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The seven major cell classes of the vertebrate retina are organized with remarkable precision into distinct layers.
The method requires an annotated data set to train the machine-learning algorithms, either by staining a subset of the investigated cells with markers, or by visual inspection and assignment of cell classes of interest.
The component parts and cell classes of the mammalian brain increase at highly predictable but differential rates with respect to absolute brain size.
Fig. 1 Cell classes of the neural model described in [23, 24] are excitatory interneurons (E), lateral inhibitory interneurons (L), crossed inhibitory interneurons (C), and edge cells (EC).
Here, we cultured three major neural cell classes of rat brain (ie, neurons, astrocytes, and oligodendrocyte precursor cells [OPCs]) obtained from commercial sources.
These three key retinal cell classes of the through pathway have been shown to contribute to particular components of the ERG waveform (see Frishman [ 3] and Weymouth and Vingrys [ 4] for review).
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In the cyprinid Carassius auratus, there was a proliferation of cell classes beyond the expectations of lamination.
The transcriptome-level profiling of seqFISH+ allows unbiased identification of cell classes and their spatial organization in tissues.
where C is the number of cell classes.
Analysis of FTL expression in the retinas of mice following diurnal exposure to light shows that bipolar cells, specific classes of amacrine cells, ganglion cells, and a dense network of processes in the inner plexiform layer are functionally activated.
Thus, interneuron types equipped with different morphological and physiological properties will support cell class-specific processing of excitatory inputs.
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