What is the appropriate description level for synaptic plasticity?

T he hypothesis that learning memory and some aspects of development are mechanistically implemented by synaptic plasticity has gained significant experimental support (1, 2). At the cellular and molecular level synaptic plasticity is a very complex phenomenon, involving hundreds of molecular species, depending on the structure of dendrites and on ion channel concentrations. If we are to understand how high-level processes arise from synaptic plasticity, and not simply that they arise from synaptic plasticity, we must know how to best characterize plasticity theoretically. Such a characterization should account for key experimental results, yet at the same time it should be as simple as possible so that we can use it to explain how plasticity can lead to learning and memory. The article by Gjorgjieva et al. (3) in PNAS argues that a sufficient model of synaptic plasticity can depend only on spike pairs and triplets and that more-complex biophysical and molecular processes might not be needed. It also shows a correspondence between this triplet-based rule and the well-known phenomenological Bienenstock Copper Munro (BCM) learning rule (4). Many of the early theories of synaptic plasticity were not formulated on the basis of low-level experimental evidence. Instead, they were motivated by the consequences of plasticity observed at a higher level, for example receptive field plasticity in visual cortex. To account for such high-level plasticity, theorists postulated phenomenological low-level mechanisms that can account for such higher-level phenomena. In the mid-1970s von der Malsburg (5) proposed rate-based network models that included synaptic plasticity and competition between cells to account for the formation of orientation selectivity and ocular dominance maps in visual cortex. The plasticity model he used was very simple: synaptic potentiation that is proportional to the product of presynaptic and postsynaptic activity variables, coupled with a normalization of the total synaptic weight. Later work has shown the limitations of this plasticity model (6). The BCM model (4) was also formulated to explain receptive field plasticity in visual cortex, and like other ratebased phenomenological models, is formulated in terms of abstract preand postsynaptic activity variables. The BCM theory (Fig. 1A) is based on two principles, formulated by two equations. First, the plasticity equation: