It would be useful to have more comments/explanation in this script of the contents of the packet
that is printed out. Even better would be to include code for a full packet decode - for example extracting the neuron id from the packet.

Dear all,
When I try to use live_spikes_connection_receive.add_receive_callback( "pop_forward", receive_spikes) to add a call back function for population "pop_forward" in spike_io.py, the call back function "receive_spikes" is never called upon the receival of spikes, although the injection of spikes is successful. I am unclear that whether there is something wrong with configuration of database communication in spike_io.py or parameters settings in spynnaker.cfg in the root directory. As I use the spike_io.py from github for a trial, I just list the spynnaker.cfg settings to see if you could give me some help,

[Machine]
machineName = 192.168.240.253
version = 3

machineTimeStep = 1000

timeScaleFactor = 1

[Database]
create_database = True
wait_on_confirmation = True
create_routing_info_to_neuron_id_mapping = True
send_start_notification = True

listen_port = 19996
notify_port = 19999
notify_hostname = localhost

[Reports]
reportsEnabled = True
writeTextSpecs = False
writePartitionerReports = True
writePlacerReports = True
writeRouterReports = True
writeRouterInfoReport = True
writeRouterDatReport = False
writeTransceiverReport = True
writeProvanceData = True
writeTagAllocationReports = True
outputTimesForSections = True
writeReloadSteps = True
defaultReportFilePath = DEFAULT
defaultApplicationDataFilePath = DEFAULT
max_reports_kept = 10
max_application_binaries_kept = 10

[Simulation]
spikes_per_second = 30
ring_buffer_sigma = 5

[Routing]

algorithm = BasicDijkstra
generate_graphs = False
graphs_output_file = tmp

[Placer]
algorithm = Radial

[TagAllocator]
algorithm = Basic

[Partitioner]
algorithm = PartitionAndPlace

[KeyAllocator]
algorithm = MallocBased

[SpecExecution]
specExecOnHost = True

[MasterPopTable]
generator = BinarySearch

In PyNNExamples/examples/stdp_example.py, "stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=10.0, tau_minus=10.0
,nearest=True),
weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=1,
A_plus=0.001, A_minus=0.0012))"
There are A_plus and A_minus as parameters in weight_dependence, however, when I check pyNN neuron models for class STDPMechanism http://neuralensemble.org/docs/PyNN/_modules/pyNN/standardmodels/synapses.html#AdditiveWeightDependence, parameters A_plus and A_minus are in timing_dependence instead of weight_dependence, is there some difference in STDPMechanism in spinnaker?

And my second question is that, what is the length of the LTP and LTD time window in STDPMechanism? This piece of info is important for the settings of simulation parameters.

the pynn example of using the push bot fails to execute correctly.

Untill such time as this works correctly with the master branches of the tool chain, the code cannot be pulled into the system.

This issue is to keep track of the problem, whilst allwoing us to close the pull request, for metric purposes.