather deep distinction between these different event timers in the form of the credit-assignment problem as it is established in the psychological literature (Machado, 1997; Staddon and Higa, 1999). The credit-assignment problem is based on the animal's attention to the relevant reward cue. How, for example, does a rat learn that the onset of the red light signals food in 40 sec and that changes in air temperature are unrelated? I believe one of the premises of the decay timer must be that the credit is assigned in the evolutionary history of the animal. Bacteria, bees, and other invertebrates do not learn to assign a particular stimulus to a decay timer. That is given to them for free. On the other hand, the decay timers in the hippocampus are actually used to solve the credit-assignment problem. They do this by maintaining the firing rates of certain pyramidal cells even after the response stimulus is gone. This allows contiguous events in time and space to be contextually associated with neighboring events (Wallenstein et al., 1998). The relevant cues to which any given event timer is sensitive are intimately related to an evolutionary bias for certain environmental cues. Thus, negative results on event timing experiments may be limited to telling us about a very specific environmental stimulus (see "Navigation," Section 4.5.2).

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