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A preprint abstract from biorxiv.org discusses the role of sensory experience in the development of neural circuits in the mouse visual cortex. The study used single-nucleus RNA-sequencing to analyze the transcriptional responses of cells in the visual cortex to sensory deprivation or stimulation. The researchers identified 1,268 unique sensory-induced genes and found that excitatory neurons, particularly layer 2/3 pyramidal neurons, were highly sensitive to sensory stimulation. The dataset provides valuable insights into the molecular mechanisms through which sensory experience shapes neural circuit wiring in the developing brain. However, it is important to note that this preprint has not yet undergone peer review. [Extracted from the article]

Neurogliaform cells are a type of GABAergic cortical interneurons that have volume transmission output properties. Their activity and function within cortical circuits are not well understood. This study developed genetic tools to target these neurons and found that their spontaneous activity is positively correlated with locomotion. Silencing these neurons increased spontaneous activity during locomotion and impaired visual responses in other neurons. The contrast-dependent visual response of neurogliaform cells varies with their location in the cortex and is influenced by their morphology and input connectivity. This research highlights the importance of neurogliaform cells in regulating cortical activity and their role in visual stimuli. [Extracted from the article]

In this paper, we propose a bioinspired model for human action recognition through modeling neural mechanisms of information processing in two visual cortical areas: the primary visual cortex (V1) and the middle temporal cortex (MT) dedicated to motion. This model, named V1-MT, is composed of V1 and MT models (layers) corresponding to their cortical areas, which are built with layered spiking neural networks (SNNs). Some neuron properties in V1 and MT, such as direction and speed selectivity, spatiotemporal inseparability, and center surround suppression, are integrated into SNNs. Based on speed and direction selectivity, V1 and MT models contain multiple SNN channels, each of which processes motion information in sequences with spatiotemporal tunings of neurons at a certain speed and different directions. Therefore, we propose two operations, input signal perceiving with 3-D Gabor filters and surround inhibition processing with 3-D differences of Gaussian functions, to perform this task according to the spatiotemporal inseparability and center surround suppression of neurons. Then, neurons are modeled with our simplified integrate-and-fire model and motion information is transformed into spike trains. Afterward, we define a new feature vector: a mean motion map computed from spike trains in all channels to represent human actions. Finally, a support vector machine is trained to classify actions represented by the feature vectors. We conducted extensive experiments on public action databases, and the results show that our model outperforms other bioinspired models and rivals the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]