Lectures related to Stationary Mean-Field: Neuronal Dynamics

Neural Networks: Hierarchical Models and Odor Taxis

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

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

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Modeling Neuronal Activity

Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.

AdEx model: Firing patterns and phase plane analysis

Explores the AdEx neuron model, analyzing firing patterns and phase planes.

Generalized Integrate-and-Fire Models

Explores the Generalized Integrate-and-Fire Model and the Nonlinear Integrate-and-Fire Model.

Optimization of Neuroprosthetic Systems

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Neuromorphic Computing: Concepts and Hardware Implementations

Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.

In Silico Neuroscience: Ion Channels and Neuronal Models

Explores detailed modeling of ion channels and neuronal morphologies in in silico neuroscience, covering neuron classification, ion channel kinetics, and experimental observations.

Network Simulation and Activity Dynamics

Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.

Three definitions of rate code

Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.

Neural Networks: Multilayer Perceptrons

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

Engineering Neurons: Optogenetics

Explores the engineering of neurons using light, chemicals, and sound to modulate neural activity and behavior.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Neural Signals and Signal Processing: Modeling and Simulation

Covers the fundamentals of neural signals and signal processing, focusing on modeling and simulation of neural systems.

Modeling Electrophysiology: Different Scales

Covers modeling electrophysiology at different scales, discussing ion channels, single neurons, and microcircuits.

Membrane potential fluctuations

Explores stochastic spike arrival and membrane potential fluctuations in single neurons.

Neural Signal Recording: Electrode Types and Applications

Explores various electrode types for neural signal recording and their applications in in-vivo and in-vitro settings.

Building Neural Networks: Assembling Brain Regions

Discusses assembling neural networks by defining space and populating it with neurons, emphasizing the challenges and strategies for accurate morphologies and volume information.