TomGeorge1234
commented
1 year ago Hey Quinn, good to hear from you! And thanks, I didn't know this about BVC tuning distances, it's always nice to have some more biological realism. Would you be interested in becoming a contributor, making these changes yourself and pull requesting them in. If so, here are some suggested changes I'd make:
- Maybe consider changing
"distance_distribution"to"pref_wall_distance_distribution"(wordy but more readable). - A small issue I have is that the additional parameter
"max_wall_dist"only applies to the case whereparams["distance_distribution"] == "uniform"and is redundant otherwise. It would be better to double up on the already existingpref_wall_distance. So in full:
if pref_wall_distance_distribution == 'rayleigh':
self.tuning_distances = np.random.rayleigh(scale=self.pref_wall_distance, size=self.n)
if pref_wall_distance_distribution == 'normal':
self.tuning_distances = normal(low=0, high=2*self.pref_wall_distance, size=self.n)
# may as well add this one in while we're here...
if pref_wall_distance_distribution == 'delta':
self.tuning_distances = self.pref_wall_distance * np.ones(size=(self.n))- Update the BVC docstring just to describe to everyone how it works.
Thoughts? No worries I can defo do it myself but may not get round to it for a couple of weeks due to other deadlines.
jquinnlee
Hello! Just writing to suggest a small feature that I hope is useful for the BVC cell type. I have noticed the tuning distance parameter for the tuning_distances attribute in the BVC class is drawn from a Rayleigh distribution by default. Based on some recent comparisons against large population recordings, I am observing better fits in a BVC-to-Place model if the BVC tuning distances are drawn from a uniform distribution, and I thought this could be a useful feature to integrate. The solution I have found easy enough is to add the param "max_wall_dist" to the params dictionary and perhaps a string to determine whether the distances are drawn from a uniform or rayleigh distribution, and then define the tuning distances from a uniform distribution, e.g:
default_params = { "n": 10, "reference_frame": "allocentric", "pref_wall_dist": 0.15, "max_wall_dist": 0.75, "distance_distribution": "uniform", "angle_spread_degrees": 11.25, "xi": 0.08, "beta": 12, "dtheta": 2, "min_fr": 0, "max_fr": 1, "name": "BoundaryVectorCells", }
...
if distance_distribution == "uniform": self.tuning_distances = np.random.uniform(low=0, high=self.max_wall_dist, size=self.n) else: self.tuning_distances = np.random.rayleigh(scale=self.pref_wall_dist, size=self.n)
Perhaps this would be a useful option to integrate for the BVC class. Many thanks for the amazing tool box!