Lecture

Structural Plasticity of Synapses: Applications in Neuroscience

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Explores data strategies for building neural circuits in simulation neuroscience.

Explores synaptic transmission, neurotransmitters, and neural plasticity principles.

Discusses dendrites as cables, covering the cable equation derivation and current conservation in coupled compartments.

Compares electrical and chemical synapses, emphasizing their properties and behavior, including the neuromuscular junction and synapses in the CNS.

Explores Rall's cable model, synaptic potential spread in dendrites, and compartmental modeling for neural simulations.

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

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

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Explores the role of synapses in computational neuroscience and the models of excitatory and inhibitory synapses.

Introduces nerve cells' electrical activity, focusing on spikes and synaptic potentials.

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

Explores optogenetics, chemogenetics, and sonogenetics to engineer neural activity using light, chemicals, and sound.

Explains injecting current into a neuron, creating recordings, running simulations, and handling synapses.

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

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

Covers computational neuroscience basics, single neuron models, synapses, and neural variability.

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Explores principles of data-driven modeling for simulating hippocampus microcircuits.

Explores the derivation of the cable equation for modeling dendrites in computational neuroscience.

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