In [2]:

    

import numpy as np
import pylab as pl
from network_correlations import *
% matplotlib inline


    

    




/home/martin/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

In [5]:

    

'''
1)  Plot the rates of neuron 1 and neuron 2 together using the provided code. What 
happens when you induce correlations?
'''

stimulus1 = -np.pi/4
stimulus2 = 5./16*np.pi
phi_pref1 = 0.      # preferred orientation of neuron 1
phi_pref2 = np.pi/4 # preferred orientation of neuron 2
c=.0              # correlation between background inputs to the neurons
    
 
outputrate=orientation_tuning(phi_pref1, phi_pref2, stimulus1, c)
outputrate2=orientation_tuning(phi_pref1, phi_pref2, stimulus2, c)

pl.figure()
pl.title('Stimulus response')
pl.scatter(outputrate[0,:],outputrate[1,:],edgecolor='none',alpha=0.25)
pl.scatter(outputrate2[0,:],outputrate2[1,:],edgecolor='none',facecolor='r',alpha=0.25)
pl.xlabel(r'$\nu_1$')
pl.ylabel(r'$\nu_2$')
pl.xlim([5,20])
pl.ylim([5,20])


    

    
Out[5]:





(5, 20)






    

In [ ]:

    

'''
2) Try different combinations of stimuli and preferred orientation and check how 
noise correlations affect the separability of the output firing rates.
'''


    

In [ ]:

    

'''
3) How does the difference between prefered orientations relate to signal correlations?
'''