Investigation of multi-site micro recordings of subthalamic nucleus neurons using machine learning MER with DBS in Parkinson`s – A simulation study

Multineural spikes were acquired with a multisite electrode placed in the hippocampus pyramidal cell layer of non-primate anesthetized snitch animals. If the impedance of each electrode-site is relatively low and the distance amongst electrode sites is appropriately miniatured, a spike generated by a neuron is parallelly recorded at multielectrode sites with different amplitudes. The covariance between the spike of the at each electrode-point and a template was computed as a damping-factor due to the volume conduction of the spike from the neuron to electrode-site. Computed damping factors were vectorized and analyzed by simple but elegant hierarchical-clustering using a multidimensional statistical-test. Since a cluster of damping vectors was shown to correspond to an antidromically identified neuron, spikes of distinct neurons are classified by suggesting to the scatterings of damping vectors. Errors in damping vector computing due to partially overlapping spikes were minimized by successively subtracting preceding spikes from raw data. Clustering errors due to complex-spike-bursts (i.e., spikes with variable-amplitudes) were prevented by detecting such bursts and using only the first spike of a burst for clustering.

Graph Learning-Based Ontology Sparse Vector Computing

The ontology sparse vector learning algorithm is essentially a dimensionality reduction trick, i.e., the key components in the p-dimensional vector are taken out, and the remaining components are set to zero, so as to obtain the key information in a certain ontology application background. In the early stage of ontology data processing, the goal of the algorithm is to find the location of key components through the learning of some ontology sample points, if the relevant concepts and structure information of each ontology vertex with p-dimensional vectors are expressed. The ontology sparse vector itself contains a certain structure, such as the symmetry between components and the binding relationship between certain components, and the algorithm can also be used to dig out the correlation and decisive components between the components. In this paper, the graph structure is used to express these components and their interrelationships, and the optimal solution is obtained by using spectral graph theory and graph optimization techniques. The essence of the proposed ontology learning algorithm is to find the decisive vertices in the graph Gβ. Finally, two experiments show that the given ontology learning algorithm is effective in similarity calculation and ontology mapping in some specific engineering fields.