| 1 | import pyNN.nest as pynn |
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| 2 | |
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| 3 | import numpy |
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| 4 | import pylab |
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| 5 | |
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| 6 | pynn.setup(timestep=0.1) |
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| 7 | |
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| 8 | numCellsExc = 2 |
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| 9 | numCellsInh = 2 |
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| 10 | |
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| 11 | w_exc = 0.0002 # uS |
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| 12 | w_inh = 0.0008 # uS |
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| 13 | |
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| 14 | expDuration = 1000 # msec |
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| 15 | |
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| 16 | if_cond_exp_params = { |
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| 17 | 'tau_m' : 20.0, |
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| 18 | 'i_offset' : 0.1, |
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| 19 | 'tau_refrac' : 3.0, |
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| 20 | 'v_thresh' :-63.0, |
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| 21 | 'tau_syn_E' : 30.0, |
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| 22 | 'tau_syn_I' : 30.0, |
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| 23 | 'v_reset' :-69.0, |
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| 24 | 'e_rev_E' : 0., |
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| 25 | 'e_rev_I' :-70., |
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| 26 | 'v_init' :-70.0 |
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| 27 | } |
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| 28 | |
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| 29 | cells_exc = pynn.create(pynn.IF_cond_exp, if_cond_exp_params, n=numCellsExc) |
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| 30 | cells_inh = pynn.create(pynn.IF_cond_exp, if_cond_exp_params, n=numCellsInh) |
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| 31 | |
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| 32 | pynn.connect(source=cells_inh, target=cells_exc, weight=w_inh, synapse_type='inhibitory') |
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| 33 | pynn.connect(source=cells_inh, target=cells_inh, weight=w_inh, synapse_type='inhibitory') |
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| 34 | |
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| 35 | pynn.record(cells_exc, 'spikes_nest.dat') |
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| 36 | |
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| 37 | pynn.run(expDuration) |
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| 38 | pynn.end() |
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| 39 | |
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| 40 | |
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| 41 | |
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