Extinction as new learning versus unlearning: considerations from a computer simulation of the cerebellum

Like many forms of Pavlovian conditioning, eyelid conditioning displays robust extinction. We used a computer simulation of the cerebellum as a tool to consider the widely accepted view that extinction involves new, inhibitory learning rather than unlearning of acquisition. Previously, this simulation suggested basic mechanistic features of extinction and savings in eyelid conditioning, with predictions born out by experiments. We review previous work showing that the simulation reproduces behavioral phenomena and lesion effects generally taken as evidence that extinction does not reverse acquisition, even though its plasticity is bidirectional with no site dedicated to inhibitory learning per se. In contrast, we show that even though the sites of plasticity are, in general, affected in opposite directions by acquisition and extinction training, most synapses do not return to their naive state after acquisition followed by extinction. These results suggest caution in interpreting a range of observations as necessarily supporting extinction as unlearning or extinction as new inhibitory learning. We argue that the question “is extinction reversal of acquisition or new inhibitory learning?” is therefore not well posed because the answer may depend on factors such as the brain system in question or the level of analysis considered.Pavlovian eyelid conditioning is robustly bidirectional. Conditioned responses that are acquired via training that pairs a conditioned stimulus (CS) with an unconditioned stimulus (US) can be rapidly extinguished with CS-alone training or unpaired CS-US training (Gormezano et al. 1983; Napier et al. 1992; Macrae and Kehoe 1999; Kehoe and Macrae 2002; Kehoe and White 2002; Weidemann and Kehoe 2003). Whether extinction involves unlearning or separate inhibitory learning that suppresses conditioned response expression remains an important issue for both behavioral theories and for investigations of underlying neural mechanisms (Pavlov 1927; Hull 1943; Konorski 1948, 1967; Rescorla and Wagner 1972; Mackintosh 1974; Rescorla 1979; Bouton 1993, 2002; Falls 1998; Myers and Davis 2002; Kehoe and White 2002). Here, we addressed this issue using a computer simulation of the cerebellum that is capable of emulating many aspects of eyelid conditioning. Although simulation results cannot resolve such issues, several aspects of the simulation''s behavior are instructive. Even though the sites of plasticity are, in general, affected in opposite directions by acquisition and extinction training, the simulation can emulate several behavioral phenomena that are generally taken as evidence that extinction does not involve unlearning. Moreover, we found that the strengths of most synapses are quite different from their naive state following acquisition and then extinction. Independent of the overall biological accuracy of this simulation, these results highlight a variety of implications for ongoing debates about the roles of unlearning versus new learning in extinction.A combination of factors makes it possible to analyze the neural basis of eyelid conditioning in detail, and to build and test computer simulations of its cerebellar mechanisms (Medina and Mauk 2000). Foremost among these is the close association between eyelid conditioning and the cerebellum (Thompson 1986; Raymond et al. 1996; Mauk and Donegan 1997). Previous studies from several labs have shown that (1) cerebellar output drives the motor pathways that produce the conditioned responses (McCormick and Thompson 1984), (2) presentation of a CS is conveyed to the cerebellum via activation of certain of its mossy fiber inputs (Steinmetz et al. 1986; Hesslow et al. 1999), and (3) presentation of the US is conveyed via activation of certain climbing fiber inputs to the cerebellum (Fig. 1A; Mauk et al. 1986). These factors are complemented by the extent to which the synaptic organization and physiology of the cerebellum are known (Eccles et al. 1967; Ito 1984), as are the behavioral properties of eyelid conditioning (Gormezano et al. 1983; Kehoe and Macrae 2002). These advantages combine with the speed of current computers to make possible the construction of biologically detailed and large-scale computer simulations of the cerebellum that can then be thoroughly tested using standard eyelid conditioning protocols (Medina et al. 2000, 2001, 2002; Medina and Mauk 1999, 2000).Open in a separate windowFigure 1Emulation of eyelid conditioning in a computer simulation of the cerebellum. (A) A schematic representation of the simulation and how it was trained using an eyelid conditioning-like protocol. The output of the simulation comes from the summed activity of the six cerebellar deep nucleus cells (blue box). The CS was conveyed to the simulation by phasic activation of 18 of the 600 mossy fibers and tonic activation of six mossy fibers (green box). The US was emulated by a brief excitatory conductance applied to the single climbing fiber. The remainder of the simulation consisted of 10,000 granule cells, 900 Golgi cells, 20 stellate/basket cells, and 20 Purkinje cells with essentials of the connectivity as shown. (B) Acquisition, extinction, and savings by the simulation. Each panel shows the equivalent of 10 d of acquisition (left panel), extinction (center), and reacquisition (right) training. Individual sweeps are averages of 10 trials, which are clustered together to approximate the equivalent of one daily session of eyelid conditioning. These sessions are numbered at the left, progressing from front to back. The blue portion of the sweeps denotes the presence of the CS. (C) The strength of the mossy fiber-to-nucleus synapses in the simulation over the three phases of training. The synapses that progressively increase in strength during acquisition and reacquisition and decrease during extinction are the six that are tonically activated by the CS. Note that extinction training only slowly and thus incompletely reverses the strengthened synapses. Savings during reacquisition in the simulation is largely attributable to this residual plasticity. The continued increase in the strength of these synapses does not produce a comparable increase in response amplitude, rather, it reflects the tendency for the network to transfer plasticity from cortex (pauses in Purkinje activity produced by LTD) to the nucleus (increased strength of mossy fiber-to-nucleus synapses). How long this process continues depends on a number of unknown factors.The present results are more easily appreciated with a brief review of previous studies (Medina et al. 2000, 2001, 2002) showing how the simulation emulates acquisition, extinction, and savings during reacquisition. These phenomena are shown for the simulation in the three panels of Figure 1B. The underlying essential elements can be summarized briefly. Presentation of a CS activates subsets of granule cells, and these subsets change somewhat over the duration of the CS. Paired training induces long-term depression (LTD) at CS-activated granule-to-Purkinje synapses that are activated when the US is presented. This leads to a learned and well timed decrease in the activity of Purkinje cells during the CS (Hesslow and Ivarsson 1994), which leads to the induction of long-term potentiation (LTP) at mossy fiber-to-nucleus synapses activated by the CS. As this plasticity develops, nucleus cells encounter during the CS strong excitation combined with release from inhibition and therefore discharge robustly, thereby driving the expression of conditioned responses (McCormick and Thompson 1984). These steps suggest that learning first occurs in the cerebellar cortex, before robust conditioned responses are seen. We have observed evidence for this latent learning in cerebellar cortex (Ohyama and Mauk 2001).During extinction, CS-activated granule-to-Purkinje synapses undergo LTP because their activation occurs in the absence of climbing fiber activity. The essential suppression of climbing fiber activity below the typical level of 1 Hz is produced by inhibition from cerebellar output (Sears and Steinmetz 1991; Hesslow and Ivarsson 1996; Kenyon et al. 1998a,b; Miall et al. 1998), which is robust during the expression of conditioned responses. This prediction of the simulation is supported by observations that blocking inhibition of climbing fibers prevents extinction (Medina et al. 2002).We have shown previously that savings during reacquisition results, at least in part, from plasticity in the cerebellar deep nucleus that is relatively resistant to extinction (Medina et al. 2001). The strengths of the CS-activated mossy fiber-to-nucleus synapses in the simulation are shown in Figure 1C for acquisition, extinction, and reacquisition. Because learned pauses in Purkinje cell activity are still present early in extinction training, the strengths of CS-activated mossy fiber-to-nucleus synapses continue to increase. Once conditioned responses are fully extinguished, due to the restoration of robust Purkinje cell activity during the CS via the induction of LTP at CS-activated granule-to-Purkinje synapses, then CS-activated mossy fiber-to-nucleus synapses begin to undergo LTD and decrease in strength. The rate at which these synapses decrease in strength with additional extinction training depends on unknown factors such as the level of Purkinje activity required for induction of LTD. These results show in principle, however, that plasticity in the cerebellar cortex is sufficient to extinguish conditioned responses, and that a network displaying fully extinguished conditioned responses can still contain strengthened mossy fiber-to-nucleus synapses. In the simulation, savings occur largely because this residual plasticity in the cerebellar nucleus enhances the conditioned responses produced by the relearning of decreased activity in the Purkinje cells. In support, we have shown in rabbits that plasticity in the cerebellar nucleus persists following extinction, and that a measure of the magnitude of this residual plasticity correlates with the rate of reacquisition (Medina et al. 2001).Here, we used the mechanisms of extinction in this simulation to stimulate discussion regarding the issue of extinction as unlearning versus extinction as new learning.