Past events

Fetal mesencephalic tissue has been used as a source of dopaminergic neurons for transplantation in clinical trials with Parkinson’s disease patients and in animal models of Parkinson’s disease. Due to the poor availability of human fetal tissue, and the ethical concerns associated with the use tissue from aborted fetuses, further development of the cell replacement therapy will critically depend on the access to alternative sources of cells for transplantation, based on the use of stem cells as a source of dopaminergic neurons. The recent discovery of Lmx1a and Msx1 as key determinant genes of mesencephalic dopaminergic neuron fate during development (Andersson et al. 2006) has opened new possibilities to drive undifferentiated stem cells towards fully functional mesencephalic dopaminergic neurons. Overexpression of these genes in stable embryonic stem (ES) cell lines is sufficient to generate neurons with almost 100% efficiency into a fully differentiated mesencephalic dopaminergic phenotype. The in vivo data obtained so far indicate that mesencephalic dopaminergic neurons can be generated in large numbers using this approach, and that they survive very well after transplantation to the striatum of 6-hydroxydopamine lesioned rats. In vivo, the Lmx1a- and Msx1-expressing cells develop into fully mature mesencephalic dopaminergic neurons, of both the A9 and A10 subtypes, and grow efficiently to form an extensive TH-positive axonal terminal network throughout the entire host striatum. Using this approach transplantable neurons with what appear to be a complete mesencephalic dopaminergic phenotype can be generated in large numbers from ES cell cultures.

The neural crest (NC) is a transient group of progenitors present in vertebrate embryos. Its component cells yield an extensive variety of derivatives such as melanocytes, neurons of many kinds, glial , ectomesenchymal and endocrine cells. Initially, presumptive NC cells are an integral part of the neuroepithelium. Subsequently, a time and axial level-specific conversion from an epithelial to a mesenchymal (EMT) state causes the cells to become motile and engage in migration. Mesenchymal NC cells then advance through stereotyped pathways, reach their homing sites and then differentiate. The molecular network underlying NC delamination and the generation of cell movement remained incompletely understood. We found that a balance between BMP and its inhibitor noggin underlies the emigration of NC independently of earlier cell specification. BMP induces delamination by triggering Wnt1 transcription. Canonical Wnt signaling promotes G1/S transition which is a necessary step for delamination of trunk NC. Successful delamination also requires the activity of effector genes that act on re-organisation of the actin cytoskeleton and alterations in adhesive properties. In this context, we found that both N-cadherin and RhoGTPase signaling play a negative modulatory role on the process. During the course of our work, we observed that in the trunk, NC cells continuously delaminate from the NT for over two days, raising the fundamental question of the source and mechanisms accounting for the production of successive waves of NC progenitors. We found that the first NC to delaminate reside in the dorsal midline of the NT and generate sympathetic ganglia, and successive waves translocate ventrodorsally in the NT to replenish the dorsal midline and then delaminate. Hence, the dorsal midline is a dynamic region traversed sequentially by progenitors that colonize NC derivatives in a ventral to dorsal order (chromaffin cells, sympathetic ganglia, then Schwann cells, DRG and finally melanocytes). Based on our data invoking a dynamic behavior of premigratory NC cells, we hypothesize the existence of a spatiotemporal fate map of derivatives present already within the NT and defined by a specific molecular code.

Entropy and information are quantities of interest to neuroscientists, because of their mathematical properties and because they place limits on the performance of a neural system. However, estimating these quantities from neural spike trains is much more challenging than estimating other statistics, such as mean and variance. The central difficulty in estimating information is tightly linked to the properties of information that make it a desirable quantity to estimate. To surmount this fundamental difficulty, most approaches to estimation of information rely (perhaps implicitly) on a model for how spike trains are related. But the nature of these model assumptions vary widely. As a result, information estimates are useful not only in situations in which several approaches provide mutually consistent results, but also in situations in which they differ. These ideas are illustrated with examples from the visual and gustatory systems.

The octopus is an active hunter, a remarkable invertebrate whose complex behaviors rely on exceptionally good visual and tactile senses coupled with highly advanced learning and memory (LM) abilities. Studying the octopus LM system may therefore reveal characteristics universally important for mediation of complex behaviors. We developed slice and isolated brain preparations of the LM area in the octopus brain to characterize the short- and long-term neural plasticity. The importance of these processes for LM are been tested in behavioral experiments. The results support the importance of LTP in behavioral LM and suggest new ideas regarding the organization of short- and long-term memory systems.

I am interested in how neural dynamics, changes in neural sensitivity on the time scale of milliseconds to seconds, support rapid changes in perceptual capability. Our key focus is in testing the hypothesis that dynamics in early sensory cortices, such as the primary somatosensory cortex, play a key role in perception. To examine these issues in a model where detailed invasive studies are possible, we study the vibrissa sensory system of rodents. To examine the broader relevance of these findings, we have a smaller parallel effort I human perception and imaging. A variety of our studies suggest that. In this seminar, I will describe work we have done to understand the 'natural scene' of vibrissa perception, the signals that are generated during active surface contact. I will then describe how these signals are encoded in 2 recently discovered cortical maps, a frequency map, shaped by the resonance properties of the vibrissae, and a direction map, encoding the angle of vibrissa motion. I will then discuss why having these cortical feature columns, with adjacent neurons sharing similar computational processing, may enhance information processing. Specifically, I will argue that this arrangement facilitates dynamic regulation of neural sensitivity by neuromodulators that have a more course spatial precision, on the order of 300-1000 microns. I will briefly mention the hemo-neural hypothesis, the proposal that changes in blood flow and volume may be one source of neural regulation on this spatial scale. I will then describe an example of the dynamic regulation of spatial activation across a cortical map, resulting from adaptation during robust thalamocortical input at 5-25 Hz. I will argue that this adaptation leads to a transition into a state that is enhanced for discrimination of alternative stimuli, but impoverished for detection of novel stimuli (decreased sensitivity). To test the hypothesis that these kinds of cortical dynamics in the primary somatosensory cortex regulate perception, I will describe human MEG experiments showing that changes in the amplitude of SI activation regulate detection probability, and showing that these changes in perception and cortical amplitude are predicted by ongoing rhythmic activity in human SI at 5-25 Hz (the 'mu' rhythm).

Large, strongly coupled neural networks tend to produce chaotic spontaneous activity. This might appear to make them unsuitable for generating reliable sensory responses or repeatable motor patterns. However, this is not the case. Inputs can induce a phase transition, leading to responses uncontaminated by chaotic "noise". Likewise, appropriately trained feedback units can control the chaos, resulting in a wide variety of repeatable output patterns. These issues will be discussed accompanied by examples and demonstrations.