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Finding Parameters: Neuron Simulation Optimization
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Related lectures (59)
AdEx model: Firing patterns and phase plane analysis
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Network Simulation and Activity Dynamics
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Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.
Computational Neuroscience: Biophysics & Modeling
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Covers the fundamentals of computational neuroscience, focusing on biophysics and modeling.
Computational Neuroscience: Biophysics
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Covers modern neuroscience synthesis, biophysical modeling, and brain building blocks with practical applications.
Neural System Organization
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Explores the organization of the nervous system, including neuron structure, synapses, neurotransmitters, and neural circuits.
Neural Cell Membrane Dynamics
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Explores neural cell membrane dynamics, including ion channels, action potentials, myelination, and bioelectronic interfaces.
Biophysical Understanding of Neuronal Behavior
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Explores the biophysical understanding of neuronal behavior, focusing on action potentials, neuron modeling challenges, and dendritic inhibition.
Building Neural Networks: Assembly Strategies
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Focuses on assembling neural network building blocks and dealing with data sparseness using various strategies and assumptions.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
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By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Multi-layer Neural Networks
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Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Neural Signal Recording: Electrode Types and Applications
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Explores various electrode types for neural signal recording and their applications in in-vivo and in-vitro settings.
Graph Search: Neural Networks and Deep Learning
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Delves into graph search, neural networks, and deep learning, covering topics like convolutional neural networks and artificial neural networks.
Untitled
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Neural Networks: Perceptron and Backpropagation
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Covers the basics of neural networks, including the perceptron model and backpropagation.
Improving Models of the Ventral Visual Pathway
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Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.
Feature Engineering: Missing Data and Standardization
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Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.
Deep Learning: Multilayer Perceptron and Training
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Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Neural Networks Recap: Activation Functions
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Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.
Feature Selection, Kernel Regression, Neural Networks Playground
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Covers feature selection, kernel regression, and neural networks through exercises.
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