Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Neuronal ensemble

Related lectures (32)

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.

Helping Humans: Neuroprosthetics MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores the application of computational neuroscience in neuroprosthetics, focusing on predicting intended arm movements based on spike times and the importance of systematic parameter optimization.

Networks of Spiking Neurons

Explores networks of spiking neurons, mean-field models, transfer functions, and predictions of state space and neuron behavior.

Cellular Physiology: Neuronal Firing Patterns

Explores recreating neuronal firing patterns through ion channel models and genetic algorithms.

Ring Attractor Dynamics in Drosophila Brain

Explores ring attractor networks in the Drosophila brain for maintaining heading direction during navigation.

Neural Interfaces: Course Overview

Covers neural interfaces, course structure, projects, and interdisciplinary design principles.

Dynamical Response Functions in Neuroscience

By Tatjana Tchumatchenko explores dynamical response functions in neuroscience, emphasizing the role of linear response functions in understanding neural activity.

Motor Neuroprosthetics: Brain-Machine Interfaces

Explores brain-machine interfaces for motor neuroprosthetics, covering recording techniques, EEG signals, and motor decoding.

Mean-field Argument: Asynchronous Activity MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores stationary asynchronous activity in neuronal populations and the mean-field argument.

Head direction cells MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Delves into head direction cells, encoding animal heading and persisting in the dark.

Optogenetics: Probing Neuronal Activity with Light

Explores optogenetics, genetically-encoded tools, and clinical applications in probing neuronal activity with light.

Recent Advances in Structural Learning for Graphical Models

Covers recent advances in structural learning for graphical models, including Gaussian models, mixed models, and extreme events.

Imaging the brain in action MOOC: Cellular Mechanisms of Brain Function

Explores imaging brain activity using various techniques and covers spatiotemporal dynamics, sensorimotor cortex mapping, and real-time imaging.

Concluding remarks MOOC: Cellular Mechanisms of Brain Function

Summarizes the course on brain function, highlighting progress and future directions.

Neuronal Dynamics: Random Networks MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

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

Stationary Mean-Field: Neuronal Dynamics MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores the stationary mean-field concept in computational neuroscience to predict neuronal activity based on population and single neuron firing rates.

Structured Synaptic Connectivity in Hippocampal Regions

Explores synaptic connectivity in hippocampal regions, emphasizing the complexity of neuronal networks and the role of modeling approaches.

Modeling Neuronal Activity

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

Neuronal Activity Probing: Optogenetics & Calcium Tools

Explores light- and calcium-gated tools for probing neuronal activity, focusing on optogenetics and fusion protein engineering.

Stochastic numerical methods for many-body quantum systems

Explores stochastic methods for quantum systems, including exact diagonalization, variational methods, neural networks, and machine learning.