Past events

Circadian rhythms in locomotor activity are an example of a well-characterized behavior for which the molecular and neurobiological bases are not yet completely understood. These rhythms are self-sustained 24 hours rhythms that underlie most physiological and behavioral processes. The central circadian clock, which is situated in the brain, is responsible for daily rhythms in locomotor activity that persist even after weeks in constant darkness (DD). Peripheral clocks are spread trough the fly body and regulate a plethora of physiological functions that include: olfaction, detoxification and immunity. All these clocks keep time trough complex transcriptional-translational feedback loops that include the proteins CLK, CYC, PER and TIM. My research focuses on the study of the molecular basis of the circadian clock. In particular, I am interested in the contribution of the different molecular interactions and processes to the generation of robust 24hs rhythms. In this context, I have recently demonstrated that transcriptional speed of the clock gene PER is a determinant of the circadian period and that translational regulation by miRNAs is part of the central circadian clock.

Never before have such intense experimental efforts been focused on neuronal circuits of the size of few hundred thousands neurons whose functions are relatively well defined. The extraordinarily powerful new genetic tools and 3D reconstruction methods, combined with modern multi-electrode arrays, telemetry, two-photon imaging and photo-activation are starting to shed bright light on the intricacies of these circuits, and in particular of the cortical column. But without tools that integrate all this different types of data, one cannot expect to gain a comprehensive understanding on how these circuits perform specific sensory-motor or cognitive functions. As in any other complex system, a modeling study is essential if we are to ever say that we understand how this system works. I will describe several attempts in my group to begin building detailed models of the cortical column, highlighting that, at both circuit level and at the level of individual neurons, models should capture experimental variability and that the building of these models should become automated. I will demonstrate how these models could be used to fruitfully guide new experiments and discuss were all this new integrated "simulation-driven brain research agenda" might lead to.

The view that the brain computes is a working hypothesis in cognitive and brain sciences. But what does it mean to say that a system computes? What distinguishes computing systems, such as brains, from non-computing systems, such as stomachs and tornadoes? I argue that a "structural" approach to computing cannot account for much of the computational work in cognitive neuroscience. Instead, I offer a modeling account, which is a variant of a "semantic" approach. On this modeling account, the key feature of computing is a similarity between the "inner" mathematical relations, defined over the representing states, and "outer" mathematical relations, defined over the represented states.

Learning to read involves exposure to large amounts of print in a focused period of time during childhood. How does this environmental transition affect cortical circuits for visual perception and shape recognition? I will present data from a developmental study of reading examining the relation between reading skill, cortical function and white matter properties in school age children. Functional properties in area MT+, and white matter properties in temporal callosal fibers, are both correlated with reading skill. I will discuss possible interpretations of these findings within a general model of the reading pathways.

Reelin, a glycoprotein of the extracellular matrix, is secreted by Cajal-Retzius cells in the marginal zone of the cortex and controls the radial migration of cortical neurons. Reelin signaling involves the lipoprotein receptors apolipoprotein E receptor 2 (ApoER2) and very low density lipoprotein receptor (VLDLR), the adapter protein Disabled1 (Dab1), and phosphatidylinositol-3-kinase (PI3K). In regulating neuronal migration, Reelin signaling eventually acts on the cytoskeleton; however, its effects on the dynamic reorganization of the cytoskeleton have remained obscure. In reeler mutants deficient in Reelin, the majority of cortical neurons are unable to migrate to their destinations, suggesting Reelin signaling to be essential for the dynamic cytoskeletal reorganization that is required for neurons to migrate. In contrast, we show that Reelin signaling stabilizes the cytoskeleton by serine3 phosphorylation of n-cofilin, an actin-depolymerizing protein. Phosphorylation at serine3 renders n-cofilin unable to depolymerize F-actin. However, depolymerization of F-actin is required for cytoskeletal reorganization. The Reelin receptor ApoER2, Dab1, src family kinases (SFKs), and PI3K were found to be involved in n-cofilin serine3 phosphorylation. Phosphorylation of n-cofilin was observed in the leading processes of migrating neurons when they reached the Reelin-containing marginal zone. Using a stripe choice assay, we found neuronal processes to be stable on Reelin-coated stripes. In contrast, on control stripes they formed lamellipodia as a sign of ongoing growth. These new results indicate that Reelin-induced stabilization of neuronal processes anchors them to the marginal zone which is crucial for directional migration by nuclear translocation. (Supported by the German Research Foundation, DFG: SFB 592)

Abstracting from physiological details, I will present a theory that suggests an explanation behind critical periods in learning as a natural consequence of learning dynamics under a small and realistic set of assumptions. Surprisingly, the same theory offers an explanation for other animal learning phenomena, such as the tendency to reverse to the status quo following a transient learning experience. Additionally, the theory suggests simple experiments that can be used to prove or refute it. If the time permits, as a commercial for future results, I will finish the talk with a brief overview of recent attempts at LANL for petascale simulations of the mammalian visual cortex.

Functional MRI (fMRI) studies have defined a series of visual processing regions in the human cortex, which are believed to enable visual recognition behaviors through a hierarchy of processing stages. At the higher stages in this hierarchy lie regions which preferentially respond to images of intact objects compared to other visual stimuli, a set of regions collectively termed object-selective cortex. Within object-selective cortex exist category-selective regions, which prefer particular categories of images over others (e.g., faces, body parts, houses or scenes). Initially these regions were considered non-retinotopic, but increasing evidence indicates substantial retinal position selectivity, and in some cases retinotopy, in these regions. What is the representation of the visual field in object-selective regions? Are separate object- and category-selective regions part of a single map or embedded within a set of distinct visual field maps? We scanned seven subjects on separate experiments to localize object/category-selective regions, and measure visual field maps (GE 3T scanner). For retinotopic experiments, subjects viewed moving bar stimuli containing different stimuli, including slowly drifting checkerboards and frontal face images. The bars extended out to around 14° eccentricity from the fovea, and had a width of ~2.6°. We employed a recently-developed method for estimating population receptive fields (pRFs) using fMRI (Dumoulin and Wandell, Neuroimage, 2008), which estimates pRF center and size for each cortical location. Face-containing bars produced substantially larger responses than checkerboards along the fusiform gyrus, improving our ability to measure visual field maps in these regions. Eccentricity maps revealed two foveal representations, which may correspond to visual field map clusters previously identified as VO and VT (Wandell et al., Neuro-opth. Jpn., 2006). These foveas are within or adjacent to fusiform face-selective regions, and separated by smoothly-varying extra-foveal maps which are less face-selective. For several subjects, pRF sizes systematically increased with eccentricity in face-selective regions. The distribution of pRF sizes were substantially larger than in earlier visual cortex, but comparable to recent measurements made in lateral occipital cortex. We find two spatially separate face-selective regions along the fusiform gyrus, with comparable visual field coverage, separated by a representation of intermediate eccentricities. This indicates these two regions are likely to fall within different visual field maps. Current work addresses possible effects of low-level visual features (e.g. spatial frequency) and stimulus visibility in driving the observed face-selective retinotopic responses. I will also present some preliminary data from retinotopic mapping with house-containing bars, and an examination of retinotopic organization in house- or scene-selective cortical regions.

Optogenetics, synthesizing microbial opsins and solid-state optics, has achieved the goal of millisecond-precision bidirectional control of defined cell types in freely behaving mammals, but has not yet been widely applied to neuroscience and neuropsychiatry experimental challenges. First, relevant to important basic science questions, we have now successfully developed methods to target and control several classes of modulatory neurons in behaving mammals and intact neural tissue, and we are probing and quantifying measures of altered circuit performance under optogenetic control of defined circuit elements to address longstanding questions about neural circuit dynamics. Second, relevant to neuropsychiatric disease questions, we have used this approach for depth targeting of hypothalamic cells (in this case, the hypocretin/orexin cells in the lateral hypothalamus), establishing for the first time a causal relationship between frequency-dependent activity of genetically defined neurons important in clinical neuropsychiatric disease and a complex orchestrated mammalian behavior. We also are now applying fast optical control and optical imaging to animal models of depression, Parkinson’s Disease, and altered social behavior relevant to autism. Insights into both normal circuit function and disease mechanisms are beginning to emerge from this multidisciplinary technological approach. Prof. Deisseroth is hosted by the students of the Department of Neurobiology, as a part of the departmental students-invited visiting scientist program.