PT  - JOURNAL ARTICLE
AU  - Mahdi Zarei
TI  - Spike discharge prediction based on Neuro-fuzzy system
AID  - 10.1101/133967
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 133967
4099  - http://biorxiv.org/content/early/2017/05/03/133967.short
4100  - http://biorxiv.org/content/early/2017/05/03/133967.full
AB  - This paper presents the development and evaluation of different versions of Neuro-Fuzzy model for prediction of spike discharge patterns. We aim to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, we analyzed the Cerebral Cortex data of the cat from [29]. Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Wang and Mendel (WM), Dynamic evolving neural-fuzzy inference system (DENFIS), Hybrid neural Fuzzy Inference System (HyFIS), genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means (SBC) algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms.