def __init__(self, input_data, model):
config = input_data.getNodeAttr('/', 'config')
Configurable.__init__(self, config['recording'])
self._add_config_value('recording_duration', quantity)
self._add_config_value('rate_bin_size', quantity)
self._add_config_value('store_times', quantity_list)
self._add_config_value('current_timestep', int)
self._add_config_value('weights_timestep', int)
self.input_data = input_data
self.dt = quantity(config['dt'])
self.model = model
self.m_spikes = b.SpikeMonitor(model.neuron)
self.m_rates = b.PopulationRateMonitor(model.neuron, self.rate_bin_size)
self.m_exc_syn_currents = b.RecentStateMonitor(
model.neuron, 'I_exc', self.recording_duration,
timestep=self.current_timestep)
self.m_inh_syn_currents = b.RecentStateMonitor(
model.neuron, 'I_inh', self.recording_duration,
timestep=self.current_timestep)
self.m_exc_weights = b.StateMonitor(
model.exc_synapses, 'w', record=True,
timestep=self.weights_timestep)
self.m_inh_weights = b.StateMonitor(
model.inh_synapses, 'w', record=True,
timestep=self.weights_timestep)
self.model.add(
self.m_spikes, self.m_rates, self.m_exc_syn_currents,
self.m_inh_syn_currents, self.m_exc_weights, self.m_inh_weights)
help="Path to the input file for which to continue the simulation.")
parser.add_argument(
'-t', '--time', type=float, nargs=1, required=True,
help="Additional time to simulate in seconds.")
parser.add_argument(
'output', nargs=1, type=str,
help="Filename of the HDF5 output file.")
parser.add_argument(
'label', nargs='?', type=str,
help="Label for the simulation. Will create a directory with the same "
+ "to store the produced data.")
args = parser.parse_args()
outpath = 'Data'
if args.label is not None:
outpath = os.path.join(outpath, args.label)
with tables.openFile(args.input[0], 'r') as input_data:
config = input_data.getNodeAttr('/', 'config')
b.defaultclock.dt = quantity(config['dt'])
model = SingleCellModel(config)
model.exc_synapses.w[:, :] = \
input_data.root.weights.excitatory.weights[:, -1]
model.inh_synapses.w[:, :] = \
input_data.root.weights.inhibitory.weights[:, -1]
recorder = SingleCellModelContinuedRecorder(input_data, model)
with tables.openFile(os.path.join(outpath, args.output[0]), 'w') as outfile:
outfile.setNodeAttr('/', 'config', config)
recorder.record(outfile, args.time[0] * b.second)
p.write(
quantity, ['model', 'tau_exc'], r'\tau_{\text{E}}',
'Decay constant of excitatory synaptic conductance')
p.write(
quantity, ['model', 'tau_inh'], r'\tau_{\text{I}}',
'Decay constant of inhibitory synaptic conductance')
end_section()
start_section('Plasticity Model')
p.write(
quantity, ['model', 'tau_stdp'], r'\tau_{\text{STDP}}',
'Decay constant of (pre and post) synaptic trace')
p.write(float, ['model', 'eta'], r'\eta\I{}', 'Learning rate of inhibitory synapses')
print r'$\eta\E{}$ & 0.0025, 0.005, 0.0075 & Learning rate of excitatory synapses\\'
init_inh_w = set(str(
config.quantity(c['model']['init_inh_w']) /
config.quantity(c['model']['g_inh_bar'])) for c in p.configs)
print r'$w\I{,i}(\SI{0}{\second})$ & %s & Initial inhibitory synaptic weights\\' % \
", ".join(init_inh_w)
init_exc_w = r''''''
print r'''$w\E{,j}(\SI{0}{\second})$ &
$0.3 + \frac{1.1}{1 + (K(j) - P)^4} + \xi$ &
Initial excitatory synaptic weights with input signal number
$K(i) \in [1, N_S]$ of synapse $j$, position of the tuning curve peak $P$,
and a noise term $\xi \in [0, 0.1]$\\'''
print r'$P$ & 5 & Peak of the tuning curve\\'
p.write(
quantity, ['model', 'g_exc_bar'], r'\bar g_{\text{E}}',
'Excitatory synaptic weight change to conductance conversion factor')
p.write(
quantity, ['model', 'g_inh_bar'], r'\bar g_{\text{I}}',
