The right time to learn: mechanisms and optimization of spaced learning

Abstract

For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning.

Figure 1. Early conceptual model of how learning trace dynamics generate an optimal interval

As described by the early model of Landauer, spaced training is more effective than massed training at strengthening some form of trace corresponding to memory storage in the brain, although this conceptual model does not posit a biochemical or structural form for the trace. This model posits that memory formation becomes more effective with longer inter-stimulus intervals between training sessions because of decreasing temporal overlap between successive, short-lived learning traces. These learning traces do not themselves constitute a memory. However, their net effect contributes to the formation of a long-lived memory trace. a–c | Learning traces elicited by two successive trials are shown. The model assumes that, for each value of the inter-trial interval (ITI) length, a quantity denoted ‘net gain owing to the reinforcing trial’ is proportional to the red area. Shorter intervals are associated with more overlap of learning traces and less net gain. Thus, a reinforcing trial is most effective after a refractory period following the preceding trial. For this conceptual model, units for amplitude and time are arbitrary. d | A greater summed effect, or net gain, of reinforcing trials occurs for longer inter-stimulus intervals. The effect reaches a plateau for long intervals as the overlap between successive learning traces reaches zero. e | Over longer times, a different quantity — the probability that a reinforcing trial will be effective at all in reactivating processes that constituted the preceding learning trace — declines. f | An optimum interval for maximizing the strength of the long-lived memory trace results when the greater net gain of reinforcement at longer intervals (from part d) is multiplied by the slowly declining probability that a reinforcement will reactivate a previous learning trace (from part e). The optimum interval for net learning is the one that produces the peak level of the trace in part f.

Figure 2. Model and hypotheses describing synaptic strengthening during spaced learning

a | In the refractory-state model, spaced stimuli (left panel; stimulus 1, followed substantially later by stimulus 2) cumulatively strengthen a memory trace (blue time course). By contrast, massed stimuli (right panel; stimulus 1 followed shortly after by stimulus 2) fail to cumulatively strengthen the memory trace. b | The cumulative synaptic strengthening in spaced training may be due to progressive enhancement of long-term potentiation (LTP), which could result from successive increases in the strength of the same synaptic contacts (shown here as successive increases in the volume of the same postsynaptic dendritic spine). Thus, in one of two current hypotheses describing synaptic strengthening during spaced learning, stimulus 1 enlarges a population of spines. If stimulus 2 follows shortly after the first stimulus (as in massed training), it cannot further affect spines. However, if stimulus 2 comes after a refractory period (as in spaced training), it can further enlarge the same population of spines. c | Alternatively, enhancement of LTP could result from successive rounds of strengthening of new synaptic contacts. Thus, in the second current hypothesis, stimulus 1 only enlarges a subset of affected spines, but primes additional spines. If stimulus 2 follows shortly after stimulus 1 (as in massed training), it has no effect. If stimulus 2 comes later (as in spaced training), it does not further enlarge the first subset of spines. Instead, stimulus 2 enlarges those spines that were primed, but not enlarged, by stimulus 1.

Figure 3. Different mechanisms may underlie enhancement of learning by spaced intervals of widely varying lengths

For relatively brief inter-trial intervals (ITIs) (bottom trace), successive trials may coincide with and reinforce peak second messenger levels generated by preceding trials. In each trace, individual rectangles represent individual trials, and converging lines between traces represent the lengthening of timescales as one moves upwards in the illustration. For somewhat longer ITIs (several minutes to ~1 hour), successive trials may reinforce the peak activities of kinases elicited by preceding trials and also elicit long-term potentiation of primed dendritic spines. Intervals of this length may also, in the hippocampus, be needed to allow replacement of inactivated receptors at stimulated spines, enabling succeeding stimulus repetitions to potentiate those spines. For intervals of ~1 hour or more, succeeding trials may also align with peaks in transcription factor activity and gene expression owing to preceding trials. For the longest ITIs (many hours or longer), succeeding trials may reactivate and thereby further potentiate consolidated memory traces. All of these processes are likely to contribute to the consolidation of long-term memory, in many if not all species. However, depending on the ITI length used in a particular spaced learning protocol, the dynamics of a particular type of process (for example, kinase activation) may contribute in particular to the efficacy of spaced learning. Also, trials at one temporal domain (for example, 1 day) may be unitary events, but also may constitute a block of spaced trials from another temporal domain (for example, minutes to hours). For example, an effective protocol for long-term sensitization training in Aplysia californica is the use of four trials with an ITI of 30 minutes, with this block repeated four times with a 1-day ITI. Thus, some effective training protocols consist of a hierarchy of temporal domains of training sessions, with briefer sessions embedded within longer ones. In this illustration, intervals are shown with regular spacing, but more effective learning may occur with irregular spacing (FIG. 4).

Figure 4. Dynamics of a model that has successfully predicted greater efficacy for a learning protocol with irregularly spaced intervals

a | A simplified mathematical model describes the activation and effects of two key kinases necessary for long-term facilitation (LTF), a cellular correlate of a simple form of learning, long-term sensitization. Brief applications of 5-hydroxytryptamine (5-HT) activate protein kinase A (PKA) by increasing the levels of the secondary messenger cyclic AMP, and activate the extracellular signal-regulated kinase (ERK) isoform of mitogen-activated protein kinase (MAPK) via a RAS–RAF–MEK cascade. PKA and ERK interact, at least in part, via the phosphorylation of transcription factors, to induce LTF. In the model, the variable ‘inducer’ represents the PKA–ERK interaction. A higher peak value of inducer was assumed to predict a greater amplitude of LTF. b | Six samples of the 10,000 5-HT protocols that were simulated with the model. All protocols consist of five 5-minute pulses of 5-HT, shown as rectangular waves, with inter-pulse intervals chosen as multiples of 5 minutes, in the range of 5–50 minutes. The standard protocol (green trace) is the protocol most commonly used in studies of LTF in vitro. The enhanced protocol (red trace) produced the largest peak value of inducer, whereas the massed protocol (blue trace) produced the smallest peak value of inducer. The standard protocol has uniform inter-pulse intervals of 20 minutes, whereas the enhanced protocol has non-uniform intervals of 10, 10, 5 and 30 min. The massed protocol has no gaps between the 5-HT pulses. c | Simulated time courses of activated PKA, activated ERK and inducer in response to the standard protocol (green traces), the enhanced protocol (red traces) and the massed protocol (blue traces). d | In an empirical validation of the model’s prediction, the LTF induced by the enhanced protocol, as determined by the percentage increase in the amplitude of excitatory postsynaptic potentials (EPSPs), was greater than the LTF produced by the standard protocol. Figure parts c and d are from REF. , Nature Publishing Group.

Figure 5. A model predicts that a pair of drugs can act synergistically to enhance LTP

A CREB-binding protein (Cbp) mutation impairs hippocampal long-term potentiation (LTP) and impairs learning in mice, and Cbp^+/− mice are considered to be a model for aspects of Rubinstein–Taybi syndrome in humans. We developed a model to examine whether drugs could be used to overcome this impairment in LTP. This figure was generated from a series of simulations of the effects of two drugs on the induction of LTP. LTP was modelled as the percentage increase in a synaptic weight variable. In the absence of drugs, simulated LTP induced by a high-frequency tetanic stimulus was strongly impaired. Only a 50% increase in synaptic weight for Cbp^+/− occurred, compared with an increase in synaptic weight of 148% with non-mutated Cbp. The effect of each drug was simply modelled as a change in the value of a kinetic parameter. In this series of simulations, the doses of two drugs — drug 1, a cyclic AMP phosphodiesterase inhibitor, and drug 2, an acetyltransferase activator — were concurrently varied. The effect of drug 1 was simulated by decreasing a rate constant for cAMP degradation, and the effect of drug 2 was simulated by increasing a rate constant for histone acetylation. The ‘dose’ of drug 1 — the amplitude of the rate constant change — was increased, and simultaneously the dose of drug 2 was decreased. Eighty pairs of drug doses were simulated. Both drugs substantially enhanced LTP. For drug 2 alone (left end point of the graph), LTP was 155%, and for drug 1 alone (right end point) LTP was 116%. For both drugs together, with smaller doses of each drug, intermediate LTP amplitudes were observed (combined-effect curve). This series of simulations further shows that additive synergism persists over a substantial range of drug doses. Additive synergism is quantified as the difference (black double arrow) between the LTP simulated when both drugs are applied together (combined effect curve), and the LTP simulated by adding together the effect of the drugs applied individually in separate simulations (summed effects curve).