1. Introduction to dynamical system. Spiking neuron as a dynamical system.
Phase portraits – why is it useful to study ion channel dynamics in the phase space.
Bifurcations – Resting, Spiking, periodic spiking, bursting, Hodgkin classification of spiking
2. Linear Systems, Differential equations, Hodgkin-Huxley model, Fitzhugh-Nagumo model.
3. Neural Encoding: Spike Trains and Firing Rates, Spike Train Statistics,The Neural Code, Estimating Firing Rates, Introduction to Receptive Fields – neuronal tuning
4. Introduction to Neural Decoding and Information theory: Entropy, Mutual Information
5. Neuronal populations, Macroscopic recordings, Pulse to wave conversions, Basics of EEG and MEG: Neuro-electromagnetism, Field theoretic approaches.
Reference:
Matlab:
Basic Syntax and variables, Writing source codes and function files to solve differential equations used in coursework.
Phase portraits – why is it useful to study ion channel dynamics in the phase space.
Bifurcations – Resting, Spiking, periodic spiking, bursting, Hodgkin classification of spiking
2. Linear Systems, Differential equations, Hodgkin-Huxley model, Fitzhugh-Nagumo model.
3. Neural Encoding: Spike Trains and Firing Rates, Spike Train Statistics,The Neural Code, Estimating Firing Rates, Introduction to Receptive Fields – neuronal tuning
4. Introduction to Neural Decoding and Information theory: Entropy, Mutual Information
5. Neuronal populations, Macroscopic recordings, Pulse to wave conversions, Basics of EEG and MEG: Neuro-electromagnetism, Field theoretic approaches.
Reference:
- Theoretical Neuroscience – Computational and Mathematical Modelling of Neural Systems by Dayan and Abbot
- Dynamical Systems in Neuroscience by Eugene Izikevich
- Tutorial on Neurobiology: Single neuron to brain chaos by Walter Freeman
Matlab:
Basic Syntax and variables, Writing source codes and function files to solve differential equations used in coursework.