Neuroscience Data Analysis

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Related lectures (32)

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In Silico Neuroscience: Data Reproducibility and Reusability

Emphasizes data reproducibility and reusability in in silico neuroscience, focusing on neuroinformatics tools and methods.

Neuroscience Data Analysis

Explores neuroscience data analysis, emphasizing structured data, computational tools, and the trend of computational neuroscience as a service.

Neuron Classification: Types and Challenges

Explores neuron classification, highlighting the importance of understanding brain complexity and the challenges in defining cell types.

Network Simulation: Spontaneous Activity and Validation

Covers spontaneous brain network activity, neural simulation, and validation, emphasizing the importance of in-vitro and in-vivo conditions for accurate network modeling.

The Microcircuits of Striatum in Silico

Explores the reconstruction of a full-scale mouse striatal cellular level model to integrate and interpret striatal data.

In Silico Neuroscience: Network Assembly

Explores assembling in silico neuroscience networks, defining space, and dealing with sparse data to reconstruct brain regions.

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.

Network Simulation and Activity Dynamics

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

Structured Synaptic Connectivity

Explores the structured synaptic connectivity between hippocampal regions using techniques like mGRASP and modeling.

Transgenic Mice: Cre-Lines and Optogenetics

Covers transgenic mice using Cre-lines and optogenetics, exploring neural cell manipulation techniques and brain region-specific gene expression.