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Merge pull request #460 from apdavison/nest-dev
Merge nest-dev into master, support for NEST 2.12.0
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| Original file line number | Diff line number | Diff line change |
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| ======================== | ||
| PyNN 0.8.3 release notes | ||
| ======================== | ||
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| 8th March 2017 | ||
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| Welcome to PyNN 0.8.3! | ||
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| NeuroML 2 | ||
| --------- | ||
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| The `neuroml` module has been completely rewritten, and updated from NeuroML v1 to v2. | ||
| This module works like other PyNN "backends", i.e. ``import pyNN.neuroml as sim``... | ||
| but instead of running a simulation, it exports the network to an XML file in NeuroML format. | ||
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| NEST 2.12 | ||
| --------- | ||
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| This release introduces support for NEST_ 2.12. Previous versions of NEST are no longer supported. | ||
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| Other changes | ||
| ------------- | ||
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| * `A couple of bug fixes`_ | ||
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| .. _Brian: http://briansimulator.org | ||
| .. _`A couple of bug fixes`: https://github.com/NeuralEnsemble/PyNN/issues?q=is%3Aclosed+milestone%3A0.8.3 |
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| """ | ||
| Demonstration of the Generalized Integrate-and-Fire model described by Pozzorini et al. (2015) | ||
| We simulate four neurons with different parameters: | ||
| 1. spike-triggered current, fixed threshold, deterministic spiking | ||
| 2. no spike-triggered current, dynamic threshold, deterministic spiking | ||
| 3 & 4. no spike-triggered current, fixed threshold, stochastic spiking | ||
| Since neurons 1 and 2 have deterministic spiking, they should produce the same spike times with | ||
| different simulators. Neurons 3 and 4, being stochastic, should spike at different times. | ||
| Reference: | ||
| Pozzorini, Christian, Skander Mensi, Olivier Hagens, Richard Naud, Christof Koch, and Wulfram Gerstner (2015) | ||
| "Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models." | ||
| PLOS Comput Biol 11 (6): e1004275. doi:10.1371/journal.pcbi.1004275. | ||
| """ | ||
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| import matplotlib | ||
| matplotlib.use('Agg') | ||
| from pyNN.utility import get_simulator, init_logging, normalized_filename | ||
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| # === Configure the simulator ================================================ | ||
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| sim, options = get_simulator(("--plot-figure", "Plot the simulation results to a file.", {"action": "store_true"}), | ||
| ("--debug", "Print debugging information")) | ||
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| if options.debug: | ||
| init_logging(None, debug=True) | ||
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| sim.setup(timestep=0.01, min_delay=1.0) | ||
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| # === Build and instrument the network ======================================= | ||
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| t_stop = 300.0 | ||
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| parameters = { | ||
| 'neurons': { | ||
| 'v_rest': -65.0, # Resting membrane potential in mV. | ||
| 'cm': 1.0, # Capacity of the membrane in nF | ||
| 'tau_m': 20.0, # Membrane time constant in ms. | ||
| 'tau_refrac': 4.0, # Duration of refractory period in ms. | ||
| 'tau_syn_E': 5.0, # Decay time of the excitatory synaptic conductance in ms. | ||
| 'tau_syn_I': 5.0, # Decay time of the inhibitory synaptic conductance in ms. | ||
| 'e_rev_E': 0.0, # Reversal potential for excitatory input in mV | ||
| 'e_rev_I': -70.0, # Reversal potential for inhibitory input in mV | ||
| 'v_reset': -65.0, # Reset potential after a spike in mV. | ||
| 'i_offset': 0.0, # Offset current in nA | ||
| 'v_t_star': -55.0, # Threshold baseline in mV. | ||
| 'lambda0': 1.0, # Firing intensity at threshold in Hz. | ||
| 'tau_eta1': 1.0, # } | ||
| 'tau_eta2': 10.0, # } Time constants for spike-triggered current in ms. | ||
| 'tau_eta3': 100.0, # } | ||
| 'tau_gamma1': 1.0, # } | ||
| 'tau_gamma2': 10.0, # } Time constants for spike-frequency adaptation in ms. | ||
| 'tau_gamma3': 100.0, # } | ||
| # the following parameters have different values for each neuron | ||
| 'delta_v': [1e-6, 1e-6, 0.5, 0.5], # Threshold sharpness in mV. | ||
| 'a_eta1': [0.1, 0.0, 0.0, 0.0], # } | ||
| 'a_eta2': [0.1, 0.0, 0.0, 0.0], # } Post-spike increments for spike-triggered current in nA | ||
| 'a_eta3': [0.1, 0.0, 0.0, 0.0], # } | ||
| 'a_gamma1': [0.0, 5.0, 0.0, 0.0], # } | ||
| 'a_gamma2': [0.0, 5.0, 0.0, 0.0], # } Post-spike increments for spike-frequency adaptation in mV | ||
| 'a_gamma3': [0.0, 5.0, 0.0, 0.0], # } | ||
| }, | ||
| 'stimulus': { | ||
| 'start': 20.0, | ||
| 'stop': t_stop - 20.0, | ||
| 'amplitude': 0.6 | ||
| } | ||
| } | ||
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| neurons = sim.Population(4, sim.GIF_cond_exp(**parameters['neurons'])) | ||
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| electrode = sim.DCSource(**parameters['stimulus']) | ||
| electrode.inject_into(neurons) | ||
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| neurons.record(['v', 'i_eta', 'v_t']) | ||
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| # === Run the simulation ===================================================== | ||
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| sim.run(t_stop) | ||
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| # === Save the results, optionally plot a figure ============================= | ||
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| filename = normalized_filename("Results", "gif_neuron", "pkl", | ||
| options.simulator, sim.num_processes()) | ||
| neurons.write_data(filename, annotations={'script_name': __file__}) | ||
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| if options.plot_figure: | ||
| from pyNN.utility.plotting import Figure, Panel | ||
| figure_filename = filename.replace("pkl", "png") | ||
| data = neurons.get_data().segments[0] | ||
| v = data.filter(name="v")[0] | ||
| v_t = data.filter(name="v_t")[0] | ||
| i_eta = data.filter(name="i_eta")[0] | ||
| Figure( | ||
| Panel(v, ylabel="Membrane potential (mV)", | ||
| yticks=True, ylim=[-66, -52]), | ||
| Panel(v_t, ylabel="Threshold (mV)", | ||
| yticks=True), | ||
| Panel(i_eta, ylabel="i_eta (nA)", xticks=True, | ||
| xlabel="Time (ms)", yticks=True), | ||
| annotations="Simulated with %s" % options.simulator.upper() | ||
| ).save(figure_filename) | ||
| print(figure_filename) | ||
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| # === Clean up and quit ======================================================== | ||
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| sim.end() |
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