Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Granger causality (GC) analysis has emerged as a porful analytical method for estimating the causal relationship among various types of neural activity data. Hover, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions beten neurons. (2) How is the estimated causality related to the underlying synaptic ights? Based on the GC,  propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic ights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects beten neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results shod, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR.
['Pei-Chiang Shao', 'Jian-Jia Huang', 'Wei-Chang Shann', 'Chen-Tung Yen', 'Meng-Li Tsai', 'Chien-Chang Yen']
I am writing a research paper on a idea Granger causality-based synaptic weights estimation for analyzing neuronal networks
[{"from": "human", "value": "I am writing a research paper on a idea Granger causality-based synaptic weights estimation for analyzing neuronal networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Granger causality (GC) analysis has emerged as a porful analytical method for estimating the causal relationship among various types of neural activity data. Hover, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions beten neurons. (2) How is the estimated causality related to the underlying synaptic ights? Based on the GC,  propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic ights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects beten neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results shod, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR."}]