A crash course on the biophysics of the brain

Models of a single neuron: Biophysics of neuronal membrane; Hodgkin-Huxley model, compartment model and dendritic nonlinearity; stochastic models of spike trains; linear-nonlinear models

Experimental data and/or theoretical questions that you are interested

Experimental data and/or theoretical questions that you are interested

Models of the visual system: Principles of sensory processing; receptive field; gain control; visual pathways and hierarchy

Neural coding in the visual system; computational principles of neural codes; infomax and efficient coding, sparse coding, combinatorial codes

Analysing big data in neuroscience: Nonlinear dimensionality reduction; independent component analysis (ICA); non-negative matrix factorisation (NMF); expectation-maximisation (EM) algorithm; graphical models

Data analysis in neuroscience

Models of the motor system: Control theories; sensory feedback; models of motor cortical activities; models of motor planning based on dynamical system theories; motor modularity

Brain-machine interfaces and robotics

Dynamics of neuronal networks: Working memory; grid cells; excitation-inhibition balance and chaos; cortical microcircuits

Excitation-inhibition balance and chaos; cortical microcircuits

Neural (synaptic/receptive filed) plasticity and learning: Hebbian and Spike-timing dependent plasticity; Hopfield model for associative memory; supervised/unsupervised learning; deep learning; reinforcement learning in neuroscience

Dynamic latent variable models; Population representations and dynamics (examples: hippocampus, motor cortex) 

Whole-brain activity data and circuits in small animals (part I)

Whole-brain activity data and circuits in small animals (part II)

Large-scale brain models (part I)

Large-scale brain models (part II)