Introduction to Computational Neuroscience
( 24 Modules )

Module #1
Introduction to Computational Neuroscience
Overview of the field, importance, and applications

Module #2
Neural Signaling and Communication
Basics of neural signaling, action potential, and synaptic transmission

Module #3
Neuron Types and Functions
Introduction to different types of neurons, their morphology, and functions

Module #4
Neural Circuits and Systems
Organization of neural circuits, systems, and their interactions

Module #5
Mathematical Modeling of Neurons
Introduction to mathematical modeling of neurons, Hodgkin-Huxley model

Module #6
Computational Models of Neural Systems
Computational models of neural systems, integrate-and-fire model

Module #7
Neural Networks and Learning
Introduction to neural networks, types of neural networks, and learning rules

Module #8
Perceptron and Single-Layer Networks
Introduction to perceptron and single-layer neural networks

Module #9
Multi-Layer Networks and Backpropagation
Introduction to multi-layer neural networks and backpropagation algorithm

Module #10
Neural Dynamics and Bifurcations
Introduction to neural dynamics, bifurcations, and phase transitions

Module #11
Spiking Neuron Models
Introduction to spiking neuron models, leaky integrate-and-fire model

Module #12
Synaptic Plasticity and Learning
Introduction to synaptic plasticity, Hebbian learning, and Spike-Timing-Dependent Plasticity (STDP)

Module #13
Recurrent Neural Networks and Oscillations
Introduction to recurrent neural networks and oscillations in neural systems

Module #14
Pattern Recognition and Classification
Introduction to pattern recognition and classification in neural networks

Module #15
Computational Models of Cognition
Introduction to computational models of cognition, attention, and decision-making

Module #16
Neural Decoding and Encoding
Introduction to neural decoding and encoding, population coding, and Bayesian inference

Module #17
Brain-Computer Interfaces and Neuroprosthetics
Introduction to brain-computer interfaces and neuroprosthetics

Module #18
Neural Disorders and Computational Models
Introduction to neural disorders, Parkinsons disease, and computational models

Module #19
Neuroimaging and Neuroinformatics
Introduction to neuroimaging techniques, functional magnetic resonance imaging (fMRI), and neuroinformatics

Module #20
Computational Neuroscience Software and Tools
Introduction to computational neuroscience software and tools, NEURON, NEST, and PyNN

Module #21
Modeling Neurological and Psychiatric Disorders
Computational modeling of neurological and psychiatric disorders, epilepsy, and depression

Module #22
Neural Systems and Cognitive Architectures
Introduction to neural systems and cognitive architectures, SOAR and LIDA

Module #23
Computational Neuroscience and Artificial Intelligence
Intersection of computational neuroscience and artificial intelligence, deep learning and neural networks

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Introduction to Computational Neuroscience career

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Introduction to Computational Neuroscience ( 24 Modules )

Introduction to Computational Neuroscience
( 24 Modules )