Tehran Institute for Advanced Studies (TEIAS)

/ The Interconnected Worlds of Computational Neuroscience, Machine Learning and Artificial Intelligence __ Hamed Nili


The Interconnected Worlds of Computational Neuroscience, Machine Learning and Artificial Intelligence

Hamed Nili

August 29, 2022
(7 Shahrivar, 1401)



Khatam University

Registration Deadline

August 28, 2022

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Hamed Nili

Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), Hamburg

Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom


The purpose of this talk is to provide a broad perspective into computational neuroscience. I will first describe the goal of neuroscience research. Then I will explain how the functional properties of different brain areas can be explored through applying advanced data-analysis techniques on data gathered from carefully designed experiments. I will also explain with a few examples how understanding the function of the brain at a computational level has benefitted the field of artificial intelligence. I would conclude that the three fields of computational neuroscience, machine learning and artificial intelligence cannot be treated as independent research topics, are strongly interconnected and recent years have seen a growing scope for cross-fertilization.


Hamed Nili

Hamed Nili received the BSc in Electrical Engineering from the Sharif University of Technology in Iran. In his BSc, he specialised in Control engineering. He did an MSc on applied digital signal processing in Southampton. This was followed by two years of research in Professor John Duncan’s lab in the University of Cambridge. Being John’s RA, he worked on single-cell data to study target detection in the PFC. He then started his PhD in the same department under the supervision of Dr Nikolaus Kriegeskorte. His PhD mainly consisted of developing methods for multivariate data analysis (e.g. the RSA toolbox) and also orientation invariance in the human visual system (using fMRI). In his postdocs in the University of Oxford he mostly used machine learning and the tools that he had developed, to investigate brain representations. Recently, he has been using information theory to examine the information content of brain regions in perceptual decision making and learning.