Syllabus

1. Structure and Function of the Brain:
This module outlines basic neuroanatomy and measurement of the brain. You will learn the principles underlying the anatomical organization of the brain and the functional segregation of higher cognitive functions, starting from the cellular level (synapses, action potentials) and working up to a more detailed consideration of the major anatomical divisions. Major functional circuits will be outlined, with an emphasis on their anatomical organization and connectivity.

2.Methods in Cognitive Neuroscience: Neural Signal Analysis and Processing, Origins of neural signals: Action potentials, local field potentials, and oscillatory activity, Neural signal types: EEG, MEG, ECoG, and invasive recordings, Signal Acquisition Techniques, EEG Principles and methods , Electrode placement, recording setup, and noise considerations, Signal digitization and pre-processing, Removing artifacts, Event-Related Potentials (ERP), Short-Time Fourier Transform (STFT), Analysis of brain rhythms and oscillations, Independent Component Analysis (ICA) for artifact removal, EEGLAB (MATLAB), MNE-Python,Neural signal analysis in cognitive tasks: Attention, memory, and decision-making.

3. Methods in Cognitive Neuroscience: Neuroimaging: CT, MRI, PET.

4. Advanced statistics: AI, Data analysis and modelling for Cognitive neuroscience:
This module covers advanced statistical techniques and AI methods for data analysis in cognitive neuroscience. It includes General Linear Models (GLM), mixed-effects models, and Bayesian hypothesis testing, alongside AI approaches like machine learning, neural networks, and time-series analysis (RNNs, LSTMs). Applications include neuroimaging data analysis (MRI, EEG), feature extraction, and brain-computer interfaces (BCI), with practical implementation using Python tools like Scikit-learn and TensorFlow. The module also addresses challenges like model interpretability, bias, and ethical considerations, preparing students for AI-driven data analysis in cognitive neuroscience.

5. Translational Research in neuroscience: Cognitive neuropsychiatry, recovery and rehabilitation after neurological damage, cognition across the life span, genetic underpinnings of cognition, and language use in deaf people.

Develop effective communication, critical thinking, and engagement skills for cognitive neuroscience research.

Introduce statistical analysis, data visualization, and advanced modeling techniques, including AI-driven methods, using Python.

Provide foundational knowledge of brain structure, neuroimaging, and cognitive processes to address current research challenges and translational applications.

At the end of the course the student will be able to
1. Communicate cognitive neuroscience concepts effectively in both oral and written forms.
2. Apply statistical techniques, such as GLM, ANOVA, and Bayesian methods, for data analysis and modeling.
3. Analyze brain anatomy, functional connectivity, and neuroimaging data to interpret mind-brain relationships.
4. Critically evaluate experimental findings and lesion-based studies to advance cognitive neuroscience understanding.
5. Translate cognitive neuroscience research into practical applications, including rehabilitation and lifespan cognitive development.

1. Banich, M. T., & Compton, R. J, Cognitive Neuroscience, Cambridge University Press , 5th Edition (2023)

Dale Purves, Roberto Cabeza, Scott A. Huettel, Kevin S. LaBar, Michael L. Platt and Marty G., Principles of Cognitive Neuroscience, Woldorff. Sinauer Associates, Inc. , 2nd Edition (2013)

MS Gazzaniga, RB Ivry, GR Mangun, Cognitive Neuroscience: The Biology of the Mind, W. W. Norton & Company , 4th Edition (2013)

Nicole Gage Bernard Baars, Fundamentals of Cognitive Neuroscience, Academic Press , 2018