def test_regression_gabor():
rf = hdf5_load('tests/data/test_data.hdf5', 'ReceptiveField', arrs=['neu'])['neu']
gabor = GaborFit.from_hdf5('tests/data/test_data.hdf5', load_prev_run=False)
gabor.fit(rf)
gnd = GaborFit.from_hdf5('tests/data/test_data.hdf5').rf_fit
assert np.mean((gabor.rf_fit - gnd)) < 0.05
#
# | $X$ | $Y$ |
# |------|------|
# | V1-1 | V1-2 |
# | V1-1 | V2-1 |
# | V2-1 | V2-2 |
#
# Then, for each group, we split the spiking data by stimulus into train and test sets.
# %% tags=["parameters"]
path_loader = "data/superstim.hdf5"
path_gabor = "data/superstim.hdf5"
# %%
cr = CCARepeatedStim(
loader := SpikeLoader.from_hdf5(path_loader), gabor := GaborFit.from_hdf5(path_gabor),
)
n_train = [500, 1000, 2000, 5000, 10000]
# %%
sns.pairplot(
cr.df,
hue="region",
vars=["x", "y", "σ", "azimuth", "altitude"],
corner=True,
plot_kws=dict(s=4, linewidth=0, alpha=0.3),
)
# %% [markdown]
# There is a sharp increase in sampled neuron at the V1-V2 boundary. This is due to the fact that V1 neurons outnumber V2 neurons by 60%. Furthermore, the azimuthal preferences of V2 neurons extend include more of the lateral visual field, reducing the number of potential matches with V1.
#
from src.gabor_analysis.gabor_fit import GaborFit
from src.spikeloader import SpikeLoader
""" Smooth retinotopic field and make contour plot. """
def gen_grid(var, σ):
zi = griddata((df['x'], df['y']),
df[var], (xi[None, :], yi[:, None]),
method='nearest')
return gaussian_filter(zi, σ, mode='mirror', truncate=3.)
if __name__ == '__main__':
loader = SpikeLoader.from_hdf5('data/raw.hdf5')
g = GaborFit.from_hdf5('data/gabor.hdf5')
df = pd.DataFrame(data=g.params_fit, columns=list(GaborFit.KEY.keys()))
df = df.join(loader.pos)
xi = np.linspace(df['x'].min(), df['x'].max(), 2000)
yi = np.linspace(df['y'].min(), df['y'].max(), 2000)
z_x, z_y = gen_grid('pos_x', 150), gen_grid('pos_y', 100),
fig, ax = plt.subplots(dpi=300)
con = ax.contour(xi,
yi,
z_x,
cmap='magma',
levels=np.arange(-10.5, -2, 1.),
alpha=1.)
from scipy.stats.stats import zscore
from src.canonical_analysis.subspace_comm import CCARepeatedStim
from src.gabor_analysis.gabor_fit import GaborFit
from src.power_law.subtract_spont import SubtractSpontAnalyzer
from src.spikeloader import SpikeLoader
alt.data_transformers.disable_max_rows()
sns.set()
# %% tags=["parameters"]
path_loader = "data/superstim_TX57.hdf5"
path_gabor = "data/superstim_TX57.hdf5"
# %%
loader = SpikeLoader.from_hdf5(path_loader)
gabor = GaborFit.from_hdf5(path_gabor)
idx_spont = loader.idx_spont
spks = zscore(loader.spks, axis=0)
spks_nospont = SubtractSpontAnalyzer(128).fit(spks,
loader.idx_spont).transform(spks)
# %% [markdown]
"""
# Canonical Correlation Analysis - Stimuli
We use CCA to capture the largest modes of the spiking data that are common to both neuron groups.
Let $X$ and $Y$ be $(n \times p_i)$ matrices where $n$ is the number of stimuli and $p_i$ is the number of neurons in each group. Random group assignment is based on a checkerboard pattern to avoid signal contamination between adjacent neurons.
Neuron assignment to each group never changes in the following analysis.
df_train.corrwith(S_neu_filtered)).assign(
regions=name,
group=group,
test_stim_in_train=case,
n=n)
out.append(corr)
return pd.concat(out).rename(columns={0: "corr"})
if __name__ == "__main__":
from src.gabor_analysis.gabor_fit import GaborFit
from src.spikeloader import SpikeLoader
cr = CCARepeatedStim(SpikeLoader.from_hdf5("data/processed.hdf5"),
GaborFit.from_hdf5("data/gabor.hdf5"))
n_train = [500, 1000, 2000, 5000, 10000, 20000]
df_un = cr.calc_cr(ns_train=n_train[:3])
test = cr.corr_between_test(df_un)
# %% [markdown]
# V1 and V2 are separated by a line where the azimuth preference reverses with increased receptive field size (σ).
#
# The retinotopy between both regions are matched by a method similar to importance sampling.
# %%
# %% [markdown]
# There is a sharp increase in sampled neuron at the V1-V2 boundary. This is due to the fact that V1 neurons outnumber V2 neurons by 60%. Furthermore, the azimuthal preferences of V2 neurons extend include more of the lateral visual field, reducing the number of potential matches with V1.
# %% [markdown]
# We randomly split each region into two for CCA and verify that the split is uniform.