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.
Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.
This paper investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to grey scale and colour image segmentation, an important task in image understanding and object recognition. A proposed neural system that exploits the synergy between neural oscillators and Kohonen self-organising maps (SOMs) is presented. It consists of a two-dimensional grid of neural oscillators which are locally connected through excitatory connections and globally connected to a common inhibitor. Each neuron is mapped to a pixel of the input image and existing objects, represented by homogenous areas, are temporally segmented through synchronisation of the activity of neural oscillators that are mapped to pixels of the same object. Self-organising maps form the basis of a colour reduction system whose output is fed to a 2D grid of neural oscillators for temporal correlation-based object segmentation. Both chromatic and local spatial features are used. The system is simulated in Matlab and its demonstration on real world colour images shows promising results and the emergence of a new bioinspired approach for colour image segmentation. The paper concludes with a discussion of the performance of the proposed system and its comparison with traditional image segmentation approaches.
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