def fit((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"] = 4e-7
my_s = {
'start': 0.0,
'end': 1000.0,
'dt': 0.05,
'pre_spikes': [np.arange(50.0,1000.0,250.0)],
'I_ext': lambda t:0.0
}
seed = int(int(time.time()*1e8)%1e9)
accs = [PeriodicAccumulator(['weights'], interval=10)]
if p["h1"]:
accums = run(my_s, lambda **kwargs:False, get_dendr_spike_det(p["thresh"]), accs, seed=seed, neuron=neuron, learn=learn, voltage_clamp=True, U_clamp=p['Uclamp'], h=1.0)
else:
accums = run(my_s, lambda **kwargs:False, get_dendr_spike_det(p["thresh"]), accs, seed=seed, neuron=neuron, learn=learn, 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 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)):
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 fit((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"] = 4e-7
my_s = {
'start': 0.0,
'end': 1000.0,
'dt': 0.05,
'pre_spikes': [np.arange(50.0, 1000.0, 250.0)],
'I_ext': lambda t: 0.0
}
seed = int(int(time.time() * 1e8) % 1e9)
accs = [PeriodicAccumulator(['weights'], interval=10)]
if p["h1"]:
accums = run(my_s,
lambda **kwargs: False,
get_dendr_spike_det(p["thresh"]),
accs,
seed=seed,
neuron=neuron,
learn=learn,
voltage_clamp=True,
U_clamp=p['Uclamp'],
h=1.0)
else:
accums = run(my_s,
lambda **kwargs: False,
get_dendr_spike_det(p["thresh"]),
accs,
seed=seed,
neuron=neuron,
learn=learn,
voltage_clamp=True,
U_clamp=p['Uclamp'])
dump(accums, 'artola/' + 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 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'])