def get_spikes(model=None, reset=None, threshold=None,
input=None, input_var='I', dt=None,
**params):
"""
Retrieves the spike times corresponding to the best parameters found by
the modelfitting function.
**Arguments**
``model``, ``reset``, ``threshold``, ``input``, ``input_var``, ``dt``
Same parameters as for the ``modelfitting`` function.
``**params``
The best parameters returned by the ``modelfitting`` function.
**Returns**
``spiketimes``
The spike times of the model with the given input and parameters.
"""
duration = len(input) * dt
ngroups = len(params[params.keys()[0]])
group = NeuronGroup(N=ngroups, model=model, reset=reset, threshold=threshold,
clock=Clock(dt=dt))
group.set_var_by_array(input_var, TimedArray(input, clock=group.clock))
for param, values in params.iteritems():
if (param == 'delays') | (param == 'fitness'):
continue
group.state(param)[:] = values
M = SpikeMonitor(group)
net = Network(group, M)
net.run(duration)
reinit_default_clock()
return M.spikes
