Understanding Neuronal Morphologies: NeuroM Basics

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Neuronal Dynamics: Random Networks

Explores the dynamics of neuronal populations, emphasizing random networks and mean-field arguments for connectivity.

In Silico Neuroscience: Network Simulation

Delves into simulating network dynamics in in silico neuroscience, covering spontaneous and evoked activity, in-vitro and in-vivo simulations, and sensitivity analysis.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Dynamical modeling, decoding, and control of multiscale brain network activity

Explores dynamical modeling, decoding, and control of brain network activity for personalized therapy.

Challenge of even bigger models

Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Introduction into the module

Introduces nerve cells' electrical activity, focusing on spikes and synaptic potentials.

Neuroscience and Machine Learning

Explores the intricate relationship between neuroscience and machine learning, highlighting the challenges of analyzing neural data and the role of machine learning tools.

Neural Networks: Perceptron

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Brain-Computer Interfaces: Advancements in Systems Neuroscience

Covers brain-computer interfaces and their impact on systems neuroscience and neuroprosthetics.