def overfit((repetition_i, p)):
neuron = get_default("neuron")
neuron["phi"]['alpha'] = p["alpha"]
neuron["phi"]['beta'] = p["beta"]
neuron["phi"]['r_max'] = p["r_max"]
learn = get_default("learn")
learn["eta"] = 1e-7
my_s = {
'start': 0.0,
'end': 4000.0,
'dt': 0.05,
'pre_spikes': [np.arange(50.0, 4000.0, 250.0)],
'I_ext': lambda t: 0.0
}
seed = int(int(time.time() * 1e8) % 1e9)
accs = [PeriodicAccumulator(['weights'], interval=10)]
accums = run(my_s,
lambda **kwargs: False,
get_dendr_spike_det_dyn_ref(p["thresh"], p["tau_ref_0"],
p["theta_0"]),
accs,
seed=seed,
neuron=neuron,
voltage_clamp=True,
U_clamp=p['Uclamp'])
dump(accums, 'artola/' + p['ident'])
def vary((repetition_i,p)):
n_vary = 5
values = {True: {"alpha":-55.0,
"beta":0.4,
"r_max":0.3},
False: {"alpha":-59.0,
"beta":0.5,
"r_max":0.17}}
vary = {"alpha":(-2.0,2.0),
"beta":(-0.1,0.2),
"r_max":(-0.05,0.15)}
down = vary[p["vary"]][0]
up = vary[p["vary"]][1]
middle = values[p["h1"]][p["vary"]]
vary_val = np.linspace(middle+down, middle+up, n_vary)[p["i"]]
values[p["h1"]][p["vary"]] = vary_val
values = values[p["h1"]]
neuron = get_default("neuron")
neuron["phi"]['r_max'] = values["r_max"]
neuron["phi"]['alpha'] = values["alpha"]
neuron["phi"]['beta'] = values["beta"]
learn = get_default("learn")
learn["eps"] = learn["eps"]*p["l_f"]
learn["eta"] = learn["eta"]*p["l_f"]
if not p["h1"]:
learn["eta"] = learn["eta"]*2.5
else:
learn["eta"] = learn["eta"]*1.3
spikes = np.array([61.0])
my_s = {
'start': 0.0,
'end': 150.0,
'dt': 0.05,
'pre_spikes': [spikes + p["delta"]],
'I_ext': lambda t: 0.0
}
seed = 1
accs = [PeriodicAccumulator(['y','weights'], interval=10), BooleanAccumulator(['spike', 'dendr_spike', 'pre_spikes'])]
if p["h1"]:
accums = run(my_s, get_fixed_spiker(spikes), get_dendr_spike_det(-50.0), accs, seed=seed, neuron=neuron, learn=learn, h=1.0)
else:
accums = run(my_s, get_fixed_spiker(spikes), get_dendr_spike_det(-50.0), accs, seed=seed, neuron=neuron, learn=learn)
dump((accums, values),'bi_poo/'+p['ident'])
def vary((repetition_i,p)):
etas = {True: 6e-8,
False: 30e-8}
varies = {"alpha": np.linspace(-52.0,-56.0,3),
"beta": np.linspace(0.15,0.25,3),
"r_max": np.linspace(0.1,0.3,3)}
learn = get_default("learn")
learn["eta"] = etas[p["h1"]]
neuron = get_default("neuron")
neuron["phi"]["alpha"] = -54.0
neuron["phi"]["beta"] = 0.25
neuron["phi"]["r_max"] = 0.2
neuron["phi"][p["vary"]] = varies[p["vary"]][p["ivary"]]
spikes = np.arange(20.0,301.0,20.0)
my_s = {
'start': 0.0,
'end': 350.0,
'dt': 0.05,
'pre_spikes': [spikes-10.0],
'I_ext': lambda t: 0.0
}
# 0.2 <= p <= 1.0
prob = 0.2 + 0.8*np.random.rand()
seed = int(int(time.time()*1e8)%1e9)
accs = [PeriodicAccumulator(['weights'], interval=10)]
if p["h1"]:
accums = run(my_s, get_fixed_spiker(spikes), get_dendr_spike_det(-50.0), accs, neuron=neuron, seed=seed, learn=learn, p_backprop=prob, h=1.0)
else:
accums = run(my_s, get_fixed_spiker(spikes), get_dendr_spike_det(-50.0), accs, neuron=neuron, seed=seed, learn=learn, p_backprop=prob)
dump((prob,accums),'sjostrom_switch/'+p['ident'])
def fit((repetition_i,p)):
values = {True: {"alpha":-55.0,
"beta":0.4,
"r_max":0.3},
False: {"alpha":-59.0,
"beta":0.5,
"r_max":0.17}}
neuron = get_default("neuron")
neuron["phi"]['r_max'] = values[p["h1"]]["r_max"]
neuron["phi"]['alpha'] = values[p["h1"]]["alpha"]
neuron["phi"]['beta'] = values[p["h1"]]["beta"]
learn = get_default("learn")
if not p["h1"]:
learn["eta"] = learn["eta"]*2.5
else:
learn["eta"] = learn["eta"]*1.3
spikes = np.array([101.0])
my_s = {
'start': 0.0,
'end': 300.0,
'dt': 0.05,
'pre_spikes': [spikes + p["delta"]],
'I_ext': lambda t: 0.0
}
seed = 1
accs = [PeriodicAccumulator(['y','weights'], interval=10), BooleanAccumulator(['spike', 'dendr_spike', 'pre_spikes'])]
if p["h1"]:
accums = run(my_s, get_fixed_spiker(spikes), get_dendr_spike_det(-50.0), accs, seed=seed, neuron=neuron, learn=learn, h=1.0)
else:
accums = run(my_s, get_fixed_spiker(spikes), get_dendr_spike_det(-50.0), accs, seed=seed, neuron=neuron, learn=learn)
dump((accums, values),'bi_poo/'+p['ident'])
def task((repetition_i, p)):
eps_stim = eps_stim_low + (eps_stim_high - eps_stim_low) * np.random.rand()
learn = get_default("learn")
learn["eta"] = [1e-7, 0.0]
learn["eps"] = [p["eps_learn"], 10**eps_stim]
neuron = get_default("neuron")
neuron["phi"]["alpha"] = -52.0
neuron["phi"]["beta"] = 0.25
neuron["phi"]["r_max"] = 0.35
post_spikes = np.arange(40.0, 2000.0, 20.0)
pre_spikes_learn = post_spikes - 10.0
pre_spikes_stim = post_spikes - p["stim_delta"]
my_s = {
'start': 0.0,
'end': 2000.0,
'dt': 0.05,
'pre_spikes': [pre_spikes_learn, pre_spikes_stim],
'I_ext': lambda t: 0.0
}
prob = p["prob"]
seed = int(int(time.time() * 1e8) % 1e9)
accs = [PeriodicAccumulator(['weights'], interval=100)]
accums = run(my_s,
get_fixed_spiker(post_spikes),
get_dendr_spike_det(p["thresh"]),
accs,
neuron=neuron,
seed=seed,
learn=learn,
p_backprop=prob)
dump((eps_stim, accums), p['ident'])
def task(inputs):
repetition_i = inputs[0]
p = inputs[1]
n_syn = p["n_syn"]
learn = get_default("learn")
learn["eps"] = 1e-1 / n_syn
learn["eta"] = 1e-3 / n_syn
neuron = get_default("neuron")
neuron["phi"]["alpha"] = p["alpha"]
neuron["phi"]["beta"] = p["beta"]
neuron["phi"]["r_max"] = p["r_max"]
neuron["g_S"] = p["g_S"]
epochs = 4
l_c = 6
eval_c = 2
cycles = epochs * l_c + (epochs + 1) * eval_c
cycle_dur = p["cycle_dur"]
t_end = cycles * cycle_dur
def exc_soma_cond(t):
if t % (cycle_dur * (l_c + eval_c)) < cycle_dur * eval_c:
return 0.0
else:
return ((1 + np.sin(np.pi / 2 + t / t_end * cycles * 2 * np.pi)) \
* 2e-3 * 1 + 8e-3) * p["g_factor"]
def inh_soma_cond(t):
if t % (cycle_dur * (l_c + eval_c)) < cycle_dur * eval_c:
return 0.0
else:
return 8e-2 * p["g_factor"]
dt = 0.05
f_r = 0.01 # 10Hz
t_pts = np.arange(0, t_end / cycles, dt)
poisson_spikes = [
t_pts[np.random.rand(t_pts.shape[0]) < f_r * dt] for _ in range(n_syn)
]
poisson_spikes = [[] if spikes.shape[0] == 0 else np.concatenate(
[np.arange(spike, t_end, cycle_dur) for spike in spikes])
for spikes in poisson_spikes]
for train in poisson_spikes:
train.sort()
my_s = {
'start': 0.0,
'end': t_end,
'dt': dt,
'pre_spikes': poisson_spikes,
'syn_cond_soma': {
'E': exc_soma_cond,
'I': inh_soma_cond
},
'I_ext': lambda t: 0.0
}
seed = int(int(time.time() * 1e8) % 1e9)
accs = [PeriodicAccumulator(get_all_save_keys(), interval=40)]
accums = run(my_s,
get_fixed_spiker(np.array([])),
get_phi_U_learner(neuron, dt),
accs,
neuron=neuron,
seed=seed,
learn=learn)
# TODO: create directrory if not exists
dump((seed, accums), "results/" + p['ident'])
def fit((repetition_i, p)):
learn = get_default("learn")
if p["h1"]:
learn['eta'] *= 0.125 / 8.0
else:
learn["eta"] *= 0.1
neuron = get_default("neuron")
neuron["phi"]['r_max'] = 0.2
neuron["phi"]['alpha'] = -54.0
neuron["phi"]['beta'] = 0.25
p_backprop = 0.75
freq = p["freq"]
delta = p["delta"]
n_spikes_in_burst = 10
burst_pause = 200.0
bursts = 50 / n_spikes_in_burst
burst_dur = 1000.0 * n_spikes_in_burst / freq
first_spike = 1000.0 / (2 * freq)
isi = 1000.0 / freq
t_end = bursts * (burst_dur + burst_pause)
spikes_in_burst = np.arange(first_spike, burst_dur, isi)
spikes = np.array([])
for i in range(bursts):
spikes = np.concatenate(
(spikes, spikes_in_burst + i * (burst_dur + burst_pause)))
pre_spikes = spikes + delta
my_s = {
'start': 0.0,
'end': t_end,
'dt': 0.05,
'pre_spikes': [pre_spikes],
'I_ext': lambda t: 0.0
}
seed = int(int(time.time() * 1e8) % 1e9)
accs = [PeriodicAccumulator(['weights'], interval=100)]
if p["h1"]:
accums = run(my_s,
get_fixed_spiker(spikes),
get_dendr_spike_det(-50.0),
accs,
seed=seed,
learn=learn,
neuron=neuron,
h=1.0,
p_backprop=p_backprop)
else:
accums = run(my_s,
get_fixed_spiker(spikes),
get_dendr_spike_det(-50.0),
accs,
seed=seed,
learn=learn,
neuron=neuron,
p_backprop=p_backprop)
dump(accums, 'sjostrom/' + p['ident'])
def task((repetition_i, p)):
n_syn = p["n_syn"]
learn = get_default("learn")
learn["eps"] = 1e-1 / (1.0 * n_syn)
learn["eta"] = learn["eps"] * p["eps_factor"]
neuron = get_default("neuron")
neuron["phi"]["alpha"] = p["alpha"]
neuron["phi"]["beta"] = p["beta"]
neuron["phi"]["r_max"] = 0.1
neuron["g_S"] = p["g_S"]
learn_epochs = 20
test_epochs = 20
epochs = learn_epochs + test_epochs
l_c = 8
eval_c = 2
cycles = epochs * l_c + (epochs + 1) * eval_c
cycle_dur = 100.0
epoch_dur = (l_c + eval_c) * cycle_dur
t_end = cycles * cycle_dur
exc_level = p["exc_level"]
g_factor = 50
def exc_soma_cond(t):
if t % (cycle_dur * (l_c + eval_c)
) < cycle_dur * eval_c or t > learn_epochs * epoch_dur:
return 0.0
else:
return ((1 + np.sin(-np.pi / 2 + t / t_end * cycles * 2 * np.pi)) *
exc_level + exc_level) * g_factor
def inh_soma_cond(t):
if t % (cycle_dur * (l_c + eval_c)
) < cycle_dur * eval_c or t > learn_epochs * epoch_dur:
return 0.0
else:
return 4e-2 * g_factor
dt = 0.05
f_r = 0.01 # 10Hz
t_pts = np.arange(0, t_end / cycles, dt)
seed = int(int(time.time() * 1e8) % 1e9)
poisson_spikes = [
t_pts[np.random.rand(t_pts.shape[0]) < f_r * dt] for _ in range(n_syn)
]
poisson_spikes = [[] if spikes.shape[0] == 0 else np.concatenate(
[np.arange(spike, t_end, cycle_dur) for spike in spikes])
for spikes in poisson_spikes]
for train in poisson_spikes:
train.sort()
my_s = {
'start': 0.0,
'end': t_end,
'dt': dt,
'pre_spikes': poisson_spikes,
'syn_cond_soma': {
'E': exc_soma_cond,
'I': inh_soma_cond
},
'I_ext': lambda t: 0.0
}
phi_spiker = get_phi_spiker(neuron)
# deprecated
def my_spiker(curr, dt, **kwargs):
# we want no spikes in eval cycles
if curr['t'] % (cycle_dur * (l_c + eval_c)) < cycle_dur * eval_c:
return False
else:
return phi_spiker(curr, dt, **kwargs)
accs = [
PeriodicAccumulator(get_all_save_keys(), interval=20, y_keep=3),
BooleanAccumulator(['spike'])
]
accums = run(my_s,
phi_spiker,
get_inst_backprop(),
accs,
neuron=neuron,
seed=seed,
learn=learn)
dump((seed, accums), 'sine_task_backprop/' + p['ident'])
def task((repetition_i, p)):
n_syn = p["n_syn"]
learn = get_default("learn")
learn["eps"] = 1e-1 / (1.0 * n_syn)
learn["eta"] = learn["eps"] * p["eps_factor"]
neuron = get_default("neuron")
neuron["phi"]["alpha"] = p["alpha"]
neuron["phi"]["beta"] = p["beta"]
neuron["phi"]["r_max"] = 0.1
neuron["g_S"] = p["g_S"]
learn_epochs = 2
test_epochs = 1
epochs = learn_epochs + test_epochs
l_c = 4
eval_c = 2
cycles = epochs * l_c + (epochs + 1) * eval_c
cycle_dur = p["cycle_dur"]
epoch_dur = (l_c + eval_c) * cycle_dur
t_end = cycles * cycle_dur
g_factor = p["g_factor"]
def exc_soma_cond(t):
if t % (cycle_dur * (l_c + eval_c)) < cycle_dur * eval_c or t > learn_epochs * epoch_dur:
return 0.0
return p["exc_level"] * p["g_factor"]
def inh_soma_cond(t):
if t % (cycle_dur * (l_c + eval_c)) < cycle_dur * eval_c or t > learn_epochs * epoch_dur:
return 0.0
return 1e-1 * p["g_factor"]
dt = 0.05
f_r = 1.0 / cycle_dur
t_pts = np.arange(0, t_end / cycles, dt)
seed = int(int(time.time() * 1e8) % 1e9)
reg_spikes = [np.arange(i+1,t_end+1,10) for i in range(n_syn)]
poisson_spikes = [t_pts[np.random.rand(t_pts.shape[0]) < f_r * dt] for _ in range(n_syn)]
poisson_spikes = [[] if spikes.shape[0] == 0 else np.concatenate(
[np.arange(spike, t_end, cycle_dur) for spike in spikes]) for spikes in poisson_spikes]
for train in poisson_spikes:
train.sort()
my_s = {
'start': 0.0,
'end': t_end,
'dt': dt,
'pre_spikes': reg_spikes,
'syn_cond_soma': {'E': exc_soma_cond, 'I': inh_soma_cond},
'I_ext': lambda t: 0.0
}
phi_spiker = get_phi_spiker(neuron)
# deprecated
def my_spiker(curr, dt, **kwargs):
# we want no spikes in eval cycles
if curr['t'] % (cycle_dur * (l_c + eval_c)) < cycle_dur * eval_c:
return False
else:
return phi_spiker(curr, dt, **kwargs)
if p["wiggle"] is None:
dendr_predictor = phi
else:
us = np.linspace(-100,20,1000)
ampl = neuron["phi"]["r_max"]
phis = phi(us, neuron)
alphas = []
for i in range(p["wiggle"]):
alphas.append(us[phis > (i+0.5)*ampl/p["wiggle"]][0])
r_m = neuron['phi']['r_max']/p["wiggle"]
def dendr_predictor(V, neuron):
return np.sum([phi(V,{'phi':{'alpha': al, 'beta':p["beta_wiggle"], 'r_max': r_m}}) for al in alphas])
accs = [PeriodicAccumulator(get_all_save_keys(), interval=20,
y_keep=3), BooleanAccumulator(['spike'])]
accums = run(my_s, get_fixed_spiker(np.array([])), get_phi_U_learner(neuron, dt, p["exc_decrease"]),
accs, neuron=neuron, seed=seed, learn=learn, dendr_predictor=dendr_predictor)
dump((seed, accums), 'wiggle_test/' + p['ident'])