def novel_pattern_replay(CONFIG):
"""
Show how a network that has weak connections in addition to its strong connections can learn to replay novel patterns that would not normally arise spontaneously.
"""
SEED = CONFIG['SEED']
LOAD_FILE_NAME = CONFIG['LOAD_FILE_NAME']
W_WEAK = CONFIG['W_WEAK']
GAIN = CONFIG['GAIN']
REFRACTORY_STRENGTH = CONFIG['REFRACTORY_STRENGTH']
LINGERING_INPUT_VALUE = CONFIG['LINGERING_INPUT_VALUE']
LINGERING_INPUT_TIMESCALE = CONFIG['LINGERING_INPUT_TIMESCALE']
STRONG_DRIVE_AMPLITUDE = CONFIG['STRONG_DRIVE_AMPLITUDE']
WEAK_DRIVE_AMPLITUDE = CONFIG['WEAK_DRIVE_AMPLITUDE']
TRIAL_LENGTH_TRIGGERED_REPLAY = CONFIG['TRIAL_LENGTH_TRIGGERED_REPLAY']
RUN_LENGTH = CONFIG['RUN_LENGTH']
FIG_SIZE_0 = CONFIG['FIG_SIZE_0']
FIG_SIZE_1 = CONFIG['FIG_SIZE_1']
FONT_SIZE = CONFIG['FONT_SIZE']
np.random.seed(SEED)
fig = plt.figure(figsize=FIG_SIZE_0, tight_layout=True)
axs = []
for row_ctr in range(3):
axs.append([fig.add_subplot(4, 3, 3*row_ctr + col_ctr) for col_ctr in range(1, 4)])
axs = list(axs)
axs.append(fig.add_subplot(4, 1, 4))
# load old network
ntwk_old = np.load(LOAD_FILE_NAME)[0]
# demonstrate how one cannot use intrinsic plasticity to learn sequence that is made of disjoint paths
path = list(ntwk_old.node_0_path_tree[0][:])
path[2:] = ntwk_old.node_1_path_tree[0][2:]
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_old.w.shape[0]), dtype=float)
for ctr, node in enumerate(path):
drives[ctr, node] = STRONG_DRIVE_AMPLITUDE
drives[len(path), path[0]] = STRONG_DRIVE_AMPLITUDE
for ctr, ax in enumerate(axs[0]):
ntwk = deepcopy(ntwk_old)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(ax, spikes, drives)
ax.set_xlim(-1, len(drives))
ax.set_ylim(-1, 20)
ax.set_xlabel('time step')
ax.set_ylabel('active \n ensemble')
ax.set_title('Strongly driving nonexisting path (trial {})'.format(ctr + 1))
w = ntwk_old.w.copy()
# add weak connection to element 2 of node_1 path tree from element 1 of node_0 path tree
w[ntwk_old.node_1_path_tree[0][2], ntwk_old.node_0_path_tree[0][1]] = W_WEAK
# make new base network
ntwk_base = network.RecurrentSoftMaxLingeringModel(
w, GAIN, REFRACTORY_STRENGTH, LINGERING_INPUT_VALUE, LINGERING_INPUT_TIMESCALE
)
ntwk_base.node_0 = ntwk_old.node_0
ntwk_base.node_0 = ntwk_old.node_1
ntwk_base.node_0_path_tree = ntwk_old.node_0_path_tree
ntwk_base.node_1_path_tree = ntwk_old.node_1_path_tree
# demonstrate how weak connections allow linking of paths into short term memory
path = list(ntwk_base.node_0_path_tree[0][:])
path[2:] = ntwk_base.node_1_path_tree[0][2:]
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_base.w.shape[0]), dtype=float)
for ctr, node in enumerate(path):
drives[ctr, node] = STRONG_DRIVE_AMPLITUDE
drives[len(path), path[0]] = STRONG_DRIVE_AMPLITUDE
for ctr, ax in enumerate(axs[1]):
ntwk = deepcopy(ntwk_base)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(ax, spikes, drives)
ax.set_xlim(-1, len(drives))
ax.set_ylim(-1, 20)
ax.set_xlabel('time step')
ax.set_ylabel('active \n ensemble')
ax.set_title('Activity from forced initial condition after \n driving path with weak connection (trial {})'.format(ctr + 1))
# demonstrate how weak connections do not substantially affect path probabilities
path = list(ntwk_base.node_0_path_tree[0][:])
path[2:] = ntwk_base.node_1_path_tree[0][2:]
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_old.w.shape[0]), dtype=float)
drives[0, path[0]] = STRONG_DRIVE_AMPLITUDE
for ctr, ax in enumerate(axs[2]):
ntwk = deepcopy(ntwk_base)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(ax, spikes, drives)
ax.set_xlim(-1, len(drives))
ax.set_ylim(-1, 20)
ax.set_xlabel('time step')
ax.set_ylabel('active ensemble')
ax.set_title('Free activity after only forcing \n initial condition (trial {})'.format(ctr + 1))
path = list(ntwk_base.node_0_path_tree[0][:])
path[2:] = ntwk_base.node_1_path_tree[0][2:]
drives = np.zeros((RUN_LENGTH, ntwk_base.w.shape[0]), dtype=float)
for ctr, node in enumerate(path):
drives[ctr, node] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[3], spikes, drives)
axs[3].set_xlim(-1, len(drives))
axs[3].set_ylim(-1, 40)
axs[3].set_xlabel('time step')
axs[3].set_ylabel('active \n ensemble')
axs[3].set_title('Free activity after driving path with weak connection')
for ax_row in axs[:-1]:
for ax in ax_row:
axis_tools.set_fontsize(ax, FONT_SIZE)
axis_tools.set_fontsize(axs[-1], FONT_SIZE)
# now demonstrate how pattern-matching computation changes with respect to short-term memory
fig, axs = plt.subplots(1, 2, figsize=FIG_SIZE_1, tight_layout=True)
path = list(ntwk_base.node_0_path_tree[1][:])
path[0] = 22
path[2] = 17
path[3] = ntwk_base.node_1_path_tree[0][3]
drives_new = np.zeros((len(path), ntwk_base.w.shape[0]), dtype=float)
drives_new[0, path[0]] = STRONG_DRIVE_AMPLITUDE
for ctr, node in enumerate(path[1:]):
drives_new[ctr + 1, node] = WEAK_DRIVE_AMPLITUDE
# drive a network with just the new drive to see how it completes the pattern
ntwk = deepcopy(ntwk_base)
ntwk.store_voltages = True
for drive in drives_new:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[0], spikes, drives_new)
axs[0].set_xlim(-1, 8)
axs[0].set_ylim(-1, ntwk_base.w.shape[0])
axs[0].set_xlabel('time step')
axs[0].set_ylabel('active ensemble')
axs[0].set_title('Weakly driving nonexistent path')
drives = np.concatenate([drives[:4, :], drives_new])
ntwk = deepcopy(ntwk_base)
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[1], spikes, drives)
axs[1].set_xlim(-1, 8)
axs[1].set_ylim(-1, ntwk_base.w.shape[0])
axs[1].set_xlabel('time step')
axs[1].set_ylabel('active ensemble')
axs[1].set_title('Weakly driving nonexistent path after \n strongly driving path with weak connection')
for ax in axs:
axis_tools.set_fontsize(ax, FONT_SIZE)
def novel_pattern_learning(CONFIG):
"""
Show that a network has an increased probability of embedding a novel pattern
into its connectivity network if we allow nonassociative priming to act.
"""
SEED = CONFIG["SEED"]
LOAD_FILE_NAME = CONFIG["LOAD_FILE_NAME"]
W_WEAK = CONFIG["W_WEAK"]
GAIN = CONFIG["GAIN"]
REFRACTORY_STRENGTH = CONFIG["REFRACTORY_STRENGTH"]
LINGERING_INPUT_VALUE = CONFIG["LINGERING_INPUT_VALUE"]
LINGERING_INPUT_TIMESCALE = CONFIG["LINGERING_INPUT_TIMESCALE"]
W_MAX = CONFIG["W_MAX"]
ALPHA = CONFIG["ALPHA"]
STRONG_DRIVE_AMPLITUDE = CONFIG["STRONG_DRIVE_AMPLITUDE"]
RUN_LENGTH = CONFIG["RUN_LENGTH"]
N_REPEATS = CONFIG["N_REPEATS"]
FIG_SIZE_0 = CONFIG["FIG_SIZE_0"]
FONT_SIZE = CONFIG["FONT_SIZE"]
np.random.seed(SEED)
# create new network with STDP learning rule
ntwk_old = np.load(LOAD_FILE_NAME)[0]
w = ntwk_old.w.copy()
# add weak connection to element 2 of node_1 path tree from element 1 of node_0 path tree
w[ntwk_old.node_1_path_tree[0][2], ntwk_old.node_0_path_tree[0][1]] = W_WEAK
w_to_track = (ntwk_old.node_1_path_tree[0][2], ntwk_old.node_0_path_tree[0][1])
path_novel = ntwk_old.node_0_path_tree[0][:2] + ntwk_old.node_1_path_tree[0][2:]
# make new base network
ntwk_base = network.RecurrentSoftMaxLingeringSTDPModelBasic(
w, GAIN, REFRACTORY_STRENGTH, LINGERING_INPUT_VALUE, LINGERING_INPUT_TIMESCALE, W_MAX, ALPHA
)
ntwk_base.node_0 = ntwk_old.node_0
ntwk_base.node_0 = ntwk_old.node_1
ntwk_base.node_0_path_tree = ntwk_old.node_0_path_tree
ntwk_base.node_1_path_tree = ntwk_old.node_1_path_tree
# show long trials of novel sequence drive, spontaneous activity, and test sequence
fig, axs = plt.subplots(N_REPEATS, 1, figsize=FIG_SIZE_0, sharex=True, tight_layout=True)
axs_twin = [ax.twinx() for ax in axs]
drives = np.zeros((RUN_LENGTH, ntwk_base.w.shape[0]), dtype=float)
for ctr, node in enumerate(path_novel):
drives[ctr, node] = STRONG_DRIVE_AMPLITUDE
for ax, ax_twin in zip(axs, axs_twin):
ntwk = deepcopy(ntwk_base)
ntwk.store_voltages = True
ws = []
for drive in drives:
ntwk.step(drive)
ws.append(ntwk.w[w_to_track])
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(ax, spikes, drives)
ax_twin.plot(ws, color="b", lw=2, alpha=0.7)
ax.set_ylabel("active \n ensemble")
ax_twin.set_ylabel("W({}, {})".format(*w_to_track), color="b")
axs[-1].set_xlabel("time step")
for ax_twin in axs_twin:
ax_twin.set_ylim(0, 2)
axs[0].set_xlim(-5, RUN_LENGTH)
axs[0].set_title("With nonassociative priming")
for ax in list(axs) + axs_twin:
axis_tools.set_fontsize(ax, FONT_SIZE)
fig, axs = plt.subplots(N_REPEATS, 1, figsize=FIG_SIZE_0, sharex=True, tight_layout=True)
axs_twin = [ax.twinx() for ax in axs]
drives = np.zeros((RUN_LENGTH, ntwk_base.w.shape[0]), dtype=float)
for ctr, node in enumerate(path_novel):
drives[ctr, node] = STRONG_DRIVE_AMPLITUDE
for ax, ax_twin in zip(axs, axs_twin):
ntwk = deepcopy(ntwk_base)
ntwk.lingering_input_value = 0
ntwk.store_voltages = True
ws = []
for drive in drives:
ntwk.step(drive)
ws.append(ntwk.w[w_to_track])
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(ax, spikes, drives)
ax_twin.plot(ws, color="b", lw=2, alpha=0.7)
ax.set_ylabel("active \n ensemble")
ax_twin.set_ylabel("W({}, {})".format(*w_to_track), color="b")
axs[-1].set_xlabel("time step")
for ax_twin in axs_twin:
ax_twin.set_ylim(0, 2)
axs[0].set_xlim(-5, RUN_LENGTH)
axs[0].set_title("Without nonassociative priming")
for ax in list(axs) + axs_twin:
axis_tools.set_fontsize(ax, FONT_SIZE)
def basic_replay_ex(CONFIG):
"""
Run a simulation demonstrating the basic capability of a network with nonassociative priming to
demonstrated replay, both triggered and spontaneous.
"""
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']
WEAK_DRIVE_AMPLITUDE = CONFIG['WEAK_DRIVE_AMPLITUDE']
TRIAL_LENGTH_TRIGGERED_REPLAY = CONFIG['TRIAL_LENGTH_TRIGGERED_REPLAY']
RUN_LENGTH = CONFIG['RUN_LENGTH']
FIG_SIZE = CONFIG['FIG_SIZE']
FONT_SIZE = CONFIG['FONT_SIZE']
np.random.seed(SEED)
ntwk_base = np.load(LOAD_FILE_NAME)[0]
ntwk_base.lingering_input_value = LINGERING_INPUT_VALUE
ntwk_base.lingering_input_timescale = LINGERING_INPUT_TIMESCALE
fig = plt.figure(figsize=FIG_SIZE, tight_layout=True)
axs = []
axs.append(fig.add_subplot(6, 2, 1))
axs.append(fig.add_subplot(6, 2, 2))
axs.append(fig.add_subplot(6, 2, 3))
axs.append(fig.add_subplot(6, 2, 4))
axs.append(fig.add_subplot(6, 1, 3))
axs.append(fig.add_subplot(6, 1, 4))
axs.append(fig.add_subplot(6, 1, 5))
axs.append(fig.add_subplot(6, 1, 6))
# play sequences aligned to the network's intrinsic path structure
path_00 = ntwk_base.node_0_path_tree[0]
path_10 = ntwk_base.node_1_path_tree[0]
# drive network for first trial: path_00
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_base.w.shape[1]), dtype=float)
drives[0, path_00[0]] = STRONG_DRIVE_AMPLITUDE
for t_ctr, node in enumerate(path_00[1:]):
drives[t_ctr + 1, node] = WEAK_DRIVE_AMPLITUDE
drives[len(path_00), path_00[0]] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_HIGH
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[0], spikes, drives)
axs[0].set_xlim(-1, len(drives))
axs[0].set_ylim(-1, 20)
axs[0].set_xlabel('time step')
axs[0].set_ylabel('active ensemble')
axs[0].set_title('Aligning external drive with \n strongly connected paths')
# drive network for first trial: path_10
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_base.w.shape[1]), dtype=float)
drives[0, path_10[0]] = STRONG_DRIVE_AMPLITUDE
for t_ctr, node in enumerate(path_10[1:]):
drives[t_ctr + 1, node] = WEAK_DRIVE_AMPLITUDE
drives[len(path_10), path_10[0]] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_HIGH
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[1], spikes, drives)
axs[1].set_xlim(-1, len(drives))
axs[1].set_ylim(-1, 20)
axs[1].set_xlabel('time step')
axs[1].set_ylabel('active ensemble')
axs[1].set_title('Aligning external drive with \n strongly connected paths')
# drive network for third trial: all path_00 except for element 2
path = list(path_00[:])
path[2] = path_10[2]
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_base.w.shape[1]), dtype=float)
drives[0, path[0]] = STRONG_DRIVE_AMPLITUDE
for t_ctr, node in enumerate(path[1:]):
drives[t_ctr + 1, node] = WEAK_DRIVE_AMPLITUDE
drives[len(path), path[0]] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_HIGH
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[2], spikes, drives)
axs[2].set_xlim(-1, len(drives))
axs[2].set_ylim(-1, 20)
axs[2].set_xlabel('time step')
axs[2].set_ylabel('active ensemble')
axs[2].set_title('Aligning external drive with \n nonexisting path')
# drive network for fourth trial: all path_10 except for element 2
path = list(path_10[:])
path[2] = path_00[2]
drives = np.zeros((TRIAL_LENGTH_TRIGGERED_REPLAY, ntwk_base.w.shape[1]), dtype=float)
drives[0, path[0]] = STRONG_DRIVE_AMPLITUDE
for t_ctr, node in enumerate(path[1:]):
drives[t_ctr + 1, node] = WEAK_DRIVE_AMPLITUDE
drives[len(path), path[0]] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_HIGH
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[3], spikes, drives)
axs[3].set_xlim(-1, len(drives))
axs[3].set_ylim(-1, 20)
axs[3].set_xlabel('time step')
axs[3].set_ylabel('active ensemble')
axs[3].set_title('Aligning external drive with \n nonexisting path')
# play sequence and then let network run spontaneously for a while
drives = np.zeros((RUN_LENGTH, ntwk_base.w.shape[1]), dtype=float)
for t_ctr, node in enumerate(path_00):
drives[t_ctr, node] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_HIGH
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[4], spikes, drives)
axs[4].set_xlim(-1, len(drives))
axs[4].set_ylim(-1, ntwk_base.w.shape[1])
axs[4].set_xlabel('time step')
axs[4].set_ylabel('active ensemble')
axs[4].set_title('Letting network run freely after driving strongly connected path (high gain)')
# play sequence and then let network run spontaneously for a while, now with lower gain
drives = np.zeros((RUN_LENGTH, ntwk_base.w.shape[1]), dtype=float)
for t_ctr, node in enumerate(path_00):
drives[t_ctr, node] = STRONG_DRIVE_AMPLITUDE
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_LOW
ntwk.store_voltages = True
for drive in drives:
ntwk.step(drive)
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[5], spikes, drives)
axs[5].set_xlim(-1, len(drives))
axs[5].set_ylim(-1, ntwk_base.w.shape[1])
axs[5].set_xlabel('time step')
axs[5].set_ylabel('active ensemble')
axs[5].set_title('Letting network run freely after driving strongly connected path (low gain)')
# let network run spontaneously for a while with no initial drive
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_HIGH
ntwk.store_voltages = True
for _ in range(RUN_LENGTH):
ntwk.step()
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[6], spikes, drives=None)
axs[6].set_xlim(-1, len(drives))
axs[6].set_ylim(-1, ntwk_base.w.shape[1])
axs[6].set_xlabel('time step')
axs[6].set_ylabel('active ensemble')
axs[6].set_title('Letting network run freely with no drive (high gain)')
# let network run spontaneously for a while with no initial drive, now with lower gain
ntwk = deepcopy(ntwk_base)
ntwk.gain = GAIN_LOW
ntwk.store_voltages = True
for _ in range(RUN_LENGTH):
ntwk.step()
spikes = np.array(ntwk.rs_history)
fancy_raster.by_row_circles(axs[7], spikes, drives=None)
axs[7].set_xlim(-1, len(drives))
axs[7].set_ylim(-1, ntwk_base.w.shape[1])
axs[7].set_xlabel('time step')
axs[7].set_ylabel('active ensemble')
axs[7].set_title('Letting network run freely with no drive (low gain)')
for ax in axs:
axis_tools.set_fontsize(ax, FONT_SIZE)
