Dr. Stephen Smith: Shotgun connectomic analysis of cortical synaptic networks
Senior Investigator, Allen Institute for Brain Science
Bio: Stephen J Smith is a Senior Investigator at the Allen Institute for Brain Science. Previously, he was Professor of Molecular and Cellular Physiology at the Stanford University School of Medicine. Smith received his Ph.D. from the University of Washington in 1977, after which he conducted postdoctoral research at the University of California, Berkeley from 1977 to 1980. He accepted a faculty position at Yale University in 1980 before relocating to Stanford in 1989. In 1990, Smith published an article in the journal Science, which proposed that certain glial cells in the human brain known as astrocytes would be able to communicate through chemical signals, rather than electrical. By utilizing the neurotransmitter glutamate, he was able to show that these cells, which had once been perceived as inert tissue between neurons, in fact were actively communicating with the rest of the brain. This discovery showed that astrocytes likely play an important role in learning and memory. Smith has also been a primary leader in the development of array tomography (AT), a proteomic imaging method of volumetric microscopy, in which ultrathin sections of a plastic-embedded tissue are sliced using an ultramicrotome, bonded in an ordered array to a glass coverslip, stained, and imaged. In utilizing array tomography, Smith and his colleagues created three-dimensional models of brains in mice, achieving a level of detail previously unattainable. Read More.
Abstract: Mechanistic understanding of cortical circuit function is certain to require quantitative information about synaptic connectivity amongst very numerous distinct cortical cell types. Electron and fluorescence microscopy offer complementary opportunities to sample cortical network function, connectivity and synapse molecular architectures. "Shotgun" fluorescence labeling of sparse, stochastic subsets of neurons can be achieved by transgenic, viral or gene-gun infection and offers special advantages of easy image acquisition and analysis, with potentially unbiased sampling of multiple cell types within individual networks. With sufficient repetition of shotgun sampling experiments and reliable means of post hoc cell type identification, it may be possible to define cortical network "wiring" statistics and synaptic properties in ways that circumvent limitations of dense electron microscopic and cell-type-directed fluorescence sampling methods. My talk will address the prospects for a "shotgun connectomics" based on sparse fluorescence labeling and combinations of in vitro or in vivo physiology and fluorescence and electron modes of array tomography.
Dr. Ivan Soltesz: Mechanisms of network oscillations in data-driven full-scale and rationally derived simple models of the hippocampus
James R. Doty Professor of Neurosurgery and Neurosciences, Stanford School of Medicine
Bio: Ivan Soltesz studies epilepsy in mice, but says children with chronic seizures are his inspiration. He's closing in on a way to quell the seizures with light – and without drugs' side effects. He is a Professor of Neurosurgery and Neurology & Neurological Sciences and a member of the Stanford Neurosciences Institute. Read More.
Abstract: Information processing in the brain is organized and facilitated by the complex interactions of intrinsic biophysical properties of distinct neuronal types, neuronal morphology, and network connectivity. These properties give rise to specific types of behaviorally relevant network oscillations and other dynamic processes that govern neural information encoding and exchange. We constructed a strictly data-driven, supercomputer-based, full-scale (1:1) computational model of the CA1 region of the hippocampus in order to increase our understanding of how the intrinsic properties and synaptic connectivity of hippocampal principal neurons and interneurons give rise to rhythmic network activity. Simulations of the full-scale CA1 model revealed that theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged even without rhythmic inputs. Furthermore, perturbations of the network connectivity pointed to sharply different roles for interneuronal types, and highlighted interneuronal diversity and GABAB receptors as key factors in theta rhythm generation. We also developed a novel modeling method called Network Clamp that allows the rational derivation of simpler models that can be run on personal computers from complex full-scale models. Supported by grants from the BRAIN Initiative, NINDS, NSF and the Stanford Neuroscience Program.