figsize=(6, 6),
constrained_layout=True)
dpls[trial_idx].plot(tmin=tmin, ax=axes[0], show=False)
# Create an fixed-step tiling of frequencies from 20 to 100 Hz in steps of 1 Hz
freqs = np.arange(20., 100., 1.)
dpls[trial_idx].plot_tfr_morlet(freqs, n_cycles=7, tmin=tmin, ax=axes[1])
###############################################################################
# As a final exercise, let us try to re-run the simulation with a tonic bias
# applied to the L5 Pyramidal cells. Notice that the oscillation waveform is
# more regular, with less noise due to the fact that the tonic depolarization
# dominates over the influence of the Poisson drive. By default, a tonic bias
# is applied to the entire duration of the simulation.
net.add_tonic_bias(cell_type='L5_pyramidal', amplitude=6.)
dpls = simulate_dipole(net, n_trials=1)
dpls[trial_idx].plot()
###############################################################################
# Notice that the Layer 5 pyramidal neurons now fire nearly synchronously,
# leading to a synchronous activation of the inhibitory basket neurons,
# resulting in a low-latency IPSP back onto the pyramidal cells. The duration
# of the IPSP is ~20 ms, after which the combined effect of the tonic bias and
# Poisson drive is to bring the pyramidal cells back to firing threshold,
# creating a ~50 Hz PING rhythm. This type of synchronous rhythm is sometimes
# referred to as “strong” PING.
net.cell_response.plot_spikes_raster()
###############################################################################
def test_tonic_biases():
"""Test tonic biases."""
hnn_core_root = op.dirname(hnn_core.__file__)
# default params
params_fname = op.join(hnn_core_root, 'param', 'default.json')
params = read_params(params_fname)
net = Network(params, add_drives_from_params=True)
with pytest.raises(ValueError, match=r'cell_type must be one of .*$'):
net.add_tonic_bias(cell_type='name_nonexistent',
amplitude=1.0,
t0=0.0,
tstop=4.0)
with pytest.raises(ValueError,
match='Duration of tonic input cannot be'
' negative'):
net.add_tonic_bias(cell_type='L2_pyramidal',
amplitude=1.0,
t0=5.0,
tstop=4.0)
simulate_dipole(net, tstop=20.)
net.external_biases = dict()
with pytest.raises(ValueError,
match='End time of tonic input cannot be'
' negative'):
net.add_tonic_bias(cell_type='L2_pyramidal',
amplitude=1.0,
t0=5.0,
tstop=-1.0)
simulate_dipole(net, tstop=5.)
with pytest.raises(ValueError, match='parameter may be missing'):
params['Itonic_T_L2Pyr_soma'] = 5.0
net = Network(params, add_drives_from_params=True)
params.update({
'N_pyr_x': 3,
'N_pyr_y': 3,
'N_trials': 1,
'dipole_smooth_win': 5,
't_evprox_1': 5,
't_evdist_1': 10,
't_evprox_2': 20,
# tonic inputs
'Itonic_A_L2Pyr_soma': 1.0,
'Itonic_t0_L2Pyr_soma': 5.0,
'Itonic_T_L2Pyr_soma': 15.0
})
# old API
net = Network(params, add_drives_from_params=True)
assert 'tonic' in net.external_biases
assert 'L2_pyramidal' in net.external_biases['tonic']
# new API
net = Network(params)
net.add_tonic_bias(cell_type='L2_pyramidal', amplitude=1.0)
assert 'tonic' in net.external_biases
assert 'L5_pyramidal' not in net.external_biases['tonic']
assert net.external_biases['tonic']['L2_pyramidal']['t0'] == 0
with pytest.raises(ValueError, match=r'Tonic bias already defined for.*$'):
net.add_tonic_bias(cell_type='L2_pyramidal', amplitude=1.0)