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