Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -334,96 +334,40 @@
334	334	population2.set(i_offset=0)
335	335	population1.record("v")
336	336	population2.record("v")(%%)
337		-(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
	337	+(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
338	338	synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
339	339	connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
340	340	(% style="color:#000000" %)sim.run(100.0)(%%)
341		-(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
342		-(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
343		-(% style="color:#000000" %)Figure(
344		- Panel(
345		- (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5],
346		- xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%)
347		-(% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)"
348		- ),
349		- (% style="color:#e74c3c" %)Panel(
350		- data2_v[:, 0:5],
351		- xticks=True, xlabel="Time (ms)",
352		- yticks=True"
353		- ),(%%)
354		-(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
355		- annotations="Simulated with NEST"
356		-).show()
357		-
358		-**Run script in terminal, show figure**
359		-)))
360		-
361		-(% class="wikigeneratedid" %)
362		-and there we have it, our simple neuronal network of integrate-and-fire neurons, written in PyNN, simulated with NEST. If you prefer to use the NEURON simulator, PyNN makes this very simple, we import the PyNN-for-NEURON module instead.
363		-
364		-(% class="box infomessage" %)
365		-(((
366		-**Screencast** - current state of editor
367		-\\(% style="color:#000000" %)"""Simple network model using PyNN"""
368		-\\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
369		-(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
370		-(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
371		-(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
372		-(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
373		- (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
374		- v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
375		- v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
376		-(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
377		-(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
378		-(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
379		-population2.set(i_offset=0)
380		-population1.record("v")
381		-population2.record("v")(%%)
382		-(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
383		-synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
384		-connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
385		-(% style="color:#000000" %)sim.run(100.0)(%%)
386		-(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0]
387		-data2_v = population2.get_data().segments[0].filter(name='v')[0]
	341	+(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
388	388	Figure(
389	389	Panel(
390		- data1_v[:, 0:5],
391		- xticks=True,
	344	+ data_v[:, 0:5],
	345	+ xticks=True, xlabel="Time (ms)",
392	392	yticks=True, ylabel="Membrane potential (mV)"
393	393	),
394		- Panel(
395		- data2_v[:, 0:5],
396		- xticks=True, xlabel="Time (ms)",
397		- yticks=True"
398		- ),(%%)
399		-(% style="color:#000000" %) title="Response of simple network",
400		- annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"
	348	+ title="Response of first five neurons with heterogeneous parameters",
	349	+ annotations="Simulated with NEST"
401	401	).show()
402	402
403		-**Run script in terminal, show figure**
	352	+Run script in terminal, show figure
404	404	)))
405	405
406		-(% class="wikigeneratedid" %)
407		-As you would hope, NEST and NEURON give essentially identical results.
	355	+(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %)
	356	+(% class="small" %)**Summary (In this tutorial, you have learned to do X…)**
408	408
409		-(% class="box successmessage" %)
410		-(((
411		-**Slide** recap of learning objectives
412		-)))
	358	+.
413	413
414		-That is the end of this tutorial, in which I've demonstrated how to build a simple network using PyNN, and to simulate it using two different simulators, NEST and NEURON.
	360	+(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %)
	361	+(% class="small" %)**Acknowledgements if appropriate**
415	415
416		-Of course, PyNN allows you to create much more complex networks than this, with more realistic neuron models, synaptic plasticity, spatial structure, and so on. You can also use other simulators, such as Brian or SpiNNaker, and you can run simulations in parallel on clusters or supercomputers.
	363	+.
417	417
418		-We will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce these more advanced features of PyNN, so keep an eye on the EBRAINS website.
	365	+(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %)
	366	+(% class="small" %)**References to websites (For more information, visit us at…)**
419	419
420		-(% class="box successmessage" %)
421		-(((
422		-**Slide** acknowledgements, contact information
423		-)))
	368	+.
424	424
425		-(% class="wikigeneratedid" %)
426		-PyNN has been developed by many different people, with financial support from several different organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS and Human Brain Project grants.
	370	+(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %)
	371	+(% class="small" %)**Contact information (For questions, contact us at…)**
427	427
428		-(% class="wikigeneratedid" %)
429		-For more information visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community.
	373	+.