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singlecell-trained.py
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singlecell-trained.py
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#!/usr/bin/env python
from config import Configurable, quantity
import brian as b
import inputs
import logging
from singlecell import ModelInputGroups
logger = logging.getLogger('single-cell-trained')
class TrainedModelBuilder(Configurable):
def __init__(self, config):
Configurable.__init__(self, config)
self._add_config_value('tau', quantity) # membrane time constant
self._add_config_value('V_rest', quantity) # resting membrane potential
self._add_config_value('threshold', quantity) # spiking threshold
self._add_config_value('g_leak', quantity) # leak conductance
self._add_config_value('refractory_period', quantity)
self._add_config_value('V_exc', quantity) # excitatory reversal potential
self._add_config_value('V_inh', quantity) # inhibitory reversal potential
self._add_config_value('I_b', quantity) # additional current
self._add_config_value('g_exc_bar', quantity)
self._add_config_value('g_inh_bar', quantity)
self._add_config_value('init_inh_w', quantity)
self._add_config_value('tau_exc', quantity) # excitat. syn. time constant
self._add_config_value('tau_inh', quantity) # inhibit. syn. time constant
self._add_config_value('tau_stdp', quantity)
self._add_config_value('tau_w', quantity)
self._add_config_value('eta', float)
self._add_config_value('rho', quantity)
self.alpha = 2 * self.rho * self.tau_stdp
self.eqs = b.Equations('''
dg_exc/dt = -g_exc / self.tau_exc : siemens
I_exc = g_exc * (self.V_exc - V) : amp
dg_inh/dt = -g_inh / self.tau_inh : siemens
I_inh = g_inh * (self.V_inh - V) : amp
dV/dt = ((self.V_rest - V) + (I_exc + I_inh + self.I_b) / \
self.g_leak) / self.tau : volt
dx/dt = -x / self.tau_stdp : 1
''')
def build_neuron_group(self, num_neurons=1):
return b.NeuronGroup(
num_neurons, model=self.eqs, reset=self.V_rest,
threshold=self.threshold, refractory=self.refractory_period)
def build_exc_synapses(self, source, target, weights):
connection = b.Connection(source, target, 'g_exc')
connection[:, 0] = weights
return connection
def build_inh_synapses(self, source, target, weights):
connection = b.Connection(source, target, 'g_inh')
connection[:, 0] = weights
return connection
class SingleCellTrainedModel(b.Network, Configurable):
def __init__(self, config, exc_weights, inh_weights):
b.Network.__init__(self)
Configurable.__init__(self, config)
self._add_config_value('stimulus_duration', quantity)
builder = TrainedModelBuilder(config['model'])
self.input_gen = inputs.GroupedSpikeTimesGenerator(
config['inputs'], self.stimulus_duration)
self.indexing_scheme = self.input_gen.get_indexing_scheme()
self.neuron = builder.build_neuron_group()
self.input_neurons = b.SpikeGeneratorGroup(
self.input_gen.num_trains, inputs.swap_tuple_values(self.input_gen))
self.input_groups = ModelInputGroups(
self.indexing_scheme, self.input_neurons)
self.exc_synapses = builder.build_exc_synapses(
self.input_groups.excitatory, self.neuron, exc_weights)
self.inh_synapses = builder.build_inh_synapses(
self.input_groups.inhibitory, self.neuron, inh_weights)
self.add(
self.neuron, self.input_neurons, self.inh_synapses,
self.exc_synapses)
class SingleCellModelSpikeRecorder(Configurable):
def __init__(self, config, model):
Configurable.__init__(self, config)
self._add_config_value('stimulus_duration', quantity)
self._add_config_value('num_trials', int)
self.model = model
self.m_spikes = b.SpikeMonitor(model.neuron)
self.model.add(self.m_spikes)
def record(self, outfile):
self._store_group_memberships(outfile)
outfile.flush()
for i in xrange(self.num_trials):
logger.info(
'Running time trial %i of %i', i + 1, self.num_trials)
self.model.run(self.stimulus_duration, report='text')
self._store_recent_spikes(outfile, i)
outfile.flush()
self._store_signals(outfile)
outfile.flush()
@staticmethod
def _store_array_with_unit(
outfile, where, name, array, unit, *args, **kwargs):
node = outfile.createArray(where, name, array, *args, **kwargs)
node.attrs.unit = unit
return node
def _store_group_memberships(self, outfile):
group = outfile.createGroup('/', 'group_memberships')
outfile.createArray(
group, 'inhibitory', self.model.input_groups.inh_group_membership)
outfile.createArray(
group, 'excitatory', self.model.input_groups.exc_group_membership)
def _store_recent_spikes(self, outfile, trial):
where = '/spikes'
name = 'trial%i' % trial
title = "Spike times of model neuron."
if self.m_spikes[0].size > 0:
self._store_array_with_unit(
outfile, where, name, self.m_spikes[0], 'second', title,
createparents=True)
else:
node = outfile.createEArray(
where, name, tables.Float32Col(), self.m_spikes[0].shape, title,
createparents=True)
node.attrs.unit = 'second'
self.m_spikes.reinit()
def _store_signals(self, outfile):
group = outfile.createGroup('/', 'signals')
for i, signal_gen in enumerate(self.model.input_gen.signal_gens):
outfile.createArray(group, 's%i' % i, signal_gen.signal)
if __name__ == '__main__':
import argparse
import json
import os.path
import tables
from brian.globalprefs import set_global_preferences
set_global_preferences(useweave=True)
logging.basicConfig()
logger.setLevel(logging.INFO)
parser = argparse.ArgumentParser(
description="Run the Vogels et al. 2011 single cell model.")
parser.add_argument(
'-c', '--config', type=str, nargs=1, required=True,
help="Path to the configuration file.")
parser.add_argument(
'-i', '--input', type=str, nargs=1, required=True,
help="Path to the data of the trained model used as input.")
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 open(args.config[0], 'r') as f:
config = json.load(f)
b.defaultclock.dt = quantity(config['dt'])
with tables.openFile(args.input[0], 'r') as data:
model = SingleCellTrainedModel(
config, data.root.weights.excitatory.weights[:, -1],
data.root.weights.inhibitory.weights[:, -1])
recorder = SingleCellModelSpikeRecorder(config, model)
with tables.openFile(os.path.join(outpath, args.output[0]), 'w') as outfile:
outfile.setNodeAttr('/', 'config', config)
recorder.record(outfile)