In Silico Neuroscience: Network Simulation

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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.

Neuroscience Data Analysis

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

Neuroscience Data Analysis

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

Challenge of even bigger models

Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.

Building Neural Networks: Assembling Brain Regions

Discusses assembling neural networks by defining space and populating it with neurons, emphasizing the challenges and strategies for accurate morphologies and volume information.

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

Attractor Networks and Spiking Neurons

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

Interspike Intervals & Renewal Processes

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

Neuronal Connectivity Patterns

Explores neuronal connectivity patterns, connection probabilities, and experimental techniques used to study synaptic connectivity.

Modeling Electrophysiology: Different Scales

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