چکیده مقاله
The brain's intricate coordination requires integrating massive amounts of information from numerous disciplines Modern methods include multielectrode arrays, calcium imaging, and optogenetics offer neuron level data In systems and circuit neuroscience, investigating huge populations of neurons is difficult Experimental neurotechnology, optimal control, signal processing, network analysis, and dimensionality reduction may solve these problems Univariate statistical technique in neuroscience research fails to show component interactions and their effects This study uses support vector machines, principal component and factor analysis, cluster analysis, multiple linear regression, and random forest regression The discipline of connectomics studies brain connections at big and small scales This shows how neuroinformatics accelerates progress NIF integrates neurological data, making database integration easy Data mining across many neuroscience data layers is also examined for pros and cons
کلیدواژهها
نویسندگان
Elyas Hadizadeh Tasbiti
Master Student of Computer Engineering Department, Imam Khomeini International University, Qazvin, Iran
Morteza Mohammadi Zanjireh
Assistant Professor of Computer Engineering Department, Imam Khomeini International University, Qazvin, Iran
شیوه ارجاع
Hadizadeh Tasbiti, Elyas and Mohammadi Zanjireh, Morteza,1402,Beyond Univariate Statistics: Harnessing Neuroinformatics and Data Mining for Comprehensive Brain Understanding,5th International Conference on Software Computing,Rudsar
ارائهشده در
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