This tutorial has been designed for the following classes:
- 2021/2022 Master class on Biorobotics of Complex Systems (Prof: Alberto Mazzoni), Physics Degree, University of Pisa, Italy
- 2021/2022 PhD class on Information Theory and Neural Modeling for Neural Engineering (Prof: Alberto Mazzoni), PhD Program in Biorobotics, Scuola Superiore Sant'Anna, Pisa
- 2020/2021 PhD class on Large Scale Network Simulations, NeuroSchool PhD Program, INT, Marseille, France
- make the EBRAINS credentials to access the SpiNNaker server (https://spinn-20.cs.man.ac.uk/hub/login)
- login on the Jupyter Lab interface
- clone this repository
git clone https://github.com/albertoarturovergani/CNT-2022 - Open the directory
SpiNNaker/and run the CNT notebook
- neurons
- cell types
- populations
- recording variables
- connections
- synapse types
- connections types
- projections
- simulation managing
- computational settings
- save and load outputs
- visualization tools
- entry network
- decaying network
- persistent network
- diverging network
- small-world network
- testing cell models network
- testing STDP model network
- VA_balance network
- basis of spiking neural network theory (https://neuronaldynamics.epfl.ch/online/index.html) or (https://neuromatch.io/academy/)
- familiarity with physical quantities related to electric circuits (e.g., voltages, conductances, currents, etc)
- basic python coding (numpy, work with dictionaries, some matplotlib tools, etc)
- import the simulator
- setup the simulator
- decide the cell types
- design the populations
- define the synapse types
- select the connection algorithm
- make the projections
- idealize the stimulus
- run the simulation
- save the results
- recover the results
- postprocessing (visualization, statistics, etc)
- close the simulations