The identification of cortical pyramidal neurons using a RPP based algorithm

Appropriately classifying neuronal subgroups is critical to numerous downstream procedures in several disciplines of biomedical research. The cortical pyramidal neuron characterization technology has achieved rapid development in recent years. However, capturing true neuronal features for accurate pyramidal neuron 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 cortical pyramidal neuron 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 cortical pyramidal neuron databases through array matching.