In Silico Neuroscience: Modeling the Brain Complexity

Related lectures (32)

Page 3 of 4

Explores the relationship between neuroscience and machine learning in visual intelligence.

Covers NeuroM basics, including checking neuron quality, extracting morphometrics, and visualizing neurons in different formats.

Covers a tutorial on simulating a hippocampus microcircuit using the user-friendly SimUI.

Explores the peculiar properties, variability, optimization, and degeneracy of CA1 neurons.

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

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Covers the fundamentals of neural signals and signal processing, focusing on modeling and simulation of neural systems.

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