Synaptic bouton sizes are tuned to best fit their physiological performances : poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011, Stockholm, Sweden, 23 - 28 July 2011

Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. To truly appreciate the myriad of events which relate synaptic function and vesicle dynamics, simulations should be done in a spatially realistic environment. This holds true in particular in order to explain the rather astonishing motor patterns presented here which we observed within in vivo recordings which underlie peristaltic contractions at a well characterized synapse, the neuromuscular junction (NMJ) of the Drosophila larva. To this end, we have employed a reductionist approach and generated three dimensional models of single presynaptic boutons at the Drosophila larval NMJ. Vesicle dynamics are described by diffusion-like partial differential equations which are solved numerically on unstructured grids using the uG platform. In our model we varied parameters such as bouton-size, vesicle output probability (Po), stimulation frequency and number of synapses, to observe how altering these parameters effected bouton function. Hence we demonstrate that the morphologic and physiologic specialization maybe a convergent evolutionary adaptation to regulate the trade off between sustained, low output, and short term, high output, synaptic signals. There seems to be a biologically meaningful explanation for the co-existence of the two different bouton types as previously observed at the NMJ (characterized especially by the relation between size and Po),the assigning of two different tasks with respect to short- and long-time behaviour could allow for an optimized interplay of different synapse types. As a side product, we demonstrate how advanced methods from numerical mathematics could help in future to resolve also other difficult experimental neurobiological issues

Synaptic bouton sizes are tuned to best fit their physiological performances

Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. To truly appreciate the myriad of events which relate synaptic function and vesicle dynamics, simulations should be done in a spatially realistic environment. This holds true in particular in order to explain the rather astonishing motor patterns presented here which we observed within in vivo recordings which underlie peristaltic contractions at a well characterized synapse, the neuromuscular junction (NMJ) of the Drosophila larva. To this end, we have employed a reductionist approach and generated three dimensional models of single presynaptic boutons at the Drosophila larval NMJ. Vesicle dynamics are described by diffusion-like partial differential equations which are solved numerically on unstructured grids using the uG platform. In our model we varied parameters such as bouton-size, vesicle output probability (Po), stimulation frequency and number of synapses, to observe how altering these parameters effected bouton function. Hence we demonstrate that the morphologic and physiologic specialization maybe a convergent evolutionary adaptation to regulate the trade off between sustained, low output, and short term, high output, synaptic signals. There seems to be a biologically meaningful explanation for the co-existence of the two different bouton types as previously observed at the NMJ (characterized especially by the relation between size and Po),the assigning of two different tasks with respect to short- and long-time behaviour could allow for an optimized interplay of different synapse types. As a side product, we demonstrate how advanced methods from numerical mathematics could help in future to resolve also other difficult experimental neurobiological issues

Reduced Body Weight and Increased Energy Expenditure in Transgenic Mice Over-Expressing Soluble Leptin Receptor

studies have shown that OBRe expression is inversely correlated to body weight and leptin levels. However, it is not clear whether OBRe plays an active role, either in collaboration with leptin or independently, in the maintenance of body weight.To investigate the function of OBRe in the regulation of energy homeostasis, we generated transgenic mice that express OBRe under the control of human serum amyloid P (hSAP) component gene promoter. The transgene led to approximately doubling of OBRe in circulation in the transgenic mice than in wild type control mice. Transgenic mice exhibited lower body weight at 4 weeks of age, and slower rate of weight gain when compared with control mice. Furthermore, transgenic mice had lower body fat content. Indirect calorimetry revealed that transgenic mice had reduced food intake, increased basal metabolic rate, and increased lipid oxidation, which could account for the differences in body weight and body fat content. Transgenic mice also showed higher total circulating leptin, with the majority of it being in the bound form, while the amount of free leptin is comparable between transgenic and control mice.These results are consistent with the role of OBRe as a leptin binding protein in regulating leptin's bioavailability and activity

Obstructive Sleep Apnea and Cardiovascular Events After Percutaneous Coronary Intervention

10.1161/CIRCULATIONAHA.115.019392CIRCULATION133212008-201

A collective AI via lifelong learning and sharing at the edge

One vision of a future artificial intelligence (AI) is where many separate units can learn independently over a lifetime and share their knowledge with each other. The synergy between lifelong learning and sharing has the potential to create a society of AI systems, as each individual unit can contribute to and benefit from the collective knowledge. Essential to this vision are the abilities to learn multiple skills incrementally during a lifetime, to exchange knowledge among units via a common language, to use both local data and communication to learn, and to rely on edge devices to host the necessary decentralized computation and data. The result is a network of agents that can quickly respond to and learn new tasks, that collectively hold more knowledge than a single agent and that can extend current knowledge in more diverse ways than a single agent. Open research questions include when and what knowledge should be shared to maximize both the rate of learning and the long-term learning performance. Here we review recent machine learning advances converging towards creating a collective machine-learned intelligence. We propose that the convergence of such scientific and technological advances will lead to the emergence of new types of scalable, resilient and sustainable AI systems.</p