Synapses are individually operated, computational units for neural communication. To manipulate physically individual synapses in a living organism, we have developed a laser ablation technique for removing single synapses in live neurons in C. elegans that operates without apparent damage to the axon. As a complementary technique, we applied microfluidic immobilization of C. elegans to facilitate long-term fluorescence imaging and observation of neuronal development. With this technique, we directly demonstrated the existence of competition between developing synapses in the HSNL motor neuron.

The paper describes a target tracking system running on a Heterogeneous Sensor Network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

Genetically encoded calcium indicators (GECIs), based on recombinant fluorescent proteins, have been engineered to observe calcium transients in living cells and organisms. Through observation of calcium, these indicators also report neural activity. We review progress in GECI construction and application, particularly toward in vivo monitoring of sparse action potentials (APs). We summarize the extrinsic and intrinsic factors that influence GECI performance. A simple model of GECI response to AP firing demonstrates the relative significance of these factors. We recommend a standardized protocol for evaluating GECIs in a physiologically relevant context. A potential method of simultaneous optical control and recording of neuronal circuits is presented.

We present a compressed domain scheme that is able to recognize and localize actions at high speeds. The recognition problem is posed as performing an action video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion directions and magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a benchmark action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our proposed method achieved a classification accuracy of 90%, comparing favorably with existing methods in action classification accuracy, and is able to localize a template video of 80 x 64 pixels with 23 frames in a test video of 368 x 184 pixels with 835 frames in just 11 seconds, easily outperforming other methods in localization speed. We also perform a systematic investigation of the effects of various encoding options on our proposed approach. In particular, we present results on the compression-classification trade-off, which would provide valuable insight into jointly designing a system that performs video encoding at the camera front-end and action classification at the processing backend.

The obesogenic effect of a high-fat (HF) diet is counterbalanced by stimulation of energy expenditure and lipid oxidation in response to a meal. The aim of this study was to reveal whether muscle nonshivering thermogenesis could be stimulated by a HF diet, especially in obesity-resistant A/J compared with obesity-prone C57BL/6J (B/6J) mice. Experiments were performed on male mice born and maintained at 30 degrees C. Four-week-old mice were randomly weaned onto a low-fat (LF) or HF diet for 2 wk. In the A/J LF mice, cold exposure (4 degrees C) resulted in hypothermia, whereas the A/J HF, B/6J LF, and B/6J HF mice were cold tolerant. Cold sensitivity of the A/J LF mice was associated with a relatively low whole body energy expenditure under resting conditions, which was normalized by the HF diet. In both strains, the HF diet induced uncoupling protein-1-mediated thermogenesis, with a stronger induction in A/J mice. Only in A/J mice: 1) the HF diet augmented activation of whole body lipid oxidation by cold; and 2) at 30 degrees C, oxygen consumption, total content, and phosphorylation of AMP-activated protein kinase (AMPK), and AICAR-stimulated palmitate oxidation in soleus muscle was increased by the HF diet in parallel with significantly increased leptinemia. Gene expression data in soleus muscle of the A/J HF mice indicated a shift from carbohydrate to fatty acid oxidation. Our results suggest a role for muscle nonshivering thermogenesis and lipid oxidation in the obesity-resistant phenotype of A/J mice and indicate that a HF diet could induce thermogenesis in oxidative muscle, possibly via the leptin-AMPK axis.

The mechanisms by which ethanol induces changes in behavior are not well understood. Here, we show that Caenorhabditis elegans loss-of-function mutations in the synaptic vesicle-associated RAB-3 protein and its guanosine triphosphate exchange factor AEX-3 confer resistance to the acute locomotor effects of ethanol. Similarly, mice lacking one or both copies of Rab3A are resistant to the ataxic and sedative effects of ethanol, and Rab3A haploinsufficiency increases voluntary ethanol consumption. These data suggest a conserved role of RAB-3-/RAB3A-regulated neurotransmitter release in ethanol-related behaviors.

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.

Although microtubules are key players in many cellular processes, very little is known about their dynamic and mechanical properties in physiological three-dimensional environments. The conventional model of microtubule dynamic instability postulates two dynamic microtubule states, growth and shrinkage. However, several studies have indicated that such a model does not provide a comprehensive quantitative and qualitative description of microtubule behavior. Using three-dimensional laser light-sheet fluorescence microscopy and a three-dimensional sample preparation in spacious Teflon cylinders, we measured microtubule dynamic instability and elasticity in interphase Xenopus laevis egg extracts. Our data are inconsistent with a two-state model of microtubule dynamic instability and favor an extended four-state model with two independent metastable pause states over a three-state model with a single pause state. Moreover, our data on kinetic state transitions rule out a simple GTP cap model as the driving force of microtubule stabilization in egg extracts on timescales of a few seconds or longer. We determined the three-dimensional elastic properties of microtubules as a function of both the contour length and the dynamic state. Our results indicate that pausing microtubules are less flexible than growing microtubules and suggest a growth-speed-dependent persistence length. These data might hint toward mechanisms that enable microtubules to efficiently perform multiple different tasks in the cell and suggest the development of a unified model of microtubule dynamics and microtubule mechanics.

Quantum conditions on the control of dynamics of a system coupled to an environment are obtained. Specifically, consider a system initially in a system subspace H(0) of dimensionality M(0), which evolves to populate system subspaces H(1), H(2) of dimensionalities M(1), M(2). Then, there always exists an initial state in H(0) that does not evolve into H(2) if M(0)>dM(2), where 2

In order to understand the connectivity of neuronal networks, their constituent neurons should ideally be studied in a common framework. Since morphological data from physiologically characterized and stained neurons usually arise from different individual brains, this can only be performed in a virtual standardized brain that compensates for interindividual variability. The desert locust, Schistocerca gregaria, is an insect species used widely for the analysis of olfactory and visual signal processing, endocrine functions, and neural networks controlling motor output. To provide a common multi-user platform for neural circuit analysis in the brain of this species, we have generated a standardized three-dimensional brain of this locust. Serial confocal images from whole-mount locust brains were used to reconstruct 34 neuropil areas in ten brains. For standardization, we compared two different methods: an iterative shape-averaging (ISA) procedure by using affine transformations followed by iterative nonrigid registrations, and the Virtual Insect Brain (VIB) protocol by using global and local rigid transformations followed by local nonrigid transformations. Both methods generated a standard brain, but for different applications. Whereas the VIB technique was designed to visualize anatomical variability between the input brains, the purpose of the ISA method was the opposite, i.e., to remove this variability. A novel individually labeled neuron, connecting the lobula to the midbrain and deutocerebrum, has been registered into the ISA atlas and demonstrates its usefulness and accuracy for future analysis of neural networks. The locust standard brain is accessible at http://www.3d-insectbrain.com .