Synaptic dendritic activity modulates the single synaptic event

Theta oscillations represent the "on-line" state of the hippocampus. The extracellular currents underlying theta waves are generated mainly by the entorhinal input, CA3 (Schaffer) collaterals, and voltage-dependent Ca(2+) currents in pyramidal cell dendrites. The rhythm is believed to be critical for temporal coding/decoding of active neuronal ensembles and the modification of synaptic weights. Nevertheless, numerous critical issues regarding both the generation of theta oscillations and their functional significance remain challenges for future research.

Changing the strength of connections between neurons is widely assumed to be the mechanism by which memory traces are encoded and stored in the central nervous system. In its most general form, the synaptic plasticity and memory hypothesis states that "activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation and is both necessary and sufficient for the information storage underlying the type of memory mediated by the brain area in which that plasticity is observed." We outline a set of criteria by which this hypothesis can be judged and describe a range of experimental strategies used to investigate it. We review both classical and newly discovered properties of synaptic plasticity and stress the importance of the neural architecture and synaptic learning rules of the network in which it is embedded. The greater part of the article focuses on types of memory mediated by the hippocampus, amygdala, and cortex. We conclude that a wealth of data supports the notion that synaptic plasticity is necessary for learning and memory, but that little data currently supports the notion of sufficiency.

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.