Neuron Classification: Types and Challenges

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Neuroscience Data Analysis

Explores neuroscience data analysis, focusing on K+ and Na+ currents, Ca²+ currents, and data reproducibility.

In Silico Neuroscience: Data Reproducibility and Reusability

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

The Microcircuits of Striatum in Silico

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

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.

Neurosciences I: Molecular Neuroscience and Neurodegeneration

Covers molecular neuroscience, neurodegeneration, cell types, neuron structure, and history of neuroscience breakthroughs.

Approaches and Rationale of Simulation Neuroscience

Explores the overview, rationale, and strategies of simulation neuroscience, emphasizing the challenges of reconstructing and simulating the brain.

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.

Introduction to Systems Neuroscience: Memory Systems Overview

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Structured Synaptic Connectivity

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

In Silico Neuroscience: Modern Approaches and Applications

Introduces modern neuroscience approaches and applications, emphasizing computational modeling and its importance in understanding the brain.