Dear Andreas,

we spent a lot of efforts to calibrate synaptic weights, e.g., see PhD  
thesis by Brüderle [1] and master thesis by Petkov [2].
A rate-based calibration similar to the one described in the thesis by  
Brüderle is used to calibrate synaptic weights.
In particular, the parameters drvifall (affects the time constant and  
synaptic weight; this parameter first, because it is more sensitive)  
and drviout (affects mostly the synaptic weight) are adjusted until a  
target rate of the postsynaptic neurons is reached.
As these parameters can be configured only row-wise for synapses,  
firing rates are averaged across all hardware neurons during  
calibration.
Due to this protocol, synapses are calibrated to generate EPSPs with  
comparable impact on the firing rates of postsynaptic neurons (i.e.  
area under EPSP), but not necessarily with comparable time constants.
As it is difficult to extract the synaptic time constant from the  
membrane potential, we can not provide you with numbers on how  
correlated time constants and area under EPSPs are.
For an example on how to modify synapse calibrations, see [3].

Regards,

Thomas

P.S. We are sorry, the source code of the calibration routines is not  
published yet, but is in preparation.

[1] http://www.kip.uni-heidelberg.de/Veroeffentlichungen/details.php?id=1917
[2] http://www.kip.uni-heidelberg.de/Veroeffentlichungen/details.php?id=2635
[3]  
https://github.com/electronicvisions/spikey_demo/blob/master/networks/epsp.py


Zitat von Andreas Stoeckel <[log in to unmask]>:

> Dear Spikey users and developers,
>
> the Spikey-PyNN interface LIF neuron model provides a limited number
> of neuron parameters compared to the canonical IF_cond_exp model --
> which is perfectly fine for my application.
>
> However, in order to have some coarse prediction of how the neurons
> behave, I need some values for the missing parameters in the
> IF_cond_exp model. The non-user definable parameters are:
>
> cm --> set to 0.2 nF
> tau_m --> implicitly given as cm / g_leak
> e_rev_E --> set to 0 mV
> tau_syn_E --> ?
> tau_syn_I --> ?
>
> As I don't use inhibitory synapses, I basically only need the value
> for tau_syn_E. I could of course capture the membrane potential over
> time and fit it to the IF_cond_exp model, but is there a easier
> method for getting these values?
>
> Eric mentioned last week that these could probably be read from the
> calibration tools, but I couldn't quite find something like that in
> the source code tree.
>
> Thank you for your support!
>
> Cheers,
> Andreas
>
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