from stimuli.AbstractStimulus import AbstractStimulus
from models.AbstractModel import AbstractModel
import numpy as np
from my_util import functions as fu
from numba import jit
from warnings import warn
from collections import OrderedDict
class LifacNoiseModel(AbstractModel):
# all times in milliseconds
# possible mem_res: 100 * 1000000 exact value unknown in p-units
DEFAULT_VALUES = OrderedDict([("mem_tau", 0.015),
("v_base", 0),
("v_zero", 0),
("threshold", 1),
("v_offset", -10),
("input_scaling", 60),
("delta_a", 0.08),
("tau_a", 0.1),
("a_zero", 2),
("noise_strength", 0.05),
("step_size", 0.00005),
("dend_tau", 0.001),
("refractory_period", 0.001)])
def __init__(self, params: dict = None):
super().__init__(params)
if self.parameters["step_size"] > 0.0001:
warn("LifacNoiseModel: The step size is quite big simulation could fail.")
self.voltage_trace = []
self.input_voltage = []
self.adaption_trace = []
self.spiketimes = []
self.stimulus = None
# self.frequency_trace = []
def simulate_slow(self, stimulus: AbstractStimulus, total_time_s):
self.stimulus = stimulus
time = np.arange(0, total_time_s, self.parameters["step_size"])
output_voltage = np.zeros(len(time), dtype='float64')
adaption = np.zeros(len(time), dtype='float64')
input_voltage = np.zeros(len(time), dtype='float64')
spiketimes = []
current_v = self.parameters["v_zero"]
current_a = self.parameters["a_zero"]
input_voltage[0] = fu.rectify(stimulus.value_at_time_in_s(time[0]))
output_voltage[0] = current_v
adaption[0] = current_a
for i in range(1, len(time), 1):
time_point = time[i]
# rectified input:
stimulus_strength = self._calculate_input_voltage_step(input_voltage[i - 1],
fu.rectify(stimulus.value_at_time_in_s(time_point)))
v_next = self._calculate_voltage_step(current_v, stimulus_strength - current_a)
a_next = self._calculate_adaption_step(current_a)
if len(spiketimes) > 0 and time[i] - spiketimes[-1] < self.parameters["refractory_period"] + self.parameters["step_size"]/2:
v_next = self.parameters["v_base"]
if v_next > self.parameters["threshold"]:
v_next = self.parameters["v_base"]
spiketimes.append(time_point)
a_next += self.parameters["delta_a"] / self.parameters["tau_a"]
output_voltage[i] = v_next
adaption[i] = a_next
input_voltage[i] = stimulus_strength
current_v = v_next
current_a = a_next
self.voltage_trace = output_voltage
self.adaption_trace = adaption
self.spiketimes = spiketimes
self.input_voltage = input_voltage
return output_voltage, spiketimes
def _calculate_voltage_step(self, current_v, input_v):
v_base = self.parameters["v_base"]
step_size = self.parameters["step_size"]
v_offset = self.parameters["v_offset"]
mem_tau = self.parameters["mem_tau"]
noise_strength = self.parameters["noise_strength"]
noise_value = np.random.normal()
noise = noise_strength * noise_value / np.sqrt(step_size)
return current_v + step_size * ((v_base - current_v + v_offset + input_v + noise) / mem_tau)
def _calculate_adaption_step(self, current_a):
step_size = self.parameters["step_size"]
return current_a + (step_size * (-current_a)) / self.parameters["tau_a"]
def _calculate_input_voltage_step(self, current_i, rectified_input):
# input_voltage[i] = input_voltage[i - 1] + (-input_voltage[i - 1] + rectified_stimulus_array[i] * input_scaling) / dend_tau
return current_i + (
(-current_i + rectified_input * self.parameters["input_scaling"]) / self.parameters["dend_tau"]) * \
self.parameters["step_size"]
def simulate(self, stimulus: AbstractStimulus, total_time_s, time_start=0):
v_zero = self.parameters["v_zero"]
a_zero = self.parameters["a_zero"]
step_size = self.parameters["step_size"]
threshold = self.parameters["threshold"]
v_base = self.parameters["v_base"]
delta_a = self.parameters["delta_a"]
tau_a = self.parameters["tau_a"]
v_offset = self.parameters["v_offset"]
mem_tau = self.parameters["mem_tau"]
noise_strength = self.parameters["noise_strength"]
input_scaling = self.parameters["input_scaling"]
dend_tau = self.parameters["dend_tau"]
ref_period = self.parameters["refractory_period"]
rectified_stimulus = rectify_stimulus_array(stimulus.as_array(time_start, total_time_s, step_size))
parameters = np.array(
[v_zero, a_zero, step_size, threshold, v_base, delta_a, tau_a, v_offset, mem_tau, noise_strength,
time_start, input_scaling, dend_tau, ref_period])
if dend_tau >= step_size:
voltage_trace, adaption, spiketimes, input_voltage = simulate_fast(rectified_stimulus, total_time_s, parameters)
else:
voltage_trace, adaption, spiketimes, input_voltage = simulate_fast_no_dend_tau(rectified_stimulus, total_time_s, parameters)
self.stimulus = stimulus
self.input_voltage = input_voltage
self.voltage_trace = voltage_trace
self.adaption_trace = adaption
self.spiketimes = spiketimes
return voltage_trace, spiketimes
def min_stimulus_strength_to_spike(self):
return self.parameters["threshold"] - self.parameters["v_base"]
def get_sampling_interval(self):
return self.parameters["step_size"]
def get_frequency(self):
# TODO also change simulates_frequency() if any calculation is added!
raise NotImplementedError("No calculation implemented yet for the frequency.")
def get_spiketimes(self):
return self.spiketimes
def get_voltage_trace(self):
return self.voltage_trace
def get_adaption_trace(self):
return self.adaption_trace
def simulates_frequency(self) -> bool:
return False
def simulates_spiketimes(self) -> bool:
return True
def simulates_voltage_trace(self) -> bool:
return True
def get_recording_times(self):
# [delay, stimulus_start, stimulus_duration, time_to_end]
self.stimulus = AbstractStimulus()
delay = 0
start = self.stimulus.get_stimulus_start_s()
duration = self.stimulus.get_stimulus_duration_s()
total_time = len(self.voltage_trace) / self.parameters["step_size"]
return [delay, start, duration, total_time]
def get_model_copy(self):
return LifacNoiseModel(self.parameters)
def get_eodf_scaled_parameters(self, factor):
scaled_parameters = self.parameters.copy()
time_param_keys = ["refractory_period", "tau_a", "mem_tau", "dend_tau", "delta_a"]
for key in time_param_keys:
scaled_parameters[key] = self.parameters[key] / factor
return scaled_parameters
def find_v_offset(self, goal_baseline_frequency, base_stimulus, threshold=2, border=50000):
test_model = self.get_model_copy()
simulation_length = 6
v_search_step_size = 100
current_v_offset = -400
current_freq = test_v_offset(test_model, current_v_offset, base_stimulus, simulation_length)
while current_freq < goal_baseline_frequency:
if current_v_offset >= border:
return border
current_v_offset += v_search_step_size
current_freq = test_v_offset(test_model, current_v_offset, base_stimulus, simulation_length)
lower_bound = current_v_offset - v_search_step_size
upper_bound = current_v_offset
return binary_search_base_freq(test_model, base_stimulus, goal_baseline_frequency, simulation_length,
lower_bound, upper_bound, threshold)
def binary_search_base_freq(model: LifacNoiseModel, base_stimulus, goal_frequency, simulation_length, lower_bound,
upper_bound, threshold):
counter = 0
if threshold <= 0:
raise ValueError("binary_search_base_freq() - LifacNoiseModel: threshold is not allowed to be negative!")
while True:
counter += 1
middle = upper_bound - (upper_bound - lower_bound) / 2
frequency = test_v_offset(model, middle, base_stimulus, simulation_length)
# print("offset: {:.1f}, freq: {:.0f}".format(middle, frequency))
# print('{:.1f}, {:.1f}, {:.1f}, {:.1f} vs {:.1f} '.format(lower_bound, middle, upper_bound, frequency, goal_frequency))
if abs(frequency - goal_frequency) < threshold:
return middle
elif frequency < goal_frequency:
lower_bound = middle
elif frequency > goal_frequency:
upper_bound = middle
else:
print('lower bound: {:.1f}, middle: {:.1f}, upper_bound: {:.1f}, frequency: {:.1f} vs goal: {:.1f} '.format(
lower_bound, middle, upper_bound, frequency, goal_frequency))
raise ValueError("binary_search_base_freq() - LifacNoiseModel: Goal frequency might be nan?")
if abs(upper_bound - lower_bound) < 0.0001:
print("v_offset search stopped. bounds converged! freq: {:.2f}, bounds: {:.0f}"
.format(frequency, lower_bound))
# print(model.parameters)
warn("Search was stopped. Upper and lower bounds converged without finding a value closer than threshold!")
return middle
def test_v_offset(model: LifacNoiseModel, v_offset, base_stimulus, simulation_length):
model.set_variable("v_offset", v_offset)
try:
v, spiketimes = model.simulate(base_stimulus, simulation_length)
# if len(spiketimes) > 0:
# print("sim length", simulation_length, "last spike", max(spiketimes), "num of spikes:", len(spiketimes))
rel_spikes = [s for s in spiketimes if s > simulation_length / 3]
return len(rel_spikes) / (2/3 * simulation_length)
except ZeroDivisionError:
print("divide by zero!")
freq = 0
# if freq > 10000:
# from IPython import embed
# import matplotlib.pyplot as plt
# embed()
return freq
@jit(nopython=True)
def rectify_stimulus_array(stimulus_array: np.ndarray):
return np.array([x if x > 0 else 0 for x in stimulus_array])
@jit(nopython=True)
def simulate_fast(rectified_stimulus_array, total_time_s, parameters: np.ndarray):
v_zero = parameters[0]
a_zero = parameters[1]
step_size = parameters[2]
threshold = parameters[3]
v_base = parameters[4]
delta_a = parameters[5]
tau_a = parameters[6]
v_offset = parameters[7]
mem_tau = parameters[8]
noise_strength = parameters[9]
time_start = parameters[10]
input_scaling = parameters[11]
dend_tau = parameters[12]
ref_period = parameters[13]
time = np.arange(time_start, total_time_s, step_size)
length = len(time)
output_voltage = np.zeros(length)
adaption = np.zeros(length)
input_voltage = np.zeros(length)
spiketimes = []
output_voltage[0] = v_zero
adaption[0] = a_zero
input_voltage[0] = rectified_stimulus_array[0]
for i in range(1, len(time), 1):
noise_value = np.random.normal()
noise = noise_strength * noise_value / np.sqrt(step_size)
input_voltage[i] = input_voltage[i - 1] + (
(-input_voltage[i - 1] + rectified_stimulus_array[i]) / dend_tau) * step_size
output_voltage[i] = output_voltage[i - 1] + ((v_base - output_voltage[i - 1] + v_offset + (
input_voltage[i] * input_scaling) - adaption[i - 1] + noise) / mem_tau) * step_size
adaption[i] = adaption[i - 1] + ((-adaption[i - 1]) / tau_a) * step_size
if len(spiketimes) > 0 and time[i] - spiketimes[-1] < ref_period + step_size/2:
output_voltage[i] = v_base
if output_voltage[i] > threshold:
output_voltage[i] = v_base
spiketimes.append((i * step_size) + time_start)
adaption[i] += delta_a / tau_a
return output_voltage, adaption, spiketimes, input_voltage
@jit(nopython=True)
def simulate_fast_no_dend_tau(rectified_stimulus_array, total_time_s, parameters: np.ndarray):
v_zero = parameters[0]
a_zero = parameters[1]
step_size = parameters[2]
threshold = parameters[3]
v_base = parameters[4]
delta_a = parameters[5]
tau_a = parameters[6]
v_offset = parameters[7]
mem_tau = parameters[8]
noise_strength = parameters[9]
time_start = parameters[10]
input_scaling = parameters[11]
dend_tau = parameters[12]
ref_period = parameters[13]
time = np.arange(time_start, total_time_s, step_size)
length = len(time)
output_voltage = np.zeros(length)
adaption = np.zeros(length)
input_voltage = rectified_stimulus_array
spiketimes = []
output_voltage[0] = v_zero
adaption[0] = a_zero
for i in range(1, len(time), 1):
noise_value = np.random.normal()
noise = noise_strength * noise_value / np.sqrt(step_size)
output_voltage[i] = output_voltage[i - 1] + ((v_base - output_voltage[i - 1] + v_offset + (
input_voltage[i] * input_scaling) - adaption[i - 1] + noise) / mem_tau) * step_size
adaption[i] = adaption[i - 1] + ((-adaption[i - 1]) / tau_a) * step_size
if len(spiketimes) > 0 and time[i] - spiketimes[-1] < ref_period + step_size/2:
output_voltage[i] = v_base
if output_voltage[i] > threshold:
output_voltage[i] = v_base
spiketimes.append((i * step_size) + time_start)
adaption[i] += delta_a / tau_a
return output_voltage, adaption, spiketimes, input_voltage