Lecture

AdEx: Adaptive exponential integrate-and-fire

Related lectures (32)

Covers the Spike Response Model (SRM) in computational neuroscience and its relation to the adaptive leaky integrate-and-fire model.

Parameter estimation

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.

Models and data

Covers the optimization of neuron models for coding and decoding in computational neuroscience.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Likelihood of Spike Train in GLM

Covers the Generalized Linear Model (GLM) in computational neuroscience.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Behavioural Control in Animals and Robots

Explores controlling behavior in animals and robots, covering historical perspectives, neuron activation, Drosophila model, advanced techniques, and mini-project organization.

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.

Modeling in vitro data

Explores modeling in vitro data for computational neuroscience, including predicting subthreshold voltage and spike times.

Interspike Intervals & Renewal Processes

Explores interspike intervals, renewal processes, and escape noise experiments in neuronal dynamics.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Generalized Integrate-and-Fire Models

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

Likelihood of a spike train

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.

Attractor Networks and Spiking Neurons

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

Clustering & Density Estimation

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

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

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

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Neural Networks: Training and Activation

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

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Scientific Computing in Neuroscience

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