From neurogenesis to neuronal regeneration: the amphibian olfactory system as a model to visualize neuronal development in vivo

How do individual neurons develop and how are they in- tegrated into neuronal circuitry？ To answer this question is essential to understand how the nervous system develops and how it is maintained during the adult life. A neural stem cell must go through several stages of maturation, including proliferation, migration, differentiation, and integration, to become fully embedded to an existing neuronal circuit. The knowledge on this topic so far has come mainly from cell culture studies. Studying the development of individual neurons within intact neuronal networks in vivo is inherently difficult. Most neurons are generated form neural stem cells during embryonic and early postnatal development.

Activation of Parvalbumin-Positive Neurons in Both Retina and Primary Visual Cortex Improves the Feature-Selectivity of Primary Visual Cortex Neurons

Several recent studies using either viral or transgenic mouse models have shown different results on whether the activation of parvalbumin-positive（PV~＋）neurons expressing channelrhodopsin-2（ChR2） in the primary visual cortex（V1） improves the orientation-and direction-selectivity of V1 neurons. Although this discrepancy was thoroughly discussed in a follow-up communication, the issue of using different models to express ChR2 in V1 was not mentioned. We found that ChR2 was expressed in retinal ganglion cells（RGCs） and V1 neurons in ChR2fl/~＋; PV-Cre mice. Our results showed that the activation of PV~＋RGCs using white drifting gratings alone significantly decreased the firing rates of V1 neurons and improved their direction-and orientation-selectivity. Longduration activation of PV~＋interneurons in V1 further enhanced the feature-selectivity of V1 neurons in anesthetized mice, confirming the conclusions from previous findings. These results suggest that the activation of both PV~＋RGCs and V1 neurons improves feature-selectivity in mice.

Visual commonsense reasoning with directional visual connections

To boost research into cognition-level visual understanding,i.e.,making an accurate inference based on a thorough understanding of visual details,visual commonsense reasoning(VCR)has been proposed.Compared with traditional visual question answering which requires models to select correct answers,VCR requires models to select not only the correct answers,but also the correct rationales.Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity,which is helpful in solving specific cognition tasks.Inspired by this idea,we propose a directional connective network to achieve VCR by dynamically reorganizing the visual neuron connectivity that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability.Specifically,we first develop a GraphVLAD module to capture visual neuron connectivity to fully model visual content correlations.Then,a contextualization process is proposed to fuse sentence representations with visual neuron representations.Finally,based on the output of contextualized connectivity,we propose directional connectivity to infer answers and rationales,which includes a ReasonVLAD module.Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.