def test_get_number_of_past_occurrences_of_specific_subsequence_works_correctly(self):
seq = np.array([0, 1, 2, 3, 5, 1, 2, 6, 0, 3, 0, 1, 2, 3, 5, 0, 0, 1, 2, 7])
subseq = np.array([0, 1, 2])
occs_correct = np.array([0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3])
np.testing.assert_array_equal(
metrics.get_number_of_past_occurrences_of_specific_sequence(seq, subseq), occs_correct,
)
def basic_replay_stats(CONFIG):
"""
Plot some statistics of this network's behavior.
"""
SEED = CONFIG['SEED']
LOAD_FILE_NAME = CONFIG['LOAD_FILE_NAME']
GAIN_HIGH = CONFIG['GAIN_HIGH']
GAIN_LOW = CONFIG['GAIN_LOW']
LINGERING_INPUT_VALUE = CONFIG['LINGERING_INPUT_VALUE']
LINGERING_INPUT_TIMESCALE = CONFIG['LINGERING_INPUT_TIMESCALE']
STRONG_DRIVE_AMPLITUDE = CONFIG['STRONG_DRIVE_AMPLITUDE']
T_SPONTANEOUS = CONFIG['T_SPONTANEOUS']
SPONTANEOUS_REPEATS = CONFIG['SPONTANEOUS_REPEATS']
FIG_SIZE = CONFIG['FIG_SIZE']
FONT_SIZE = CONFIG['FONT_SIZE']
np.random.seed(SEED)
fig, axs = plt.subplots(1, 3, figsize=FIG_SIZE, tight_layout=True)
ntwk_base = np.load(LOAD_FILE_NAME)[0]
n_nodes = ntwk_base.w.shape[0]
# show that adding in lingering activity increases probability of replay
driven_path = ntwk_base.node_0_path_tree[0]
p_replay_no_lingering = 1
for node_prev, node_next in zip(driven_path[:-1], driven_path[1:]):
intrinsic = ntwk_base.w[:, node_prev]
refractory = np.zeros((n_nodes,), dtype=float)
refractory[node_prev] = ntwk_base.refractory_strength
inputs = intrinsic + refractory
prob = np.exp(ntwk_base.gain * inputs)
prob /= prob.sum()
p_replay_no_lingering *= prob[node_next]
lingering_inputs = np.zeros((n_nodes,), dtype=float)
lingering_inputs[np.array(driven_path)] = LINGERING_INPUT_VALUE
p_replay_with_lingering = 1
for node_prev, node_next in zip(driven_path[:-1], driven_path[1:]):
intrinsic = ntwk_base.w[:, node_prev]
refractory = np.zeros((n_nodes,), dtype=float)
refractory[node_prev] = ntwk_base.refractory_strength
inputs = intrinsic + refractory + lingering_inputs
prob = np.exp(ntwk_base.gain * inputs)
prob /= prob.sum()
p_replay_with_lingering *= prob[node_next]
axs[0].bar([0, 1], [p_replay_no_lingering, p_replay_with_lingering], align='center')
axs[0].set_xticks([0, 1])
axs[0].set_xticklabels(['Without \n nonassociative \n priming', 'With \n nonassociative \n priming'])
axs[0].set_ylabel('sequence replay probability')
for ax, gain in zip(axs[1:], [GAIN_LOW, GAIN_HIGH]):
# show expected number of spontaneous replays with and without nonassociative priming
ntwk_no_nap = deepcopy(ntwk_base)
ntwk_no_nap.gain = gain
ntwk_with_nap = deepcopy(ntwk_base)
ntwk_with_nap.gain = gain
ntwk_with_nap.lingering_input_value = LINGERING_INPUT_VALUE
ntwk_with_nap.lingering_input_timescale = LINGERING_INPUT_TIMESCALE
drives = np.zeros((T_SPONTANEOUS, n_nodes), dtype=float)
for t, node in enumerate(driven_path):
drives[t, node] = STRONG_DRIVE_AMPLITUDE
past_occurrences_of_driven_seq_no_nap = []
for _ in range(SPONTANEOUS_REPEATS):
ntwk = deepcopy(ntwk_no_nap)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
activation_seq = np.array(ntwk.rs_history).nonzero()[1]
past_occurrences_of_driven_seq_no_nap.append(
metrics.get_number_of_past_occurrences_of_specific_sequence(activation_seq, driven_path)
)
past_occurrences_of_driven_seq_no_nap = np.array(past_occurrences_of_driven_seq_no_nap)
past_occurrences_of_driven_seq_no_nap_mean = np.mean(past_occurrences_of_driven_seq_no_nap, axis=0)
past_occurrences_of_driven_seq_no_nap_sem = stats.sem(past_occurrences_of_driven_seq_no_nap, axis=0)
past_occurrences_of_driven_seq_with_nap = []
for _ in range(SPONTANEOUS_REPEATS):
ntwk = deepcopy(ntwk_with_nap)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
activation_seq = np.array(ntwk.rs_history).nonzero()[1]
past_occurrences_of_driven_seq_with_nap.append(
metrics.get_number_of_past_occurrences_of_specific_sequence(activation_seq, driven_path)
)
past_occurrences_of_driven_seq_with_nap = np.array(past_occurrences_of_driven_seq_with_nap)
past_occurrences_of_driven_seq_with_nap_mean = np.mean(past_occurrences_of_driven_seq_with_nap, axis=0)
past_occurrences_of_driven_seq_with_nap_sem = stats.sem(past_occurrences_of_driven_seq_with_nap, axis=0)
ts = np.arange(T_SPONTANEOUS)
ax.plot(ts, past_occurrences_of_driven_seq_no_nap_mean, color='b', lw=2)
ax.fill_between(
ts,
past_occurrences_of_driven_seq_no_nap_mean - past_occurrences_of_driven_seq_no_nap_sem,
past_occurrences_of_driven_seq_no_nap_mean + past_occurrences_of_driven_seq_no_nap_sem,
color='b',
alpha=0.3,
)
ax.plot(ts, past_occurrences_of_driven_seq_with_nap_mean, color='g', lw=2)
ax.fill_between(
ts,
past_occurrences_of_driven_seq_with_nap_mean - past_occurrences_of_driven_seq_with_nap_sem,
past_occurrences_of_driven_seq_with_nap_mean + past_occurrences_of_driven_seq_with_nap_sem,
color='g',
alpha=0.3,
)
ax.set_xlabel('time step')
ax.set_ylabel('past occurrences')
ax.set_title('gain = {}'.format(gain))
ax.legend(['Without nonasso-\n ciative priming', 'With nonasso- \n ciative priming'], loc='best')
for ax in axs:
axis_tools.set_fontsize(ax, FONT_SIZE)
