Dr Fariba Sharifian

School of Computer Science and Mathematics

Research interests:
Cognitive Neuroscience, Computational Neuroscience, Artificial Intelligence, Machine Learning, Visual Coding, Visual Learning, Neural Network Modeling, Neuroimaging, Functional Magnetic Resonance Imaging, Vision, Visual Attention, Contextual Modulation.

Degrees

Aalto University, Finland, PhD
Sharif University of Technology, Iran, Master
Sharif University of Technology, Iran, Bachelor

Academic appointments

Senior Lecturer in Computer Science, Liverpool John Moores University, 2022 - present
Senior Lecturer in Computer Science, Glyndwr University, 2021 - 2022
Research Fellow, IfADo - Leibniz-Institut für Arbeitsforschung an der TU Dortmund, 2019 - 2021
Postdoctoral Researcher, Ruhr University Bochum, 2018 - 2019
Postdoctoral Researcher, Otto-von-Guericke-University Magdeburg, 2016 - 2018

Journal article

Billichova M, Joyce Coan L, Czanner S, Kovacova M, Sharifian F, Czanner G. 2024. Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment PLoS One, DOI Author Url Publisher Url

Arnau S, Sharifian F, Wascher E, Larra MF. 2023. Removing the cardiac field artefact from the EEG using neural network regression. Psychophysiology, DOI Publisher Url Public Url

Wascher E, Sharifian F, Gutberlet M, Schneider D, Getzmann S, Arnau S. 2022. Mental chronometry in big noisy data PLOS ONE, 17 :e0268916-e0268916 DOI Publisher Url

Sharifian F, Schneider D, Arnau S, Wascher E. 2021. Decoding of cognitive processes involved in the continuous performance task International Journal of Psychophysiology, 167 :57-68 DOI Publisher Url Public Url

Schmidt A, Geringswald F, Sharifian F, Pollmann S. 2020. Not scene learning, but attentional processing is superior in team sport athletes and action video game players Psychological Research, 84 :1028-1038 DOI Publisher Url

Preuschhof C, Sharifian F, Rosenblum L, Pohl TM, Pollmann S. 2019. Contextual cueing in older adults: Slow initial learning but flexible use of distractor configurations Visual Cognition, 27 :563-575 DOI Publisher Url

Wang L, Sharifian F, Napp J, Nath C, Pollmann S. 2019. Persistent and flexible perceptual training effect in simulated retinal implant vision Journal of Vision, 19 :27a-27a DOI Publisher Url

Wang L, Sharifian F, Napp J, Nath C, Pollmann S. 2018. Cross-task perceptual learning of object recognition in simulated retinal implant perception Journal of Vision, 18 :22-22 DOI Publisher Url

Sharifian F, Contier O, Preuschhof C, Pollmann S. 2017. Reward modulation of contextual cueing: Repeated context overshadows repeated target location Attention, Perception, & Psychophysics, 79 :1871-1877 DOI Publisher Url

Sharifian F, Heikkinen H, Vigário R, Vanni S. 2016. Contextual Modulation is Related to Efficiency in a Spiking Network Model of Visual Cortex Frontiers in Computational Neuroscience, 9 DOI Publisher Url

Vanni S, Sharifian F, Heikkinen H, Vigário R. 2015. Modeling fMRI signals can provide insights into neural processing in the cerebral cortex Journal of Neurophysiology, 114 :768-780 DOI Publisher Url

Heikkinen H, Sharifian F, Vigario R, Vanni S. 2015. Feedback to distal dendrites links fMRI signals to neural receptive fields in a spiking network model of the visual cortex Journal of Neurophysiology, 114 :57-69 DOI Publisher Url

Sharifian F, Nurminen L, Vanni S. 2013. Visual Interactions Conform to Pattern Decorrelation in Multiple Cortical Areas Boraud T. PLoS ONE, 8 :e68046-e68046 DOI Publisher Url

Fellowships:

Fellow of the Higher Education Academy (FHEA), Advance HE.

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