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@rowleya this is my reimagining of the modular neuron stuff using templates. I'm pretty happy with it aside from the fact that it seems to be making the adaptive neuron way slower for no reason I can yet understand.

* Neurons are split into dynamics, input and threshold
* Combined together using something resembling policy design pattern into ModularNeuron
* Implemented if_curr_exp and if_cond_exp in this way
* In C++ member functions which are SYNTACTICALLY inline are inline
* Static inline deprecated in C++
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LGTM - just one comment regarding the merging of the currents (assume this will be shown).

static inline bool Update(MutableState &mutableState, const ImmutableState &immutableState,
S1615 excInput, S1615 inhInput, S1615 extCurrent)
static S1615 Update(MutableState &mutableState, const ImmutableState &immutableState,
S1615 inputCurrent)
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Not sure that I would merge these currents prior to this point. Keeping them separate allows the possibility of having a merged-in-conductance model for example (we would have to provide a Raw input type which would look very like the Curr, and a Passive synapse type)

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2 participants