Contributing

The library allows us to define and simulate models from computational neuroscience easily. The library exposes two functions:

function sim!(p::Vector{AbstractPopulation}, c::Vector{AbstractConnection}, duration<:Real) end function train!(p::Vector{AbstractConnection}, c:Vector{AbstractConnection}, duration<:Real) end The functions support simulation with and without neural plasticity; the model is defined within the arguments passed to the functions. Models are composed of 'AbstractPopulation' and 'AbstractConnection' arrays.

Any elements of AbstractPopulation must implement the method:

function integrate!(p, p.param, dt) end Conversely, elements of AbstractConnection must implement the methods:

function forward!(p, p.param) end function plasticity!(c, c.param, dt) end The library is rich in examples of common neuron models that can be used as a basis.

In the notebook folder, there is a tutorial about how to use SparseMatrices in the SNN framework.