def createTree(self, reinitialize=1, v_eq=-75.):
"""
Create simple NET structure
2 3
| |
| |
---1---
|
|
0
|
"""
self.v_eq = v_eq
loc_ind = np.array([0, 1, 2])
# kernel constants
alphas = 1. / np.array([.5, 8.])
gammas = np.array([-1., 1.])
alphas_ = 1. / np.array([1.])
gammas_ = np.array([1.])
# nodes
node_0 = NETNode(0, [0, 1, 2], [], z_kernel=(alphas, gammas))
node_1 = NETNode(1, [0, 1, 2], [0], z_kernel=(alphas_, gammas_))
node_2 = NETNode(2, [1], [1], z_kernel=(alphas_, gammas_))
node_3 = NETNode(3, [2], [2], z_kernel=(alphas_, gammas_))
# add nodes to tree
net_py = NET()
net_py.setRoot(node_0)
net_py.addNodeWithParent(node_1, node_0)
net_py.addNodeWithParent(node_2, node_1)
net_py.addNodeWithParent(node_3, node_1)
# store
self.net_py = net_py
self.cnet = netsim.NETSim(net_py, v_eq=self.v_eq)
def createTree2(self, reinitialize=1, add_lin=True, v_eq=-75.):
"""
Create simple NET structure
3 4
| |
| |
---2---
1 |
| |
---0---
|
"""
self.v_eq = v_eq
loc_ind = np.array([0, 1, 2])
# kernel constants
alphas = 1. / np.array([1.])
gammas = np.array([1.])
# nodes
node_0 = NETNode(0, [0, 1, 2], [], z_kernel=(alphas, gammas))
node_1 = NETNode(1, [0], [0], z_kernel=(alphas, gammas))
node_2 = NETNode(2, [1, 2], [], z_kernel=(alphas, gammas))
node_3 = NETNode(3, [1], [1], z_kernel=(alphas, gammas))
node_4 = NETNode(4, [2], [2], z_kernel=(alphas, gammas))
# add nodes to tree
net_py = NET()
net_py.setRoot(node_0)
net_py.addNodeWithParent(node_1, node_0)
net_py.addNodeWithParent(node_2, node_0)
net_py.addNodeWithParent(node_3, node_2)
net_py.addNodeWithParent(node_4, node_2)
# linear terms
alphas = 1. / np.array([1.])
gammas = np.array([1.])
self.lin_terms = {
1: Kernel((alphas, gammas)),
2: Kernel((alphas, gammas))
} if add_lin else {}
# store
self.net_py = net_py
self.cnet = netsim.NETSim(net_py,
lin_terms=self.lin_terms,
v_eq=self.v_eq)
def createPointNeurons(self, v_eq=-75.):
self.v_eq = v_eq
self.dt = .025
gh, eh = 50., -43.
h_chan = channelcollection.h()
self.greens_tree = GreensTree(
file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'ball.swc'))
self.greens_tree.setPhysiology(1., 100. / 1e6)
self.greens_tree.addCurrent(h_chan, gh, eh)
self.greens_tree.fitLeakCurrent(v_eq, 10.)
self.greens_tree.setEEq(v_eq)
self.greens_tree_pas = self.greens_tree.__copy__(new_tree=GreensTree())
self.greens_tree_pas.asPassiveMembrane()
self.sim_tree = self.greens_tree.__copy__(new_tree=NeuronSimTree())
# set the impedances
self.greens_tree_pas.setCompTree()
self.freqs = np.array([0.])
self.greens_tree_pas.setImpedance(self.freqs)
# create sov tree
self.sov_tree = self.greens_tree_pas.__copy__(new_tree=SOVTree())
self.sov_tree.calcSOVEquations(maxspace_freq=50.)
z_inp = self.greens_tree_pas.calcZF((1, .5), (1, .5))[0]
alphas, gammas = self.sov_tree.getSOVMatrices(locarg=[(1., .5)])
# create NET
node_0 = NETNode(0, [0], [0], z_kernel=(alphas, gammas[:, 0]**2))
net_py = NET()
net_py.setRoot(node_0)
# check if correct
assert np.abs(gammas[0, 0]**2 / np.abs(alphas[0]) - z_inp) < 1e-10
assert np.abs(node_0.z_bar - z_inp) < 1e-10
# to initialize neuron tree
self.sim_tree.initModel(dt=self.dt)
# add ion channel to NET simulator
a_soma = 4. * np.pi * (self.sim_tree[1].R * 1e-4)**2
self.cnet = netsim.NETSim(net_py, v_eq=self.v_eq)
hchan = channelcollection.h()
self.cnet.addChannel(hchan, 0, gh * a_soma, eh)
# add the synapse
# to neuron tree
self.sim_tree.addDoubleExpSynapse((1, .5), .2, 3., 0.)
self.sim_tree.setSpikeTrain(0, 0.001, [5.])
# to net sim
self.cnet.addSynapse(0, {
'tau_r': .2,
'tau_d': 3.,
'e_r': 0.
},
g_max=0.001)
self.cnet.setSpikeTimes(0, [5. + self.dt])
def loadTree(self, reinitialize=0):
if not hasattr(self, 'net') or reinitialize:
alphas = np.array([1.]); gammas = np.array([1.])
# define nodes
node_r = NETNode(0, [0,1,2,3,4,5], [], z_kernel=(alphas, gammas))
node_s = NETNode(1, [0], [0], z_kernel=(alphas, gammas))
node_b1 = NETNode(2, [1,2,3,4,5], [1], z_kernel=(alphas, gammas))
node_b2 = NETNode(3, [2,3,4], [2], z_kernel=(alphas, gammas))
node_l3 = NETNode(4, [3], [3], z_kernel=(alphas, gammas))
node_l4 = NETNode(5, [4], [4], z_kernel=(alphas, gammas))
node_l5 = NETNode(6, [5], [5], z_kernel=(alphas, gammas))
# add nodes to tree
self.net = NET()
self.net.setRoot(node_r)
self.net.addNodeWithParent(node_s, node_r)
self.net.addNodeWithParent(node_b1, node_r)
self.net.addNodeWithParent(node_b2, node_b1)
self.net.addNodeWithParent(node_l3, node_b2)
self.net.addNodeWithParent(node_l4, node_b2)
self.net.addNodeWithParent(node_l5, node_b1)
def createTree(self, reinitialize=1, v_eq=-75.):
'''
Create simple NET structure
2 3
| |
| |
---1---
|
|
0
|
'''
self.v_eq = v_eq
loc_ind = np.array([0, 1, 2])
# import btstructs
# import morphologyReader as morphR
# net_py = btstructs.STree()
# # node 0
# alphas = -1. / np.array([.5, 8.])
# gammas = np.array([-1.,1.])
# self.z_0 = - np.sum(gammas / alphas)
# node_0 = btstructs.SNode(0)
# node_0.set_content({'layerdata': morphR.layerData(loc_ind, alphas, gammas)})
# dat = node_0.get_content()['layerdata']
# dat.set_ninds(np.array([]))
# net_py.set_root(node_0)
# # node 1
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# self.z_1 = - np.sum(gammas / alphas)
# node_1 = btstructs.SNode(1)
# node_1.set_content({'layerdata': morphR.layerData(loc_ind, alphas, gammas)})
# dat = node_1.get_content()['layerdata']
# dat.set_ninds(np.array([0]))
# net_py.add_node_with_parent(node_1, node_0)
# # node 2
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# self.z_2 = - np.sum(gammas / alphas)
# node_2 = btstructs.SNode(2)
# node_2.set_content({'layerdata': morphR.layerData(loc_ind[1:2], alphas, gammas)})
# dat = node_2.get_content()['layerdata']
# dat.set_ninds(np.array([loc_ind[1]]))
# net_py.add_node_with_parent(node_2, node_1)
# # node 3
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# node_3 = btstructs.SNode(3)
# self.z_3 = - np.sum(gammas / alphas)
# node_3.set_content({'layerdata': morphR.layerData(loc_ind[2:], alphas, gammas)})
# dat = node_3.get_content()['layerdata']
# dat.set_ninds(np.array([loc_ind[2]]))
# net_py.add_node_with_parent(node_3, node_1)
# kernel constants
alphas = 1. / np.array([.5, 8.])
gammas = np.array([-1., 1.])
alphas_ = 1. / np.array([1.])
gammas_ = np.array([1.])
# nodes
node_0 = NETNode(0, [0, 1, 2], [], z_kernel=(alphas, gammas))
node_1 = NETNode(1, [0, 1, 2], [0], z_kernel=(alphas_, gammas_))
node_2 = NETNode(2, [1], [1], z_kernel=(alphas_, gammas_))
node_3 = NETNode(3, [2], [2], z_kernel=(alphas_, gammas_))
# add nodes to tree
net_py = NET()
net_py.setRoot(node_0)
net_py.addNodeWithParent(node_1, node_0)
net_py.addNodeWithParent(node_2, node_1)
net_py.addNodeWithParent(node_3, node_1)
# store
self.net_py = net_py
self.cnet = netsim.NETSim(net_py, v_eq=self.v_eq)
def createTree2(self, reinitialize=1, add_lin=True, v_eq=-75.):
'''
Create simple NET structure
3 4
| |
| |
---2---
1 |
| |
---0---
|
'''
self.v_eq = v_eq
loc_ind = np.array([0, 1, 2])
# import btstructs
# import morphologyReader as morphR
# net_py = btstructs.STree()
# # node 0
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# self.z_0 = - np.sum(gammas / alphas)
# node_0 = btstructs.SNode(0)
# node_0.set_content({'layerdata': morphR.layerData(loc_ind, alphas, gammas)})
# dat = node_0.get_content()['layerdata']
# dat.set_ninds(np.array([]))
# net_py.set_root(node_0)
# # node 1
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# self.z_1 = - np.sum(gammas / alphas)
# node_1 = btstructs.SNode(1)
# node_1.set_content({'layerdata': morphR.layerData(loc_ind[0:1], alphas, gammas)})
# dat = node_1.get_content()['layerdata']
# dat.set_ninds(np.array([0]))
# net_py.add_node_with_parent(node_1, node_0)
# # node 2
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# self.z_2 = - np.sum(gammas / alphas)
# node_2 = btstructs.SNode(2)
# node_2.set_content({'layerdata': morphR.layerData(loc_ind[1:], alphas, gammas)})
# dat = node_2.get_content()['layerdata']
# dat.set_ninds(np.array([]))
# net_py.add_node_with_parent(node_2, node_0)
# # node 3
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# self.z_3 = - np.sum(gammas / alphas)
# node_3 = btstructs.SNode(3)
# node_3.set_content({'layerdata': morphR.layerData(loc_ind[1:2], alphas, gammas)})
# dat = node_3.get_content()['layerdata']
# dat.set_ninds(np.array([loc_ind[1]]))
# net_py.add_node_with_parent(node_3, node_2)
# # node 4
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# node_4 = btstructs.SNode(4)
# self.z_4 = - np.sum(gammas / alphas)
# node_4.set_content({'layerdata': morphR.layerData(loc_ind[2:], alphas, gammas)})
# dat = node_4.get_content()['layerdata']
# dat.set_ninds(np.array([loc_ind[2]]))
# net_py.add_node_with_parent(node_4, node_2)
# # linear terms
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# lin3 = morphR.linearLayerData([1], alphas, gammas)
# alphas = -1. / np.array([1.])
# gammas = np.array([1.])
# lin4 = morphR.linearLayerData([2], alphas, gammas)
# self.lin_terms = [lin3, lin4] if add_lin else []
# kernel constants
alphas = 1. / np.array([1.])
gammas = np.array([1.])
# nodes
node_0 = NETNode(0, [0, 1, 2], [], z_kernel=(alphas, gammas))
node_1 = NETNode(1, [0], [0], z_kernel=(alphas, gammas))
node_2 = NETNode(2, [1, 2], [], z_kernel=(alphas, gammas))
node_3 = NETNode(3, [1], [1], z_kernel=(alphas, gammas))
node_4 = NETNode(4, [2], [2], z_kernel=(alphas, gammas))
# add nodes to tree
net_py = NET()
net_py.setRoot(node_0)
net_py.addNodeWithParent(node_1, node_0)
net_py.addNodeWithParent(node_2, node_0)
net_py.addNodeWithParent(node_3, node_2)
net_py.addNodeWithParent(node_4, node_2)
# linear terms
alphas = 1. / np.array([1.])
gammas = np.array([1.])
self.lin_terms = {
1: Kernel((alphas, gammas)),
2: Kernel((alphas, gammas))
} if add_lin else {}
# store
self.net_py = net_py
self.cnet = netsim.NETSim(net_py,
lin_terms=self.lin_terms,
v_eq=self.v_eq)