During much of sleep, virtually all cortical neurons undergo a slow oscillation (<1 Hz) in membrane potential, cycling from a hyperpolarized state of silence to a depolarized state of intense firing. This slow oscillation is the fundamental cellular phenomenon that organizes other sleep rhythms such as spindles and slow waves. Using high-density electroencephalogram recordings in humans, we show here that each cycle of the slow oscillation is a traveling wave. Each wave originates at a definite site and travels over the scalp at an estimated speed of 1.2-7.0 m/sec. Waves originate more frequently in prefrontal-orbitofrontal regions and propagate in an anteroposterior direction. Their rate of occurrence increases progressively reaching almost once per second as sleep deepens. The pattern of origin and propagation of sleep slow oscillations is reproducible across nights and subjects and provides a blueprint of cortical excitability and connectivity. The orderly propagation of correlated activity along connected pathways may play a role in spike timing-dependent synaptic plasticity during sleep.

The thick-tufted layer 5b pyramidal cell extends its dendritic tree to all six layers of the mammalian neocortex and serves as a major building block for the cortical column. L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. Furthermore, we show a useful way to analyze model parameters with our sets of models, which enabled us to identify some of the mechanisms responsible for the dynamic properties of L5b pyramidal cells as well as mechanisms that are sensitive to morphological changes. This automated framework can be used to develop a database of faithful models for other neuron types. The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities.

Most of us sleep 7-8 h per night, and if we are deprived of sleep our performance suffers greatly; however, a few do well with just 3-4 h of sleep-a trait that seems to run in families. Determining which genes underlie this phenotype could shed light on the mechanisms and functions of sleep. To do so, we performed mutagenesis in Drosophila melanogaster, because flies also sleep for many hours and, when sleep deprived, show sleep rebound and performance impairments. By screening 9,000 mutant lines, we found minisleep (mns), a line that sleeps for one-third of the wild-type amount. We show that mns flies perform normally in a number of tasks, have preserved sleep homeostasis, but are not impaired by sleep deprivation. We then show that mns flies carry a point mutation in a conserved domain of the Shaker gene. Moreover, after crossing out genetic modifiers accumulated over many generations, other Shaker alleles also become short sleepers and fail to complement the mns phenotype. Finally, we show that short-sleeping Shaker flies have a reduced lifespan. Shaker, which encodes a voltage-dependent potassium channel controlling membrane repolarization and transmitter release, may thus regulate sleep need or efficiency.

Hill, Sean and Giulio Tononi. Modeling sleep and wakefulness in the thalamocortical system. J Neurophysiol 93: 2005. First published November 10, 2004; doi:10.1152/jn.00915.2004. When the brain goes from wakefulness to sleep, cortical neurons begin to undergo slow oscillations in their membrane potential that are synchronized by thalamocortical circuits and reflected in EEG slow waves. To provide a self-consistent account of the transition from wakefulness to sleep and of the generation of sleep slow waves, we have constructed a large-scale computer model that encompasses portions of two visual areas and associated thalamic and reticular thalamic nuclei. Thousands of model neurons, incorporating several intrinsic currents, are interconnected with millions of thalamocortical, corticothalamic, and both intra-and interareal corticocortical connections. In the waking mode, the model exhibits irregular spontaneous firing and selective responses to visual stimuli. In the sleep mode, neuromodulatory changes lead to slow oscillations that closely resemble those observed in vivo and in vitro. A systematic exploration of the effects of intrinsic currents and network parameters on the initiation, maintenance, and termination of slow oscillations shows the following. 1) An increase in potassium leak conductances is sufficient to trigger the transition from wakefulness to sleep.2) The activation of persistent sodium currents is sufficient to initiate the up-state of the slow oscillation. 3) A combination of intrinsic and synaptic currents is sufficient to maintain the up-state. 4) Depolarization-activated potassium currents and synaptic depression terminate the up-state. 5) Corticocortical connections synchronize the slow oscillation. The model is the first to integrate intrinsic neuronal properties with detailed thalamocortical anatomy and reproduce neural activity patterns in both wakefulness and sleep, thereby providing a powerful tool to investigate the role of sleep in information transmission and plasticity.

It is well-established that synapse formation involves highly selective chemospecific mechanisms, but how neuron arbors are positioned before synapse formation remains unclear. Using 3D reconstructions of 298 neocortical cells of different types (including nest basket, small basket, large basket, bitufted, pyramidal, and Martinotti cells), we constructed a structural model of a cortical microcircuit, in which cells of different types were independently and randomly placed. We compared the positions of physical appositions resulting from the incidental overlap of axonal and dendritic arbors in the model (statistical structural connectivity) with the positions of putative functional synapses (functional synaptic connectivity) in 90 synaptic connections reconstructed from cortical slice preparations. Overall, we found that statistical connectivity predicted an average of 74 ± 2.7% (mean ± SEM) synapse location distributions for nine types of cortical connections. This finding suggests that chemospecific attractive and repulsive mechanisms generally do not result in pairwise-specific connectivity. In some cases, however, the predicted distributions do not match precisely, indicating that chemospecific steering and aligning of the arbors may occur for some types of connections. This finding suggests that random alignment of axonal and dendritic arbors provides a sufficient foundation for specific functional connectivity to emerge in local neural microcircuits.connectome | microcircuitry | neocortex | neuronal connectivity D uring development, a broad range of molecular signaling mechanisms act to form the neocortical layers (1, 2), lay down guidance tracts for neurons to position themselves and grow their axonal and dendritic arbors (3, 4), shape the morphologies of different types of neurons (5), guide input axons to specific layers (6), trigger differentiation and clustered arborizations of axonal arbors (7), prune axonal arbors in an activitydependent manner (8), guide axonal growth along the axon initial segment (9), and selectively form synapses between specific types of neurons (10-12). Molecular mechanisms also contribute to the activity-triggered formation of synapses between specific pairs of neurons (13). The mechanisms determining this selective connectivity have been a subject of active debate for over a century (3,(14)(15)(16)(17), with proposals ranging from the idea of "chemical relations" between connected neurons in the work by Langley (18) to the "chemoaffinity hypothesis" (the idea that precise synaptic positioning is specified by "highly specific cytochemical affinities" between neurons) in the work by Sperry (19). The result is an intriguing map of synaptic connections between all neurons and specific types of neurons, commonly called the connectome. How the connectome arises is still debated (20). A question that is at the core of the debate is to what extent do chemospecific mechanisms specifically align neuronal arbors between neurons before synaptic connections are formed.Spines are dynamic s...