1st Day
Introduction; sensory systems
Rm 103 & 104, C.W. Chu College
09:00 – 10:30
Overall Introduction; information theoretical approaches to model synaptic plasticity
11:00 – 12:30
Student self-introduction presentations session: What experimental data and/or theoretical questions are you interested in?
14:00 – 15:30
A crash course on the biophysics of the brain
16:00 – 17:30
Models of the visual system; principles of sensory processing; receptive field; gain control; visual pathways and hierarchy
19:15 – 20:45
Math bootcamp: linear algebra; differential equations; probability.
2nd Day
Neural dynamics and representations
Rm 103 & 104, C.W. Chu College
09:00 – 10:30
Modeling how the brain learns to represent the world: Abstraction and Probability
11:00 – 12:30
Biologically constrained computational models for understanding brain functions and neural dynamics (recurrent neural networks, spiking RNN, context-dependent decision making, spatial navigation, traveling waves)
14:00 – 15:30
Dynamic latent variable models; Population representations and dynamics (examples: hippocampus, motor cortex)
16:00 – 17:30
Mathematical Minds: Modeling Neural Phenomena with Equations and Insight
19:15 – 20:45
Data analysis bootcamp: information of spike trains.
3rd Day
Neural dynamics, neural codes, and neural networks
Rm 103 & 104, C.W. Chu College
09:00 – 10:30
Neural coding in the visual system; the Linear-nonlinear-Poisson cascade model of spike generation and its application to the visual system; efficient coding, sparse coding
11:00 – 12:30
Neural circuits that mediate sensory perception and behavior; connectivity and interactions between visual and motor neural circuitry
14:00 – 15:30
Dynamics of neuronal networks and connectivity
16:00 – 17:30
Neural network models for neuroscience
19:15 – 22:15
Training RNN as models of neural activity data.
4th Day
Neural population activities over multiple timescales
Rm 103 & 104, C.W. Chu College
09:00 – 10:30
Imaging and analysis of brain-wide activity in the worm and fish
11:00 – 12:30
Organizing behaviors across different timescales
13:30 – 18:30
Boat trip to Ap Chau, with a guided tour of the island
20:00 – 21:30
Optional. Free exploration and networking time.
5th Day
Analysis of neural data; motor system
Classroom: Rm 103 & 104, C.W. Chu College
09:00 – 10:30
Analyzing complex high-dimensional data in neuroscience and biology; linear and nonlinear dimensionality reduction techniques; expectation-maximization (EM) algorithm
11:00 – 12:30
independent component analysis (ICA); non-negative matrix factorization (NMF)
14:00 – 15:30
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.
16:00 – 17:30
Free time: For problem sets, project preparation, exploration, networking etc.
19:15 – 22:15
Data analysis: machine learning and neural network techniques.
6th Day
Brain-machine interfaces; applications
Rm 103 & 104, C.W. Chu College
09:00 – 10:30
Applications of machine learning, neural network models, CNN, RNN, DNN, in neuronal signal processing, image recognition, computational neuroscience
11:00 – 12:30
Brain-machine interfaces and robotics
14:00 – 15:30
Free time: For problem sets, project preparation, exploration, networking etc.
16:00 – 17:30
Student presentation session: Project presentations