Computational Cognitive Science Lab

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Welcome to our computational cognition and learning lab in the Department of Cognitive Science at IIIT Hyderabad.

What exactly are the research questions?

Research in our lab is investigating how do different brain areas communicate and allocate resources, routes information and optimize information processing during various task conditions (emotion, perception, multisensory integration). What exactly are the causal effects physical parameters such as communication delay between brain areas, noise and oscillations at different frequencies in shaping up functional responses (integration and segregation) between salient brain regions under perceptual learning, visual processing, motor processing, Auditory processing and resting state conditions? How do their functional responses (emotion processing, multisensory integration, language processing, attention) alter in aging, under various disease states? What is current understanding overlap between healthy and pathological aging based on prominent Cognitive Science theory of neuro compensation and de-differentiation? How do hierarchical brain areas communicate using underlying neuronal dynamics to shape up memory, attention, action selection and cognition?

What are our methods in understanding cognitive dynamics?

Our group is also concerned with questions in the area of developing intelligent data analysis combining  machine learning, nonlinear dynamical systems, network analysis to uncover interrelationships and dependencies among variables observed in the neuroimaging data recorded in fMRI, EEG, MEG. In the information age, intelligent data analysis goes beyond the pure collection and organization the of data. Hence, group develops intelligent methods, algorithms to understand structural organization of the human brain and their functional correlates. With multiple national and international collaboration with leading neuroimaging labs currently the group is engaged in research to the above important questions that arise frequently  in cognitive neuroscience, especially to understand mechanistically the role of learning, context dependent modulation and noise in shaping up functional and perhaps behavioral responses. Learning is thought to change the connections between the neurons in the brain, a process called synaptic plasticity. Using mathematical modeling and  computational tools, we model synaptic plasticity across different time scales (fast time scales milliseconds activity) to very slow synaptic activity (in the order of multiple of minutes, days) that reproduces experimental findings. We develop methods, benchmark simulation platforms, connectivity toolboxes to understand concerted activity such as global oscillations involving multiple brain areas on the temporal scale (EEG, MEG neuroimaging techniques). At the same time we also look at the change in the spatial functional connectivity across brain areas (due to aging, learning and structural lesions) that can be empirically measured using high spatial resolution functional MRI and theoretically predicted using large scale simulation and mathematical modeling. We are working in tight collaboration with several experimental (national and international) neuroimaging and behavioral laboratories, which performs structural, functional MRI and behavioral responses in human participants. We are also actively involved in the design of novel stimuli to elicit specific behavioral responses that we are interested in, measurement and theoretical modeling of behavioral responses to understand the dynamics of cognition.