Adult neurogenesis in the olfactory bulb (OB) is considered as a competition in which neurons scramble during a critical selection period for integration and survival. Moreover, newborn neurons are thought to replace pre-existing ones that die. Despite indirect evidence supporting this model, systematic in vivo observations are still scarce. We used two-photon in vivo imaging to study neuronal integration and survival. We show that loss of new neurons in the OB after arrival at terminal positions occurs only at low levels. Moreover, long-term observations showed that no substantial cell death occurred at later stages. Neuronal death was induced by standard doses of thymidine analogs, but disappeared when low doses were used. Finally, we demonstrate that the OB grows throughout life. This shows that neuronal selection during OB-neurogenesis does not occur after neurons reached stable positions. Moreover, this suggests that OB neurogenesis does not represent neuronal turnover but lifelong neuronal addition.

We propose simple and effective models for the image annotation that make use of Convolutional Neural Network (CNN) features extracted from an image and word embedding vectors to represent their associated tags. Our first set of models is based on the Canonical Correlation Analysis (CCA) framework that helps in modeling both views -visual features (CNN feature) and textual features (word embedding vectors) of the data. Results on all three variants of the CCA models, namely linear CCA, kernel CCA and CCA with k-nearest neighbor (CCA-KNN) clustering, are reported. The best results are obtained using CCA-KNN which outperforms previous results on the Corel-5k and the ESP-Game datasets and achieves comparable results on the IAPRTC-12 dataset. In our experiments we evaluate CNN features in the existing models which bring out the advantages of it over dozens of handcrafted features. We also demonstrate that word embedding vectors perform better than binary vectors as a representation of the tags associated with an image. In addition we compare the CCA model to a simple CNN based linear regression model, which allows the CNN layers to be trained using back-propagation.

The current interest in developing Glycine transporter Type 1 (GlyT-1) inhibitors, for diseases such as schizophrenia, has led to the demand for a GlyT-1 PET molecular imaging tool to aid drug development and dose selection. We report on [(11) C]GSK931145 as a novel GlyT-1 imaging probe in primate and man. Primate PET studies were performed to determine the level of specific binding following homologous competition with GSK931145 and the plasma-occupancy relationship of the GlyT-1 inhibitor GSK1018921. Human PET studies were performed to determine the test-retest reproducibility of [(11) C]GSK931145 and the plasma-occupancy relationship of GSK1018921. [(11) C]GSK931145 entered primate and human brain and yielded a heterogeneous pattern of uptake which was similar in both species with highest uptake in midbrain, thalamus, and cerebellum. Homologous competition in primates indicated no viable reference region and gave binding potential estimates between 1.5 and 3 for midbrain, thalamus and cerebellum, While the distribution and binding potential values were similar across species, both the plasma free fraction (f(P) : 0.8 vs. 8%) and delivery (K(1) : 0.025 vs. 0.126 ml cm(-3) min(-1) ) were significantly lower in humans. Test-retest reproducibility in humans calculated using a two tissue compartmental model was poor (VAR(V(T) ): 29-38%), but was improved using a pseudo reference tissue model (VAR(BP(ND) ): 16-23%). GSK1018921 EC(50) estimates were 22.5 and 45.7 ng/ml in primates and humans, respectively.

981 95 U S A For a model cortical neuron with three active conductances, we studied the dependence of the firing rate on the degree of synchrony in its synaptic inputs. The effect of synchrony was determined as a function of three parameters: number of inputs, average input frequency, and the synaptic strength (maximal unitary conductance change). Synchrony alone could increase the cell's firing rate when the product of these three parameters was below a critical value. But for higher values of the three parameters, synchrony could reduce firing rate. Instantaneous responses to time-varying input firing rates were close to predictions from steady-state responses when input synchrony was high, but fell below steady-state responses when input synchrony was low. Effectiveness of synaptic transmission, measured by the peak area of cross-correlations between input and output spikes, increased with increasing synchrony.

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