def test_recording():
""" tests if the shape of recording is correct """
import numpy as np
from rockpool import timeseries as ts
from rockpool.layers import FFIAFNest
# - Generic parameters
weights = np.array([[-0.1, 0.02, 0.4], [0.2, -0.3, -0.15]])
bias = 0.01
tau_mem = [0.02, 0.05, 0.1]
# - Layer generation
fl0 = FFIAFNest(
weights=weights,
dt=0.0001,
bias=bias,
tau_mem=tau_mem,
refractory=0.001,
record=True,
)
# - Input signal
tsInCont = ts.TSContinuous(times=np.arange(15) * 0.01, samples=np.ones((15, 2)))
# - Compare states before and after
vStateBefore = np.copy(fl0.state)
dFl0 = fl0.evolve(tsInCont, duration=0.1)
assert np.shape(fl0.recorded_states.samples) == (1000, 3)
fl0.terminate()
def test_FFNestLayer():
""" Test FFIAFNest"""
from rockpool import timeseries as ts
from rockpool.layers import FFIAFNest
# - Generic parameters
weights = np.array([[-0.1, 0.02, 0.4], [0.2, -0.3, -0.15]])
bias = 0.01
tau_mem = [0.02, 0.05, 0.1]
# - Layer generation
fl0 = FFIAFNest(
weights=weights,
dt=0.001,
bias=bias,
tau_mem=tau_mem,
refractory=0.001,
record=True,
)
# - Input signal
tsInCont = ts.TSContinuous(times=np.arange(15) * 0.01, samples=np.ones((15, 2)))
# - Compare states before and after
vStateBefore = np.copy(fl0.state)
dFl0 = fl0.evolve(tsInCont, duration=0.1)
dFl0 = fl0.evolve(tsInCont, duration=0.1)
assert fl0.t == 0.2
assert (vStateBefore != fl0.state).any()
fl0.reset_all()
assert fl0.t == 0
assert (vStateBefore == fl0.state).all()
fl0.terminate()
def test_chargeAndSpikeSingleNeuron():
"""
single neuron test
charge neuron exactly to threshold without crossing using the bias
add small input to make it spike
"""
from rockpool import timeseries as ts
from rockpool.layers import FFIAFNest
weights = [[1.0]]
bias = [0.375]
tau_mem = [0.01]
vReset = -0.07
vRest = -0.07
vTh = -0.055
fC = 0.25
dt = 0.001
refractory = 0.002
fl0 = FFIAFNest(
weights=weights,
dt=dt,
bias=bias,
tau_mem=tau_mem,
v_reset=vReset,
v_rest=vRest,
v_thresh=vTh,
capacity=fC,
refractory=refractory,
record=True,
name="test",
)
# - Input signal
vTime = np.arange(0, 1, dt)
vVal = np.zeros([len(vTime), 1])
vVal[500] = 0.001
tsInCont = ts.TSContinuous(vTime, vVal)
dFl0 = fl0.evolve(tsInCont, duration=1.0)
assert not dFl0.isempty()
fl0.terminate()
def test_chargeSingleNeuron():
"""
single neuron test
charge neuron exactly to threshold without crossing using the bias
"""
from rockpool.layers import FFIAFNest
weights = [[1.0]]
epsilon = 1e-5
bias = [0.375 - epsilon]
tau_mem = [0.01]
vReset = -0.07
vRest = -0.07
vTh = -0.055
fC = 0.25
dt = 0.001
refractory = 0.002
fl0 = FFIAFNest(
weights=weights,
dt=dt,
bias=bias,
tau_mem=tau_mem,
v_reset=vReset,
v_rest=vRest,
v_thresh=vTh,
capacity=fC,
refractory=refractory,
record=True,
name="test",
)
dFl0 = fl0.evolve(duration=1.0)
assert fl0.state[0] > vTh - 0.00001
assert fl0.state[0] <= vTh
assert dFl0.isempty()
fl0.terminate()
def test_setWeightsIn():
""" Test weight setting"""
from rockpool.layers import FFIAFNest, RecIAFSpkInNest, RecAEIFSpkInNest
from rockpool import TSEvent, TSContinuous
import numpy as np
# - Generic parameters
weights_in = np.array([[-0.5, 0.02, 0.4], [0.2, -0.3, -0.15]])
weights_rec = np.random.rand(3, 3) * 0.01
bias = 0.01
tau_mem = [0.02, 0.05, 0.1]
tau_syn_exc = [0.2, 0.01, 0.01]
tau_syn_inh = tau_syn_exc
# - Different input weights for initialization of fl1
weights_in1 = np.array([[-0.1, 0.02, 0.4], [0.2, -0.3, -0.15]])
# - Input time series
tsInpCont = TSContinuous(np.arange(15) * 0.01, np.ones(15) * 0.1)
tsInp = TSEvent([0.1], [0])
## -- FFIAFNEst
# - Layer generation
fl0 = FFIAFNest(
weights=weights_in, dt=0.001, bias=bias, tau_mem=tau_mem, refractory=0.001
)
fl1 = FFIAFNest(
weights=weights_in1, dt=0.001, bias=bias, tau_mem=tau_mem, refractory=0.001
)
# - Set input weights to same as fl0
fl1.weights = weights_in
assert (fl1.weights_ == weights_in).all()
# - Compare states before and after
fl0.evolve(tsInpCont, duration=0.12)
fl1.evolve(tsInpCont, duration=0.12)
assert (fl0.state == fl1.state).all()
fl0.terminate()
fl1.terminate()
## -- RecIAFSpkInNest
# - Layer generation
fl0 = RecIAFSpkInNest(
weights_in=weights_in,
weights_rec=weights_rec,
dt=0.001,
bias=bias,
tau_mem=tau_mem,
tau_syn_exc=tau_syn_exc,
tau_syn_inh=tau_syn_inh,
refractory=0.001,
record=True,
)
fl1 = RecIAFSpkInNest(
weights_in=weights_in1,
weights_rec=weights_rec,
dt=0.001,
bias=bias,
tau_mem=tau_mem,
tau_syn_exc=tau_syn_exc,
tau_syn_inh=tau_syn_inh,
refractory=0.001,
record=True,
)
# - Set input weights to same as fl0
fl1.weights_in = weights_in
assert (fl1.weights_in_ == weights_in).all()
# - Compare states before and after
fl0.evolve(tsInp, duration=0.12)
fl1.evolve(tsInp, duration=0.12)
assert (fl0.state == fl1.state).all()
fl0.terminate()
fl1.terminate()
## -- RecAEIFSpkInNest
# - Layer generation
fl0 = RecAEIFSpkInNest(
weights_in=weights_in,
weights_rec=weights_rec,
dt=0.001,
bias=bias,
tau_mem=tau_mem,
tau_syn_exc=tau_syn_exc,
tau_syn_inh=tau_syn_inh,
refractory=0.001,
record=True,
)
fl1 = RecAEIFSpkInNest(
weights_in=weights_in1,
weights_rec=weights_rec,
dt=0.001,
bias=bias,
tau_mem=tau_mem,
tau_syn_exc=tau_syn_exc,
tau_syn_inh=tau_syn_inh,
refractory=0.001,
record=True,
)
# - Set input weights to same as fl0
fl1.weights_in[0, 0] = weights_in[0, 0]
assert (fl1.weights_in_ == weights_in).all()
# - Compare states before and after
fl0.evolve(tsInp, duration=0.12)
fl1.evolve(tsInp, duration=0.12)
assert (fl0.state == fl1.state).all()
fl0.terminate()
fl1.terminate()