Lectures related to Head direction cells

Explores neural navigation mechanisms and the Nobel Prize awarded discoveries.

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.

Role of the hippocampus MOOC: Simulating a hippocampus micro-circuit

Delves into the hippocampus's role in memory, space, and time encoding, and its implications in brain pathologies.

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.

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.

Scientific Computing in Neuroscience

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

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.

Escape noise MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores escape noise in computational neuroscience, covering stochastic intensity, interspike intervals, likelihood functions, noise model comparison, and rate versus temporal codes.

AdEx model: Firing patterns and phase plane analysis MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Three definitions of rate code MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Interspike Intervals & Renewal Processes MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Models and data MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Generalized Integrate-and-Fire Models MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

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.

MathDetour 1: Separation of time scales MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores the concept of separation of time scales in computational neuroscience and the reduction of detail in two-dimensional neuron models.

Modeling in vitro data MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Spike Response Model (SRM)MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Parameter estimation MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

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

Variability of Spike Trains MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores the variability of spike trains in computational neuroscience, covering experiments, sources of variability, and stochastic spike arrival and firing.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla Log in to Mediaspace to watch this video

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