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Working on the models

master
Lev 2 years ago
parent
commit
6c582c00cb
  1. 962
      PopulationSampling.ipynb
  2. 163
      SimpleCell.ipynb
  3. 181
      TuningCurves.ipynb
  4. 2
      model/decoding.py
  5. 2
      model/persistence.py
  6. 27
      sym_model.py

962
PopulationSampling.ipynb

File diff suppressed because one or more lines are too long

163
SimpleCell.ipynb

File diff suppressed because one or more lines are too long

181
TuningCurves.ipynb

File diff suppressed because one or more lines are too long

2
model/decoding.py

@ -14,7 +14,7 @@ from tqdm import trange
import ripser
from .persistence import persistence
from persistence import persistence
EPSILON = 0.0000000000001

2
model/persistence.py

@ -16,7 +16,7 @@ import ripser
from persim import plot_diagrams
import gudhi
from .decorators import multi_input
from decorators import multi_input
def hausdorff(data1, data2, homdim, coeff):

27
sym_model.py

@ -23,7 +23,10 @@ grating_f = sp.cos(k * (x - x0) * sp.cos(theta) + k * (y - y0) * sp.sin(theta) +
receptive_field = 1 / (2 * sp.pi * sigma * sigma) * sp.exp(-(x ** 2 + y ** 2) / (2 * sigma ** 2)) * sp.cos(
k * x * sp.cos(theta) + k * y * sp.sin(theta) + phi)
receptive_field = receptive_field.subs(theta, 0).subs(phi, 0)
p = sp.cosh(k ** 2 * sigma ** 2 * sp.cos(theta)) * sp.exp(k ** 2 * (1 + sp.cos(theta) ** 2) / 2) * sp.cos(
# p = sp.cosh(k ** 2 * sigma ** 2 * sp.cos(theta)) * sp.exp(k ** 2 * (1 + sp.cos(theta) ** 2) / 2) * sp.cos(
# phi - k * (x0 * sp.cos(theta) + y0 * sp.sin(theta)))
p = sp.cosh(k ** 2 * sigma ** 2 * sp.cos(theta) * 4) * sp.exp(-4 * k ** 2 * sigma ** 2) * sp.cos(
phi - k * (x0 * sp.cos(theta) + y0 * sp.sin(theta)))
sigma_split = np.arange(0.1, 1, 0.05)
@ -31,6 +34,10 @@ k_split = np.arange(0.2, 6, 0.2)
xy_split = np.arange(-1, 1, 0.05)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
@dataclass
class Cell:
sigma_val: float = defaults[sigma]
@ -93,11 +100,17 @@ class Population:
xy_dist: np.ndarray = np.ones(len(xy_split))):
return cls(cells=[Cell.random(sigma_dist, k_val, xy_dist) for _ in range(n)])
def get_response(self, phi_deg: float, theta_deg: float) -> typing.List[float]:
return [cell.get_value(theta_deg, phi_deg) for cell in self.cells]
def get_response(self, phi_deg: float, theta_deg: float, coef: float = 4, use_sigmoid: bool = True) -> np.ndarray:
return (sigmoid if use_sigmoid else (lambda x: x))(np.array([cell.get_value(theta_deg, phi_deg) for cell in self.cells]) * coef)
def sample_responses(self, n: int) -> np.ndarray:
def sample_responses(
self, n: int, noise_sigma: float = 0, coef: float = 2, use_sigmoid: bool = True,
custom_grid: typing.Optional[np.ndarray] = None
) -> np.ndarray:
return np.array([
self.get_response(phi_deg, theta_deg % 180)
for phi_deg, theta_deg in np.random.uniform(0, 360, (n, 2))
])
np.array([self.get_response(phi_deg, theta_deg % 180, coef=coef, use_sigmoid=use_sigmoid), np.ones(len(self.cells)) * phi_deg,
np.ones(len(self.cells)) * theta_deg]).swapaxes(0, 1)
for phi_deg, theta_deg in (np.random.uniform(0, 360, (n, 2)) if custom_grid is None else custom_grid)
]) + np.random.normal(
0, [noise_sigma, 0, 0],
(n if custom_grid is None else len(custom_grid), len(self.cells), 3))

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