Unsupervised classification of neural spikes with a hybrid multilayer artificial neural network.
The understanding of the brain structure and function and its computational style is one of the biggest challenges both in Neuroscience and Neural Computation. In order to reach this and to test the predictions of neural network modeling, it is necessary to observe the activity of neural populations. In this paper we propose a hybrid modular computational system for the spike classification of multiunits recordings. It works with no knowledge about the waveform, and it consists of two moduli: a Preprocessing (Segmentation) module, which performs the detection and centering of spike vectors using programmed computation; and a Processing (Classification) module, which implements the general approach of neural classification: feature extraction, clustering and discrimination, by means of a hybrid unsupervised multilayer artificial neural network (HUMANN). The operations of this artificial neural network on the spike vectors are: (i) compression with a Sanger Layer from 70 points vector to five principal component vector; (ii) their waveform is analyzed by a Kohonen layer; (iii) the electrical noise and overlapping spikes are rejected by a previously unreported artificial neural network named Tolerance layer; and (iv) finally the spikes are labeled into spike classes by a Labeling layer. Each layer of the system has a specific unsupervised learning rule that progressively modifies itself until the performance of the layer has been automatically optimized. The procedure showed a high sensitivity and specificity also when working with signals containing four spike types.
García
P
P
Department of Statistics, Operating Research and Computation of La Laguna University, Canary Islands, Spain.
Suárez
C P
CP
Rodríguez
J
J
Rodríguez
M
M
D000818:Animals; D016477:Artifacts; D005071:Evoked Potentials / Q000502:physiology; D008297:Male; D008433:Mathematics; D016571:Neural Networks, Computer; D051381:Rats; D017207:Rats, Sprague-Dawley; D012680:Sensitivity and Specificity