Modeling in vitro data

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Brain-Computer Interfaces: Advancements in Systems Neuroscience

Covers brain-computer interfaces and their impact on systems neuroscience and neuroprosthetics.

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.

Attractor Networks and Spiking Neurons

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

Likelihood of Spike Train in GLM

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

Neuronal Dynamics: Random Networks

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

Modeling Electrophysiology: Different Scales

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

MathDetour 1: Separation of time scales

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

Membrane potential fluctuations

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

Helping Humans: Neuroprosthetics

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.

Spike Response Model (SRM)

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