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
