  Correctly identifying neuronal subsets is critical to multiple downstream
methods in several areas of neuroscience research. The hippocampal interneuron
characterization technology has achieved rapid development in recent years.
However, capturing true neuronal features for accurate interneuron
characterization and segmentation has remained elusive. In the current study, a
novel global preserving estimate algorithm is used to capture the non-linearity
in the features of hippocampal interneurons after factor Algorithm. Our results
provide evidence for the effective integration of the original linear and
nonlinear neuronal features and achieves better characterization performance on
multiple hippocampal interneuron databases through array matching.
