Spiking neural net example

[sneakers-the-rat]

here ya go dan, this makes probably the least computational sense of all the demos but here ya go - you can indeed do a spiking neural net where all the neurons run in different processes and spend 99% of the time in ser/des shuttling events back and forth lmao. cycles are actually no problem when you don't care about event alignment that much.

python -m noob run examples/snn-fully-connected.yaml --runner zmq-freerun

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📚 Documentation preview 📚: https://noob--199.org.readthedocs.build/en/199/

[codspeed-hq]

Merging this PR will improve performance by 10.03%

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

⚡ 1 improved benchmark
✅ 7 untouched benchmarks
⏩ 8 skipped benchmarks¹

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	Simulation	test_kitchen_sink_process[ZMQRunner-testing-kitchen-sink]	3.3 ms	3 ms	+10.03%

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_{Comparing nobes-snn (c52feda) with main (fbff7cf)}

Footnotes

1. 8 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

[sneakers-the-rat]

this we'll actually merge - providing runners is a sort of obvious thing to be able to do