Aligned Poly-l-lactic Acid Nanofibers Induce Self-Assembly of Primary Cortical Neurons into 3D Cell Clusters.

Relative to two-dimensional (2D) culture, three-dimensional (3D) culture of primary neurons has yielded increasingly physiological responses from cells. Electrospun nanofiber scaffolds are frequently used as a 3D biomaterial support for primary neurons in neural tissue engineering, while hydrophobic surfaces typically induce aggregation of cells. Poly-l-lactic acid (PLLA) was electrospun as aligned PLLA nanofiber scaffolds to generate a structure with both qualities. Primary cortical neurons from E18 Sprague-Dawley rats cultured on aligned PLLA nanofibers generated 3D clusters of cells that extended highly aligned, fasciculated neurite bundles within 10 days. These clusters were viable for 28 days and responsive to AMPA and GABA. Relative to the 2D culture, the 3D cultures exhibited a more developed profile; mass spectrometry demonstrated an upregulation of proteins involved in cortical lamination, polarization, and axon fasciculation and a downregulation of immature neuronal markers. The use of artificial neural network inference suggests that the increased formation of synapses may drive the increase in development that is observed for the 3D cell clusters. This research suggests that aligned PLLA nanofibers may be highly useful for generating advanced 3D cell cultures for high-throughput systems.

Biologically Inspired Intensity and Depth Image Edge Extraction.

In recent years, artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low-cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature, it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks, we emulate functional computational aspects of biological visual systems. The results demonstrate that the proposed bioinspired artificial vision system has increased performance over existing computer vision feature extraction approaches.