def setUpClass(cls):
"""
Define and compile the network for this test.
"""
input_neuron = Neuron(parameters="r=0.0")
output_neuron = Neuron(equations="""
r = sum(p1) + sum(p2)
""")
syn_max = Synapse(psp="pre.r * w", operation="max")
syn_min = Synapse(psp="pre.r * w", operation="min")
syn_mean = Synapse(psp="pre.r * w", operation="mean")
pop1 = Population((3, 3), neuron=input_neuron)
pop2 = Population(4, neuron=output_neuron)
proj1 = Projection(pop1, pop2, target="p1", synapse=syn_max)
proj1.connect_all_to_all(weights=1.0)
proj2 = Projection(pop1, pop2, target="p2", synapse=syn_min)
proj2.connect_all_to_all(weights=1.0)
proj3 = Projection(pop1, pop2, target="p3", synapse=syn_mean)
proj3.connect_all_to_all(weights=1.0)
cls.test_net = Network()
cls.test_net.add([pop1, pop2, proj1, proj2, proj3])
cls.test_net.compile(silent=True)
cls.net_pop1 = cls.test_net.get(pop1)
cls.net_pop2 = cls.test_net.get(pop2)
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(
equations = "r = transfer_function(sum(exc), 0.0)",
functions = "transfer_function(x, t) = if x > t: if x > 2*t : (x - 2*t)^2 else: x - t else: 0."
)
neuron2 = Neuron(
equations = "r = glob_pos(sum(exc))"
)
synapse = Synapse(
equations="w += hebb(pre.r, post.r)",
functions="hebb(x, y) = x * y"
)
pop = Population(10, neuron)
pop2 = Population(10, neuron2)
proj = Projection(pop, pop, 'exc', synapse).connect_all_to_all(1.0)
self.test_net = Network()
self.test_net.add([pop, pop2, proj])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(pop)
self.net_proj = self.test_net.get(proj)
def setUpClass(cls):
"""
Compile the network for this test.
The input_neuron will generate a sequence of values:
r_t = [-1, 0, 2, 5, 9, 14, 20, ...]
"""
input_neuron = Neuron(equations="""
r = r + t : init = -1
""")
neuron2 = Neuron(equations="""
r = sum(ff)
""")
pop1 = Population((3), input_neuron)
pop2 = Population((3), neuron2)
# A projection with non-uniform delay
proj = Projection(pop1, pop2, target="ff")
proj.connect_one_to_one(weights=1.0, delays=Uniform(1, 5))
# Build up network
cls.test_net = Network()
cls.test_net.add([pop1, pop2, proj])
cls.test_net.compile(silent=True)
# Store references for easier usage in test cases
cls.net_proj = cls.test_net.get(proj)
cls.net_pop1 = cls.test_net.get(pop1)
cls.net_pop2 = cls.test_net.get(pop2)
def setUpClass(cls):
"""
Compile the network for this test
"""
neuron = Neuron(equations="r = 1")
neuron2 = Neuron(equations="r = sum(exc)")
pop1 = Population((3, 3), neuron)
pop2 = Population((3, 3), neuron2)
proj1 = Projection(pre=pop1, post=pop2, target="exc")
proj2 = Projection(pre=pop1, post=pop2, target="exc")
proj3 = Projection(pre=pop1, post=pop2, target="exc")
proj1.connect_one_to_one(weights=0.1)
proj2.connect_all_to_all(weights=0.1)
proj3.connect_fixed_number_pre(3, weights=0.1)
cls.test_net = Network()
cls.test_net.add([pop1, pop2, proj1, proj2, proj3])
cls.test_net.compile(silent=True)
cls.test_proj1 = cls.test_net.get(proj1)
cls.test_proj2 = cls.test_net.get(proj2)
cls.test_proj3 = cls.test_net.get(proj3)
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="tau = 10", equations="r += 1/tau * t")
neuron2 = Neuron(parameters="tau = 10: population",
equations="r += 1/tau * t: init = 1.0")
Oja = Synapse(parameters="""
tau = 5000.0 : postsynaptic
alpha = 8.0
""",
equations="""
tau * dw/dt = pre.r * post.r - alpha * post.r^2 * w
""")
pop1 = Population(5, neuron)
pop2 = Population(8, neuron2)
proj = Projection(pre=pop1, post=pop2, target="exc", synapse=Oja)
proj.connect_all_to_all(weights=1.0)
self.test_net = Network()
self.test_net.add([pop1, pop2, proj])
self.test_net.compile(silent=True)
self.net_proj = self.test_net.get(proj)
def setUpClass(cls):
"""
Compile the network for this test
"""
# neuron defintions common used for test cases
local_eq = Neuron(
equations="""
noise = Uniform(0,1)
r = t
"""
)
global_eq = Neuron(
equations="""
noise = Uniform(0,1) : population
glob_r = t : population
r = t
"""
)
mixed_eq = Neuron(
parameters="glob_par = 1.0: population",
equations="""
r = t + glob_par
"""
)
bound_eq = Neuron(
parameters="""
min_r=1.0: population
max_r=3.0: population
""",
equations="""
r = t : min=min_r, max=max_r
"""
)
tc_loc_up_pop = Population(3, local_eq)
tc_glob_up_pop = Population(3, global_eq)
tc_mixed_up_pop = Population(3, mixed_eq)
tc_bound_up_pop = Population(3, bound_eq)
m = Monitor(tc_bound_up_pop, 'r')
cls.test_net = Network()
cls.test_net.add([tc_loc_up_pop, tc_glob_up_pop,
tc_mixed_up_pop, tc_bound_up_pop, m])
cls.test_net.compile(silent=True)
cls.net_loc_pop = cls.test_net.get(tc_loc_up_pop)
cls.net_glob_pop = cls.test_net.get(tc_glob_up_pop)
cls.net_mix_pop = cls.test_net.get(tc_mixed_up_pop)
cls.net_bound_pop = cls.test_net.get(tc_bound_up_pop)
cls.net_m = cls.test_net.get(m)
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="""
r=0
""")
cov = Synapse(parameters="""
tau = 5000.0
""",
equations="""
tau * dw/dt = (pre.r - mean(pre.r) ) * (post.r - mean(post.r) )
""")
pre = Population(6, neuron)
post = Population(1, neuron)
proj = Projection(pre, post, "exc",
synapse=cov).connect_all_to_all(weights=1.0)
self.test_net = Network()
self.test_net.add([pre, post, proj])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(post)
def setUpClass(cls):
"""
Compile the network for this test
"""
simple_neuron = Neuron(
parameters="r=1.0"
)
eq_set = Synapse(
equations="""
glob_var = 0.1 : projection
semi_glob_var = 0.2 : postsynaptic
w = t + glob_var + semi_glob_var
"""
)
pop0 = Population(3, simple_neuron)
pop1 = Population(1, simple_neuron)
proj = Projection(pop0, pop1, "exc", eq_set)
proj.connect_all_to_all(weights=0.0)
cls.test_net = Network()
cls.test_net.add([pop0, pop1, proj])
cls.test_net.compile(silent=True)
def setUpClass(cls):
"""
Compile the network for this test.
The input_neuron will generate a sequence of values:
r_t = [-1, 0, 2, 5, 9, 14, 20, ...]
one time as global (glob_r) and one time as local variable (r).
"""
input_neuron = Neuron(equations="""
glob_r = glob_r + t : init = -1, population
r = r + t : init = -1
""")
neuron2 = Neuron(equations="""
r = sum(ff)
""")
synapse_glob = Synapse(psp="pre.glob_r * w")
pop1 = Population((3), input_neuron)
pop2 = Population((3), neuron2)
# A projection with uniform delay
proj = Projection(pre=pop1, post=pop2, target="ff")
proj.connect_one_to_one(weights=1.0, delays=10.0)
# A projection with uniform delay
proj2 = Projection(pre=pop1,
post=pop2,
target="ff_glob",
synapse=synapse_glob)
proj2.connect_one_to_one(weights=1.0, delays=10.0)
# Build up network
cls.test_net = Network()
cls.test_net.add([pop1, pop2, proj, proj2])
cls.test_net.compile(silent=True)
# Store references for easier usage in test cases
cls.net_proj = cls.test_net.get(proj)
cls.net_proj2 = cls.test_net.get(proj2)
cls.net_pop1 = cls.test_net.get(pop1)
cls.net_pop2 = cls.test_net.get(pop2)
def setUpClass(self):
"""
Compile the network for this test. Adapted the example
from documentation.
"""
SimpleSpike = Neuron(equations="mp=g_exc", spike="mp >= 1.0", reset="")
inp = Population(1, neuron=Neuron(equations="r=sin(t)"))
out = Population(1, neuron=SimpleSpike)
m = Monitor(out, "mp")
proj = CurrentInjection(inp, out, 'exc')
proj.connect_current()
self.test_net = Network()
self.test_net.add([inp, out, proj, m])
self.test_net.compile(silent=True)
self.output = self.test_net.get(out)
self.m = self.test_net.get(m)
def setUpClass(cls):
"""
Build up the network
"""
simple_emit = Neuron(spike="t==1", )
simple_recv = Neuron(equations="""
g_exc1 = 0
g_exc2 = 0
g_exc3 = 0
""",
spike="g_exc1>30")
# simple in/out populations
in_pop = Population(5, neuron=simple_emit)
out_pop = Population(2, neuron=simple_recv)
# create the projections for the test cases (TC)
# TC: no delay
proj = Projection(pre=in_pop, post=out_pop, target="exc1")
proj.connect_all_to_all(weights=1.0, storage_format="csr")
# TC: uniform delay
proj_u = Projection(pre=in_pop, post=out_pop, target="exc2")
proj_u.connect_all_to_all(weights=1.0,
delays=2.0,
storage_format="csr")
# TC: non-uniform delay
proj_nu = Projection(pre=in_pop, post=out_pop, target="exc3")
proj_nu.connect_all_to_all(weights=1.0, delays=Uniform(2, 10))
# Monitor to record the currents
m = Monitor(out_pop, ["g_exc1", "g_exc2", "g_exc3"])
# build network and store required object
# instances
net = Network()
net.add([in_pop, out_pop, proj, proj_u, proj_nu, m])
cls.test_net = net
cls.test_net.compile(silent=True)
cls.test_g_exc_m = net.get(m)
cls.test_proj = net.get(proj_nu)
def setUpClass(cls):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="r=0.0")
out1 = Neuron(equations="""
r = sum(one2one)
""")
out2 = Neuron(equations="""
r = sum(all2all) + sum(fnp)
""")
pop1 = Population((17, 17), neuron)
pop2 = Population((17, 17), out1)
pop3 = Population(4, out2)
proj = Projection(pre=pop1, post=pop2, target="one2one")
proj.connect_one_to_one(
weights=0.0,
force_multiple_weights=True) # weights set in the test
proj2 = Projection(pre=pop1, post=pop3, target="all2all")
proj2.connect_all_to_all(weights=Uniform(0, 1))
proj3 = Projection(pre=pop1, post=pop3, target="fnp")
proj3.connect_fixed_number_pre(5, weights=Uniform(0, 1))
cls.test_net = Network()
cls.test_net.add([pop1, pop2, pop3, proj, proj2, proj3])
cls.test_net.compile(silent=True)
cls.net_pop1 = cls.test_net.get(pop1)
cls.net_pop2 = cls.test_net.get(pop2)
cls.net_pop3 = cls.test_net.get(pop3)
cls.net_proj = cls.test_net.get(proj)
cls.net_proj2 = cls.test_net.get(proj2)
cls.net_proj3 = cls.test_net.get(proj3)
def setUpClass(self):
"""
Compile the network for this test
"""
neuron = Neuron(parameters="""
r=0
""",
equations="""
mean_r = mean(r)
max_r = max(r)
min_r = min(r)
l1 = norm1(r)
l2 = norm2(r)
""")
pop = Population(6, neuron)
self.test_net = Network()
self.test_net.add([pop])
self.test_net.compile(silent=True)
self.net_pop = self.test_net.get(pop)
def setUpClass(self):
"""
Compile the network for this test
"""
BuiltinFuncs = Neuron(parameters="""
base = 2.0
""",
equations="""
r = modulo(t,3)
pr = power(base,3)
clip_below = clip(-2, -1, 1)
clip_within = clip(0, -1, 1)
clip_above = clip(2, -1, 1)
""")
pop1 = Population(1, BuiltinFuncs)
mon = Monitor(pop1,
['r', 'pr', 'clip_below', 'clip_within', 'clip_above'])
self.test_net = Network()
self.test_net.add([pop1, mon])
self.test_net.compile(silent=True)
self.test_mon = self.test_net.get(mon)
J = 15.0 * np.sqrt(D)
tau = 10.0
tau_syn = 0.1
v_th = 100.0
T = 50.0
dt = 0.001
setup(method='explicit', dt=dt)
# theta neuron definition
Theta = Neuron(parameters=f"""
tau = {tau} : population
eta = {eta}
J = {J} : population
tau_s = {tau_syn} : population
""",
equations="""
v_old = v_new
tau * dv/dt = 1.0 - cos(v_old) + (1.0 + cos(v_old))*(eta + J*g_syn*tau) : init=6.2832, min=0.0
tau_s * dg_syn/dt = g_exc*tau_s - g_syn
v_tmp = v/(2*pi) : int
v_new = (v/(2*pi) - v_tmp)*2*pi
""",
spike="(v_new > pi)*(v_old < pi)")
# population setup
pop1 = Population(N, neuron=Theta, name="ThetaPop1")
pop1.eta = eta + D * np.tan(
(np.pi / 2) * (2 * np.arange(1, N + 1) - N - 1) / (N + 1))
# projection setup
proj = Projection(pre=pop1, post=pop1, target='exc', name='fb')
proj.connect_all_to_all(100.0 / N, allow_self_connections=False)
np.random.seed()
setup(num_threads=params['num_threads'])
#Model parameters
baseline_dopa = Constant('baseline_dopa', params['baseline_dopa'])
reversal = Constant('reversal', changed['reversal_SNr'])
#####################################################
########## Neuron models #########################
#####################################################
LinearNeuron = Neuron(
parameters="""
tau = 10.0 : population
phi = 0.0 : population
B = 0.0
""",
equations="""
tau * dmp/dt = -mp + sum(exc) - sum(inh) + B + phi * Uniform(-1.0,1.0)
r = pos(mp)
""",
name="Linear Neuron",
description=
"Regular rate-coded neuron with excitatory and inhibitory inputs plus baseline and noise."
)
DopamineNeuron = Neuron(
parameters="""
tau = 10.0 : population
alpha = 0 : population
B = 0.0
""",
equations="""
aux = if (sum(exc)>0): pos(1.0-B-sum(inh)) else: -10 * sum(inh)
# -*- coding: utf-8 -*-
from ANNarchy import Neuron
# The following devices current (rate) injection to spiking population
# follow the examples on Hybrid networks: https://annarchy.readthedocs.io/en/latest/manual/Hybrid.html
CurrentInjector = Neuron(equations="""
r = amplitude
""",
parameters="""
amplitude = 0.0
""")
DCCurrentInjector = CurrentInjector
ACCurrentInjector = Neuron(equations="""
r = amplitude * sin(omega*t + phase) + offset
""",
parameters="""
omega = 0.0
amplitude = 1.0
phase = 0.0
offset = 0.0
""")
from ANNarchy import Neuron, Population, Projection, setup, Synapse, Uniform, Constant
from ANNarchy.extensions.convolution import Pooling, Convolution
from parameters import params
from functions import rangeX, Gaussian2D, positive
from Connections import con_scale
from changed_val import changed
setup(num_threads=params['num_threads'])
minVis = Constant('minVis', 0)
##########################################
########## NEURON DEFINITION ##########
##########################################
## Input Neuron: Has to be set to value. Does not change over time.
Inp_Neuron = Neuron(parameters="r = 0.0")
## Basic Auxillary Neuron is transmitting an unmodified input
Aux_Neuron = Neuron(equations="""r = sum(exc)""")
## Neuron of V1 population: Applies the power rule to the given baseline input
# See Eq 4.29
V1_Neuron = Neuron(parameters="""
pV1C = 'pV1C' : population
tau_up = 1.0 : population
tau_down = 20.0 : population
baseline = 0.0
noise = 'noise_V1' : population
frequency = 1/15. : population
""",
equations="""
Izhikevich_Hamker = Neuron(parameters="""
a = 0.02
b = 0.2
c = -72.0
d = 6.0
n0 = 140.
n1 = 5.0
n2 = 0.04
I = 0.0
tau_refrac = 10.0
tau_ampa = 10.0
tau_gaba = 10.0
E_ampa = 0.0
E_gaba = -90.0
tau_syn = 1.0
C = 1.0
v_th = 30.0
""",
equations="""
I_syn_ex = - g_ampa*(v-E_ampa)
I_syn_in = - g_gaba*(v-E_gaba)
I_syn = I_syn_ex + I_syn_in - g_base*v
dg_base/dt = -g_base/tau_syn : init = 0
dg_ampa/dt = -g_ampa/tau_ampa : init = 0
dg_gaba/dt = -g_gaba/tau_gaba : init = 0
dv/dt = n2*v*v+n1*v+n0 - u/C + I + I_syn : init = -72.0
du/dt = a*(b*(v)-u) : init = -14.4
""",
spike="""
v>=v_th
""",
reset="""
v = c
u = u+d
""",
refractory="""tau_refrac""")
from ANNarchy import Neuron, Population, Projection, setup, Synapse, Uniform, Constant
from ANNarchy.extensions.convolution import Pooling, Convolution
from parameters import params
from functions import rangeX, Gaussian2D, positive
from Connections import con_scale
from changed_val import changed
setup(num_threads=params['num_threads'])
minVis = Constant('minVis', 0)
##########################################
########## NEURON DEFINITION ##########
##########################################
## Input Neuron: Has to be set to value. Does not change over time.
FEFFix_Neuron = Neuron(parameters="r = 0.0",
name="FEFfix Neuron",
description="Input neuron, rate has to be specified.")
## Basic Auxillary Neuron is transmitting an unmodified input
Aux_Neuron = Neuron(
equations="""r = sum(exc)""",
name="Auxillary Neuron",
description="Basic auxillary neuron is transmitting an unmodified input.")
## Neuron of V1 population: Applies the power rule to the given baseline input
# See Eq 4.29
V1_Neuron = Neuron(
parameters="""
pV1C = 'pV1C' : population
tau_up = 1.0 : population
tau_down = 20.0 : population
def setUpClass(cls):
"""
Compile the network for this test
"""
def my_diagonal(pre, post, weight):
synapses = CSR()
for post_rk in post.ranks:
pre_ranks = []
delays = []
if post_rk - 1 in pre.ranks:
pre_ranks.append(post_rk - 1)
if post_rk in pre.ranks:
pre_ranks.append(post_rk)
if post_rk + 1 in pre.ranks:
pre_ranks.append(post_rk + 1)
synapses.add(post_rk, pre_ranks, [weight] * len(pre_ranks),
[0] * len(pre_ranks))
return synapses
def my_diagonal_with_uniform_delay(pre, post, weight, delay):
synapses = CSR()
for post_rk in post.ranks:
pre_ranks = []
delays = []
if post_rk - 1 in pre.ranks:
pre_ranks.append(post_rk - 1)
if post_rk in pre.ranks:
pre_ranks.append(post_rk)
if post_rk + 1 in pre.ranks:
pre_ranks.append(post_rk + 1)
synapses.add(post_rk, pre_ranks, [weight] * len(pre_ranks),
[delay] * len(pre_ranks))
return synapses
def my_diagonal_with_non_uniform_delay(pre, post, weight, delay):
synapses = CSR()
for post_rk in post.ranks:
pre_ranks = []
delays = []
if post_rk - 1 in pre.ranks:
pre_ranks.append(post_rk - 1)
if post_rk in pre.ranks:
pre_ranks.append(post_rk)
if post_rk + 1 in pre.ranks:
pre_ranks.append(post_rk + 1)
synapses.add(post_rk, pre_ranks, [weight] * len(pre_ranks),
delay.get_values(len(pre_ranks)))
return synapses
neuron = Neuron(equations="r = 1")
neuron2 = Neuron(equations="r = sum(exc)")
pop1 = Population(5, neuron)
pop2 = Population(5, neuron2)
proj1 = Projection(pre=pop1, post=pop2, target="exc")
proj1.connect_with_func(method=my_diagonal, weight=0.1)
proj2 = Projection(pre=pop1, post=pop2, target="exc2")
proj2.connect_with_func(method=my_diagonal_with_uniform_delay,
weight=0.1,
delay=2)
proj3 = Projection(pre=pop1, post=pop2, target="exc3")
proj3.connect_with_func(method=my_diagonal_with_non_uniform_delay,
weight=0.1,
delay=DiscreteUniform(1, 5))
cls.test_net = Network()
cls.test_net.add([pop1, pop2, proj1, proj2, proj3])
cls.test_net.compile(silent=True)
cls.test_proj1 = cls.test_net.get(proj1)
cls.test_proj2 = cls.test_net.get(proj2)
cls.test_proj3 = cls.test_net.get(proj3)
from changed_val import changed
import numpy as np
np.random.seed()
setup(num_threads=params['num_threads'])
#Model parameters
baseline_dopa = Constant('baseline_dopa', params['baseline_dopa'])
reversal = Constant('reversal', changed['reversal_SNr'])
#####################################################
########## Neuron models #########################
#####################################################
LinearNeuron = Neuron(parameters="""
tau = 10.0 : population
noise = 0.0 : population
baseline = 0.0
""",
equations="""
tau * dmp/dt + mp = sum(exc) - sum(inh) + baseline + noise * Uniform(-1.0,1.0)
r = pos(mp) : max=0.15
""")
DopamineNeuron = Neuron(parameters="""
tau = 10.0 : population
firing = 0 : population
baseline = 0.0
""",
equations="""
test=sum(inh)
aux = if (sum(exc)>0): pos(1.0-baseline-sum(inh)) else: -10 * sum(inh)
tau * dmp/dt + mp = firing * aux + baseline
r = pos(mp)