def test_model_definition():
''' Tests various ways of defining models.
Note that this test only checks for whether the interface accepts what it
is supposed to accept, not whether the Synapses class actually does what
it is supposed to do... '''
G = NeuronGroup(1, model='v:1', threshold=NoThreshold())
# model, pre and post string
syn = Synapses(G, model='w:1', pre='v+=w', post='v+=w')
# list of pre codes
syn = Synapses(G, model='w:1', pre=['v+=w', 'v+=1'])
# an equation object
model = SynapticEquations('w:1')
syn = Synapses(G, model=model, pre='v+=w')
############################################################################
# Some more complex examples from the docs
############################################################################
# event-driven simulations
model='''w:1
dApre/dt=-Apre/taupre : 1 (event-driven)
dApost/dt=-Apost/taupost : 1 (event-driven)'''
syn = Synapses(G, model=model)
model ='''w : 1
p : 1'''
syn = Synapses(G, model=model, pre="v+=w*(rand()<p)")
syn = Synapses(G,
model='''x : 1
u : 1
w : 1''',
pre='''u=U+(u-U)*exp(-(t-lastupdate)/tauf)
x=1+(x-1)*exp(-(t-lastupdate)/taud)
i+=w*u*x
x*=(1-u)
u+=U*(1-u)''')
# lumped variables
a = 1 / (10 * ms)
b = 1 / (20 * ms)
c = 1 / (30 * ms)
neurons = NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1
gtot : 1""")
S=Synapses(neurons,
model='''dg/dt=-a*g+b*x*(1-g) : 1
dx/dt=-c*x : 1
w : 1 # synaptic weight
''',
pre='x+=w')
neurons.gtot = S.g
v0 = 0
tau = 5 * ms
neurons = NeuronGroup(1, model='''dv/dt=(v0-v+Igap)/tau : 1
Igap : 1''')
S=Synapses(neurons, model='''w:1 # gap junction conductance
Igap=w*(v_pre-v_post): 1''')
neurons.Igap = S.Igap
# static equations in synaptic model
V_L = -70 * mV
C_m = 0.5 * nF
g_m = 25 * nS
V_E1 = 0 * mV
V_E2 = -20 * mV
g_1 = 0.1 * nS
g_2 = 0.2 * nS
tau1 = 5 * ms
tau2 = 50 * ms
neurons = NeuronGroup(1, model='''dV/dt = (-1/C_m)*((g_m)*(V-V_L) + I_tot) : volt
I_tot : amp''', reset=-55*mV, threshold=-50*mV)
S=Synapses(neurons, model='''I_tot = I_1 + I_2 : amp
I_1 = sI_1*g_1*(V_post-V_E1) : amp
dsI_1/dt = -sI_1 / tau1 : 1
I_2 = sI_2*g_2*(V_post-V_E2) : amp
dsI_2/dt = -sI_2 / tau1 : 1
''', pre='sI_1 += 1; sI_2 += 1')
S[:, :] = True
neurons.I_tot = S.I_tot
def test_model_definition():
''' Tests various ways of defining models.
Note that this test only checks for whether the interface accepts what it
is supposed to accept, not whether the Synapses class actually does what
it is supposed to do... '''
G = NeuronGroup(1, model='v:1', threshold=NoThreshold())
# model, pre and post string
syn = Synapses(G, model='w:1', pre='v+=w', post='v+=w')
# list of pre codes
syn = Synapses(G, model='w:1', pre=['v+=w', 'v+=1'])
# an equation object
model = SynapticEquations('w:1')
syn = Synapses(G, model=model, pre='v+=w')
############################################################################
# Some more complex examples from the docs
############################################################################
# event-driven simulations
model = '''w:1
dApre/dt=-Apre/taupre : 1 (event-driven)
dApost/dt=-Apost/taupost : 1 (event-driven)'''
syn = Synapses(G, model=model)
model = '''w : 1
p : 1'''
syn = Synapses(G, model=model, pre="v+=w*(rand()<p)")
syn = Synapses(G,
model='''x : 1
u : 1
w : 1''',
pre='''u=U+(u-U)*exp(-(t-lastupdate)/tauf)
x=1+(x-1)*exp(-(t-lastupdate)/taud)
i+=w*u*x
x*=(1-u)
u+=U*(1-u)''')
# lumped variables
a = 1 / (10 * ms)
b = 1 / (20 * ms)
c = 1 / (30 * ms)
neurons = NeuronGroup(1,
model="""dv/dt=(gtot-v)/(10*ms) : 1
gtot : 1""")
S = Synapses(neurons,
model='''dg/dt=-a*g+b*x*(1-g) : 1
dx/dt=-c*x : 1
w : 1 # synaptic weight
''',
pre='x+=w')
neurons.gtot = S.g
v0 = 0
tau = 5 * ms
neurons = NeuronGroup(1,
model='''dv/dt=(v0-v+Igap)/tau : 1
Igap : 1''')
S = Synapses(neurons,
model='''w:1 # gap junction conductance
Igap=w*(v_pre-v_post): 1''')
neurons.Igap = S.Igap
# static equations in synaptic model
V_L = -70 * mV
C_m = 0.5 * nF
g_m = 25 * nS
V_E1 = 0 * mV
V_E2 = -20 * mV
g_1 = 0.1 * nS
g_2 = 0.2 * nS
tau1 = 5 * ms
tau2 = 50 * ms
neurons = NeuronGroup(
1,
model='''dV/dt = (-1/C_m)*((g_m)*(V-V_L) + I_tot) : volt
I_tot : amp''',
reset=-55 * mV,
threshold=-50 * mV)
S = Synapses(neurons,
model='''I_tot = I_1 + I_2 : amp
I_1 = sI_1*g_1*(V_post-V_E1) : amp
dsI_1/dt = -sI_1 / tau1 : 1
I_2 = sI_2*g_2*(V_post-V_E2) : amp
dsI_2/dt = -sI_2 / tau1 : 1
''',
pre='sI_1 += 1; sI_2 += 1')
S[:, :] = True
neurons.I_tot = S.I_tot
