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Raju S. Bapi

Affiliations: 
University of Hyderabad, Hyderabad, Telangana, India 
Area:
Neural Networks, Sequence learning, fMRI, BioInformatics
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"Raju Bapi"
Mean distance: 14.14 (cluster 29)
 		SNBCP
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Parents

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Daniel S. Levine grad student University of Texas at Arlington

Children

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Krishna P. Miyapuram grad student University of Hyderabad
V. S. Chandrasekhar Pammi grad student University of Hyderabad
Rakesh Sengupta grad student University of Hyderabad

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Publications

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Bera K, Shukla A, Bapi RS. (2021) Cognitive and Motor Learning in Internally-Guided Motor Skills. Frontiers in Psychology. 12: 604323
Bera K, Shukla A, Bapi RS. (2021) Motor Chunking in Internally Guided Sequencing. Brain Sciences. 11
Wutz A, Shukla A, Bapi RS, et al. (2015) Expansion and Compression of Time Correlate with Information Processing in an Enumeration Task. Plos One. 10: e0135794
Wutz A, Shukla A, Bapi RS, et al. (2014) Subjective Distortions of Time Influence Rapid Information Accrual Procedia - Social and Behavioral Sciences. 126: 127-128
Miyapuram KP, Pamnani U, Doya K, et al. (2014) Inter Subject Correlation of Brain Activity during Visuo-Motor Sequence learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8834: 35-41
Pammi VS, Miyapuram KP, Ahmed, et al. (2012) Changing the structure of complex visuo-motor sequences selectively activates the fronto-parietal network. Neuroimage. 59: 1180-9
Miyapuram KP, Doya K, Bapi RS. (2011) Chunking During Learning of Visuomotor Sequences with Spatial and Arbitrary Rules: Preliminary Findings Psychological Studies. 57: 22-28
Chakravarthy VS, Joseph D, Bapi RS. (2010) What do the basal ganglia do? A modeling perspective. Biological Cybernetics. 103: 237-53
Singh V, Miyapuram KP, Bapi RS. (2007) Detection of cognitive states from fMRI data using machine learning techniques Ijcai International Joint Conference On Artificial Intelligence. 587-592
Bapi RS, Miyapuram KP, Graydon FX, et al. (2006) fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences. Neuroimage. 32: 714-27
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