presented at a colloquium entitled ‘ ‘ Memory : Recording Experience in Cells and Circuits , ’ ’ organized by

Almost all theoretical and experimental studies of the mechanisms underlying learning and memory focus on synaptic efficacy and make the implicit assumption that changes in synaptic efficacy are both necessary and sufficient to account for learning and memory. However, network dynamics depends on the complex interaction between intrinsic membrane properties and synaptic strengths and time courses. Furthermore, neuronal activity itself modifies not only synaptic efficacy but also the intrinsic membrane properties of neurons. This paper presents examples demonstrating that neurons with complex temporal dynamics can provide short-term ‘‘memory’’ mechanisms that rely solely on intrinsic neuronal properties. Additionally, we discuss the potential role that activity may play in long-term modification of intrinsic neuronal properties. While not replacing synaptic plasticity as a powerful learning mechanism, these examples suggest that memory in networks results from an ongoing interplay between changes in synaptic efficacy and intrinsic membrane properties. The behavior of rhythmic networks depends on the complex interaction between the dynamics of the individual neurons (their intrinsic properties) and the strengths and time courses of the synapses among them (1–3). Years of research on networks as diverse as central pattern generators in invertebrates and vertebrates (3), the thalamus (4–6), and the cerebellum (7) have clearly shown that complex neuronal characteristics, such as oscillatory and plateau properties, play crucial roles in shaping neural network output. Recent work has expanded the brain regions that show neuronal oscillations to the cortex (8), suggesting that complex intrinsic properties are likely to shape the computational dynamics of many, if not all, brain regions. It is commonly assumed that short and long-lasting changes in synaptic efficacy are both necessary and sufficient to account for the stable changes in network dynamics that we call ‘‘memory’’. The remarkable success of early neural network models, in which only synaptic strengths were modified but memories were stored, showed that changes in network output could result solely from changes in synaptic strength (9). An attractive feature of synaptic modification is that it can be restricted to a subset of the synaptic connections made by a neuron, or made onto a neuron. And last, but not least, the experimental work on the Aplysia gill withdrawal reflex and hippocampal slices, in which long-lasting changes in synaptic strength could be produced, and the mechanisms underlying those changes studied (10, 11) provided strong impetus to look primarily at synaptic plasticity as the mechanism underlying memory in intact animals. However, in this paper, we show that intrinsic neuronal currents can also play a role in memory phenomena in at least two different ways. First, a variety of ‘‘short-term’’ memory mechanisms can result from slowly activating and inactivating membrane conductances that make the response of a cell depend on its recent history of activity. Second, activity can modify, on a longer time scale, the intrinsic properties of neurons, resulting in long-lasting experience-dependent changes. These ‘‘intrinsic memory phenomena’’ should be viewed as powerful additions to the compendium of mechanisms that can be called into play to allow experience to modify neural circuits. Neurons Have a Variety of Intrinsic Membrane Properties Neurons can display a variety of activity patterns that depend on the number and type of voltage channels in their membranes (Fig. 1). Some neurons are silent unless excited; others are spontaneously active. Some neurons display intrinsic oscillatory properties involving periodic bursts of action potentials. Modifications in the number of each channel type present in the membrane can change a variety of neuronal properties, including firing frequency and threshold, rate of spike repolarization, degree of postinhibitory rebound, burst amplitude, and burst period. Moreover, changes in channel density can move neurons from one kind of activity pattern (such as tonic firing) to another (such as bursting).