Computational Neuroscience

Description:

This course provides a comprehensive introduction to brain science, human psychophysics, neural activity, and computational neuroscience. It covers fundamental concepts such as brain signals (EEG & MEG), spiking neural activity, and behavioral readouts. The course also includes hands-on programming with Python, focusing on the Leaky Integrate-and-Fire (LIF) neuron model for simulating neural dynamics.

Eligible Audience:

• Undergraduate and graduate students in neuroscience, psychology, and biomedical engineering.
• Researchers interested in computational neuroscience and neurotechnology.
• Data scientists and AI enthusiasts exploring brain-inspired computing.
• Developers and engineers working on brain-computer interfaces (BCI).

Content:

Module 1: Introduction to Brain Science

1. Introduction to Brain Science Part I – Overview of neuroscience fundamentals.
2. Introduction to Brain Science Part II – Basic principles of brain function and neural circuits.

Module 2: Human Psychophysics & Behavioral Analysis

3. Human Psychophysics Part I – Sensory perception and cognitive processing.
4. Human Psychophysics Part II – Measurement techniques in psychophysics.
5. Human Psychophysics Part III – Applications and real-world implications.
6. Behavioral Readouts – Understanding behavioral responses in neuroscience experiments.

Module 3: Neural Activity and Brain Signals

7. Spiking Activity Part I – Introduction to action potentials and neural spikes.
8. Spiking Activity Part II – Neural coding and information processing.
9. Brain Signals EEG & MEG Part I – Basics of electroencephalography (EEG) and magnetoencephalography (MEG).
10. Brain Signals EEG & MEG Part II – Applications and analysis of EEG & MEG signals.

Module 4: Computational Neuroscience & Python Programming

11. LIF Neuron Part I [Python] – Introduction to the Leaky Integrate-and-Fire (LIF) neuron model.
12. LIF Neuron Part II [Python] – Implementing LIF models in Python.
13. LIF Neuron Part III [Python] – Advanced simulations of LIF neurons.
14. LIF Neuron Part IV [Python] – Optimizing and analyzing neural models.
15. LIF Neuron Part V [Python] – Exploring network-level simulations.
16. LIF Neuron Part VI [Python] – Integrating LIF neurons with real-world data.

Course Outcomes:

By the end of this course, participants will be able to:

• Understand core concepts of brain science and psychophysics.
•  Analyze behavioral and neural responses using experimental techniques.
• Interpret EEG & MEG signals and their applications in neuroscience.
• Implement and simulate LIF neuron models using Python.
• Apply computational neuroscience techniques to model neural activity.

Instructor:

Eng: Nada Salah

• Bachelor’s degree in Biomedical and Bioinformatics Engineering,Egypt-Japan University of Science and Technology.
• computational neuroscience researcher with an interest in the cognitive and computational aspects of the brain. 
• focuses on the Brain-Computer Interfaces, EEG Modalities, Neuroinformatics, and Machine Learning.
• Member at Arabs in Neuroscience (AiN)
• Member at Women in Neuroscience UK (WiNUK)