Course

NX-450: Computational neurosciences: biophysics

Lectures in this course (15)

Covers modern neuroscience synthesis, biophysical modeling, and brain building blocks with practical applications.

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

Neuroscience Data Analysis

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

Neuron Classification: Types and Challenges

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

Ion Channels: Structure and Function

Explores the biophysics of ion channels and their vital role in neuronal signaling and physiological processes.

Biophysical Understanding of Neuronal Behavior

Explores the biophysical understanding of neuronal behavior, focusing on action potentials, neuron modeling challenges, and dendritic inhibition.

Neocortical Neuron Classification

Explores neocortical neuron classification, ion channel kinetics, challenges in modeling individual cells, and data integration.

Structured Synaptic Connectivity

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

Synaptic Transmission: Electrical vs. Chemical

Explores the differences between electrical and chemical synapses, synaptic plasticity, and receptor behavior.

Synaptic Plasticity: Forms and Mechanisms

Explores different forms of synaptic plasticity and the mechanisms behind them, emphasizing the role of calcium in inducing and maintaining plastic changes.

Building Neural Networks: Assembly Strategies

Focuses on assembling neural network building blocks and dealing with data sparseness using various strategies and assumptions.

Network Simulation and Activity Dynamics

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

Scientific Computing in Neuroscience

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

Neuromorphic Computing: Challenges & Future

Compares digital and neuromorphic computing, highlighting challenges and future prospects in the field.

Transgenic Mice: Cre-Lines and Optogenetics

Explores transgenic mice, optogenetics, single-cell RNA sequencing, the Allen Brain Atlas, and electrode technologies in neuroscience research.