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recamp_2pop.py
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recamp_2pop.py
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# -*- coding: utf-8 -*-
import numpy as np
import os
import datetime,time
import pylab as pl
import grid_utils.plotlib as pp
import grid_utils.gridlib as gl
import grid_utils.simlib as sl
from grid_utils.random_walk import RandomWalk
from grid_utils.spatial_inputs import SpatialInputs
from grid_utils.spatial_inputs import rhombus_mises
def dir_vect(theta):
"""
Returns a 2-d vector given an angle theta
"""
return np.array([np.cos(theta),np.sin(theta)])
def find_bump_peak_idxs(map1d,unravel=True,**kwargs):
"""
Utility function that returns the 2D coordinates of an activity bump
"""
from scipy.ndimage import gaussian_filter
map_side=int(np.sqrt(len(map1d)))
map2d=map1d.reshape(map_side,map_side)
map2d_smoothed=gaussian_filter(map2d, sigma=3,mode='wrap')
max_idx=map2d_smoothed.argmax()
if unravel:
return np.unravel_index(max_idx, map2d.shape)
else:
return max_idx
def get_bumps_idxs_all_shifts(r,N_e):
bump_idsx2d=np.zeros((2,N_e))
# compute the coordinates of the output bump of each stimulus bump shift
for shift_idx in range(N_e):
bx,by=find_bump_peak_idxs(r[:N_e,shift_idx],unravel=True)
bump_idsx2d[0,shift_idx]=bx
bump_idsx2d[1,shift_idx]=by
return bump_idsx2d
def get_activation_fun(activation,r_max):
"""
Neuronal activation function
"""
clip = lambda(x) : x.clip(min=0,max=r_max)
tanh = lambda(x) : np.clip(np.tanh(x/float(r_max))*r_max,1e-16,r_max)
if activation == 'linear':
return lambda x : x
if activation == 'clip':
return clip
elif activation == 'tanh':
return tanh
else:
raise Exception('Invalid activation function')
class __RecAmp2Pop(object):
"""
This class implements the basic methods for simulating the amplification model in 2D with 2 populations of
neuron (excitatory and inhibitory). This class is private and abstract and is the common building block
upon which the classes RecAmp2PopLearn and RecAmp2PopSteady are written.
The class RecAmp2PopLearn deals with the learning of the recurrent connectivity.
The class RecAmp2PopSteady deals with the simulation of the output steady-state patterns
"""
def __init__(self):
pass
def get_inputs(self,force_gen=False,comp_gridness_score=False,comp_tuning_index=True):
"""
Read inputs from disk and loads into the object
"""
#print self.paramMap
self.inputs=SpatialInputs(sl.map_merge(self.paramMap,{'n':self.n_e}),
force_gen=force_gen,
comp_gridness_score=comp_gridness_score,
comp_tuning_index=comp_tuning_index)
self.inputs_flat=self.inputs.inputs_flat
self.h_e=self.inputs_flat.T
self.h_i=np.zeros((self.N_i,self.NX))
self.h=np.vstack([self.h_e,self.h_i])
def get_hardwired_speed_weights(self):
"""
Generate hardwired speed-weight connectivity matrix
"""
phase_shift=self.speed_phase_shift
# row 1 has the weights of speed cells to grid cell 1
self.W_speed_east=np.zeros_like(self.W_ee)
self.W_speed_west=np.zeros_like(self.W_ee)
self.W_speed_north=np.zeros_like(self.W_ee)
self.W_speed_south=np.zeros_like(self.W_ee)
if self.use_eight_directions is True:
self.W_speed_north_east=np.zeros_like(self.W_ee)
self.W_speed_north_west=np.zeros_like(self.W_ee)
self.W_speed_south_east=np.zeros_like(self.W_ee)
self.W_speed_south_west=np.zeros_like(self.W_ee)
for phase_idx,phase in enumerate(self.gp.phases):
shifted_north_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi/2.),self.gp.phases)
shifted_south_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi/2.),self.gp.phases)
shifted_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(0),self.gp.phases)
shifted_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi),self.gp.phases)
self.W_speed_north[phase_idx,:]=self.W_ee[shifted_north_phase_idx,:]
self.W_speed_south[phase_idx,:]=self.W_ee[shifted_south_phase_idx,:]
self.W_speed_east[phase_idx,:]=self.W_ee[shifted_east_phase_idx,:]
self.W_speed_west[phase_idx,:]=self.W_ee[shifted_west_phase_idx,:]
if self.use_eight_directions is True:
shifted_north_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi/4),self.gp.phases)
shifted_north_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(np.pi*3/4),self.gp.phases)
shifted_south_east_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi/4),self.gp.phases)
shifted_south_west_phase_idx=gl.get_pos_idx(phase+phase_shift*dir_vect(-np.pi*3/4),self.gp.phases)
self.W_speed_north_east[phase_idx,:]=self.W_ee[shifted_north_east_phase_idx,:]
self.W_speed_north_west[phase_idx,:]=self.W_ee[shifted_north_west_phase_idx,:]
self.W_speed_south_east[phase_idx,:]=self.W_ee[shifted_south_east_phase_idx,:]
self.W_speed_south_west[phase_idx,:]=self.W_ee[shifted_south_west_phase_idx,:]
def switch_to_tuned_inputs(self):
"""
Switches on feed-forward spatial tuning (during the simulation)
"""
self.h_e=self.inputs_flat.T
self.h=np.vstack([self.h_e,self.h_i])
def switch_to_untuned_inputs(self):
"""
Switches off feed-forward spatial tuning (during the simulation)
"""
self.h_e=self.inputs.noise_flat.T
self.h=np.vstack([self.h_e,self.h_i])
def switch_to_no_feedforward_inputs(self):
"""
Switches off feed-forward spatial tuning (during the simulation)
"""
self.h_e=np.ones_like(self.inputs.noise_flat.T)*self.feed_forward_off_value
self.h=np.vstack([self.h_e,self.h_i])
def get_tuned_excitatory_weights(self,binary=True):
"""
Computes a tuned excitatory connectivity matrix
It sets to W_max_e the weights of the num_conns_ee connections with the smallest phase difference
If fixed_connectivity_tuning is set to a value smaller than 1, only the specified fraction of connections will be tuned
"""
self.W_ee=np.zeros((self.N_e,self.N_e))
if not hasattr(self,'fixed_connectivity_tuning'):
self.fixed_connectivity_tuning=1
num_tuned_conns=int(np.floor(self.fixed_connectivity_tuning*self.num_conns_ee))
num_untuned_conns=self.num_conns_ee-num_tuned_conns
for i in xrange(self.N_e):
ref_phase=self.gp.phases[i,:]
dists=gl.get_periodic_dist_on_rhombus(self.n_e,ref_phase,self.gp.phases,self.gp.u1,self.gp.u2)
if binary is True:
sorted_idxs=np.argsort(dists)
tuned_idxs=sorted_idxs[:self.num_conns_ee]
np.random.shuffle(tuned_idxs)
all_idxs=np.arange(self.N_e)
np.random.shuffle(all_idxs)
self.W_ee[i,tuned_idxs[0:num_tuned_conns]]=self.W_max_ee
self.W_ee[i,all_idxs[:num_untuned_conns]]=self.W_max_ee
else:
# initialize bump activity to zero
bump=np.zeros(self.N_e)
# To account for the periodicity of the phase space we need to add up 9 bumps that are shifted
# of +- one period in each dimension (i.e., 3 horizontal shifts + 3 vertical shifts)
for xp in (-1,0,1):
for yp in (-1,0,1):
shift=xp*self.gp.u1+yp*self.gp.u2
bump+=rhombus_mises(self.gp.phases-ref_phase[np.newaxis,:]+shift,0.1)
bump=bump/bump.sum()*self.W_tot_ee
self.W_ee[i,:]=bump
#self.W_ee[i,:]=expit(2*(1-dists/dists.max()))
#self.W_ee[i,:]/=self.W_ee[i,:].sum()
#self.W_ee[i,:]*=self.W_tot_ee
self.W[:self.N_e,:self.N_e]=self.W_ee
def get_random_inhibitory_weights(self):
"""
Compute random inhibitory connectivity matrix
"""
self.W_ei=np.zeros((self.N_e,self.N_i))
self.W_ie=np.zeros((self.N_i,self.N_e))
self. W_ii=np.zeros((self.N_i,self.N_i))
# connections to the excitatory neurons
for row_idx in range(self.N_e):
# from ihibitory
all_idxs_ei=np.arange(self.N_i)
np.random.shuffle(all_idxs_ei)
self.W_ei[row_idx,all_idxs_ei[0:self.num_conns_ei]]=self.W_max_ei
# connections to inhibitory neurons
for row_idx in range(self.N_i):
# from exitatory
all_idxs_ie=np.arange(self.N_e)
np.random.shuffle(all_idxs_ie)
self.W_ie[row_idx,all_idxs_ie[0:self.num_conns_ie]]=self.W_max_ie
# from inhibitory
all_idxs_ii=np.arange(self.N_i)
np.random.shuffle(all_idxs_ii)
self.W_ii[row_idx,all_idxs_ii[0:self.num_conns_ii]]=self.W_max_ii
self.W[:self.N_e,self.N_e:]=self.W_ei
self.W[self.N_e:,:self.N_e]=self.W_ie
self.W[self.N_e:,self.N_e:]=self.W_ii
def post_init(self,force_gen_inputs=False,comp_gridness_score=False,comp_tuning_index=True,get_inputs=True):
# set the seed
np.random.seed(self.seed)
# switches between local recurrent inhibition (n_i>0) or feed-forward inhibition (r0<0)
assert(self.n_i==0 or self.r0==0)
assert(self.n_i>0 or self.r0<0)
#print sl.params_to_str(self.paramMap)
self.N_e=self.n_e**2 # total number of excitatory neurons
self.N_i=self.n_i**2 # total number of inhibitory neurons
self.N=self.N_e+self.N_i # total number of neurons
self.NX=self.nx**2 # total number of space samples
self.num_conns_ee=int(np.floor(self.N_e*self.frac_conns_ee))
if self.n_i>0:
self.num_conns_ie=int(np.floor(self.N_e*self.frac_conns_ie))
self.num_conns_ei=int(np.floor(self.N_i*self.frac_conns_ei))
self.num_conns_ii=int(np.floor(self.N_i*self.frac_conns_ii))
# mean input/output weight for the excitatory neurons
self.W_av_star=float(self.W_tot_ee)/self.N_e
# maximal connection strength for excitatory/inhibitory neurons
self.W_max_ee=float(self.W_tot_ee)/self.num_conns_ee
if self.n_i>0:
self.W_max_ie=float(self.W_tot_ie)/self.num_conns_ie
self.W_max_ei=-float(self.W_tot_ei)/self.num_conns_ei
self.W_max_ii=-float(self.W_tot_ii)/self.num_conns_ii
# get grid properties
self.gp=gl.GridProps(self.n_e,self.grid_T,self.grid_angle)
# compute recurrent connectivity matrix (W_ee can be overwritten by learning or loaded from disk)
self.W=np.zeros((self.N,self.N))
self.get_tuned_excitatory_weights()
if self.N_i>0:
self.get_random_inhibitory_weights()
self.zero_phase_idx = gl.get_pos_idx([0.,0.],self.gp.phases)
# get inputs
if get_inputs:
self.get_inputs(force_gen_inputs,comp_gridness_score,comp_tuning_index)
def run_recurrent_dynamics(self,
record_mean_max=True,
record_rec_input_evo=False,
activation='tanh',
r_max=100,
init_r=None,
custom_h=None):
"""
The output activity is computed for each pixel independently without modeling
the random walk of the virtual rat explicitely
"""
print '\nRunning recurrent dynamics'
activation_fun=get_activation_fun(activation,r_max)
# initialization of the feed-forward input
if custom_h is None:
h=self.h
else:
assert(custom_h.shape[0]==self.N_e+self.N_i)
h=custom_h
# initialization of the output rates
if init_r is None:
r=np.zeros_like(h)
else:
assert(init_r.shape[0]==self.N_e+self.N_i)
r=init_r
# check that shapes matches
assert(h.shape==r.shape)
# number of spatial locations
num_pos=h.shape[1]
num_steps=int(self.recdyn_time/self.dt)
if record_mean_max is True:
self.rec_input_mean_vect=np.zeros((self.N,num_steps))
self.rec_input_max_vect=np.zeros((self.N,num_steps))
self.r_mean_vect=np.zeros((self.N,num_steps))
self.r_max_vect=np.zeros((self.N,num_steps))
self.r_evo=np.zeros((self.N,num_pos,self.recdyn_num_snaps))
if record_rec_input_evo:
self.rec_input_evo=np.zeros((self.N,num_pos,self.recdyn_num_snaps))
delta_snap=num_steps/self.recdyn_num_snaps
snap_idx=0
rec_input=np.zeros_like(r)
start_clock=time.time()
for t in range(num_steps):
#print '%d/%d'%(t,num_steps)
if np.remainder(t,delta_snap)==0 and snap_idx<self.recdyn_num_snaps:
sl.print_progress(snap_idx,self.recdyn_num_snaps,start_clock=start_clock,step=1)
self.r_evo[:,:,snap_idx]=r
#print('r_min: ',r.min())
#print('r_max: ',r.max())
if record_rec_input_evo:
self.rec_input_evo[:,:,snap_idx]=rec_input
snap_idx+=1
if record_mean_max:
self.rec_input_mean_vect[:,t]=np.mean(rec_input,axis=1)
self.rec_input_max_vect[:,t]=np.max(rec_input,axis=1)
self.r_mean_vect[:,t]=np.mean(r,axis=1)
self.r_max_vect[:,t]=np.max(r,axis=1)
# recurrent input
rec_input=np.dot(self.W,r)
# total input, add feed-forward inhibition if recurrent inhibition is not explicitely modeled
tot_input=h+rec_input
if self.N_i==0:
tot_input+=self.r0
tot_activation = activation_fun(tot_input)
r=r+(self.dt/self.tau)*(-r+tot_activation)
self.r=r
def compute_steady_scores(self,comp_inhibitory_scores=True,force_input_scores=False):
# excitatory scores
R_e=self.r[0:self.N_e,:].T
R_e=np.reshape(R_e,(self.nx,self.nx,self.N_e))
self.re_scores,re_spacings=gl.gridness_evo(R_e[:,:,:],self.L/self.nx,num_steps=10)
if comp_inhibitory_scores is True:
# inhibitory scores
R_i=self.r[self.N_e:,:].T
R_i=np.reshape(R_i,(self.nx,self.nx,self.N_i))
self.ri_scores,ri_spacings=gl.gridness_evo(R_i[:,:,:],self.L/self.nx,num_steps=10)
# input scores
if not hasattr(self.inputs,'in_scores') or force_input_scores:
print 'Computing input scores'
self.inputs.gen_data(False,comp_gridness_score=True)
self.he_scores=self.inputs.in_scores
def save_steady_scores(self):
"""
Updates data files by adding gridness scores
"""
assert (hasattr(self,'re_scores') and hasattr(self,'ri_scores') and hasattr(self,'he_scores') )
data=np.load(self.data_path,allow_pickle=True)
dataMap=dict(data.items())
scores_attrs=['re_scores','ri_scores','he_scores']
for scores_attr in scores_attrs:
assert(hasattr(self,scores_attr)),'%s is not a field'%scores_attr
dataMap[scores_attr]=getattr(self,scores_attr)
np.savez(self.data_path,**dataMap)
def load_steady_scores(self):
"""
Loads gridness scores. Generates and save them if not present in the data file.
"""
data=np.load(self.data_path,allow_pickle=True)
scores_attrs=['re_scores','ri_scores','he_scores']
if 're_scores' not in data.keys():
self.compute_steady_scores()
self.save_steady_scores()
data=np.load(self.data_path,allow_pickle=True)
for scores_attr in scores_attrs:
assert(scores_attr in data.keys())
setattr(self,scores_attr,data[scores_attr])
def update_speed_weights_step(self):
"""
Update step to learn speed weights
"""
weights_list = [self.W_speed_east, self.W_speed_west,self.W_speed_north,self.W_speed_south]
speed_input_list = [self.speed_inputs_east,self.speed_inputs_west,
self.speed_inputs_north,self.speed_inputs_south]
if self.use_eight_directions is True:
weights_list+=[self.W_speed_north_east,
self.W_speed_north_west,self.W_speed_south_east,self.W_speed_south_west]
speed_input_list+=[self.speed_inputs_north_east,self.speed_inputs_north_west,
self.speed_inputs_south_east,self.speed_inputs_south_west]
for weights,speed_input in zip(weights_list,speed_input_list):
weight_update=speed_input*(self.rr[:self.N_e]-self.input_mean)*(self.rr_e_trace.T-self.input_mean)
weights+=self.learn_rate_speed_weights*weight_update
# normalize to fixed mean of incoming and outgoing weights
weights-=(weights.mean(axis=1)-self.W_av_star)[:,np.newaxis]
weights-=(weights.mean(axis=0)-self.W_av_star)[np.newaxis,:]
# clip weights
np.clip(weights,0,self.W_max_e,out=weights)
def update_speed_input_step(self,curr_v):
"""
Update step for speed inputs (also used to learn speed weights)
"""
# update speed inputs
self.speed_inputs_east*=0
self.speed_inputs_west*=0
self.speed_inputs_north*=0
self.speed_inputs_south*=0
if self.use_eight_directions is True:
self.speed_inputs_north_east*=0
self.speed_inputs_north_west*=0
self.speed_inputs_south_east*=0
self.speed_inputs_south_west*=0
#speed_values=self.rr[:self.N_e,0]
speed_values=np.ones((self.N_e,1))
if curr_v[0]>0:
# north-east
if self.use_eight_directions is True and curr_v[1]>0:
self.speed_inputs_north_east=speed_values
# south-east
elif self.use_eight_directions is True and curr_v[1]<0:
self.speed_inputs_south_east=speed_values
#east
else:
self.speed_inputs_east=speed_values
elif curr_v[0]<0:
# north-west
if self.use_eight_directions is True and curr_v[1]>0:
self.speed_inputs_north_west=speed_values
# south-west
elif self.use_eight_directions is True and curr_v[1]<0:
self.speed_inputs_south_west=speed_values
# west
else:
self.speed_inputs_west=speed_values
else:
# north
if curr_v[1]>0:
self.speed_inputs_north=speed_values
# south
elif curr_v[1]<0:
self.speed_inputs_south=speed_values
def update_total_speed_input_step(self,curr_v):
"""
Update step to compute the total speed input to add to the recurrent dynamics
"""
tot_speed_input_east=np.dot(self.W_speed_east,self.speed_inputs_east)/self.N_e
tot_speed_input_west=np.dot(self.W_speed_west,self.speed_inputs_west)/self.N_e
tot_speed_input_north=np.dot(self.W_speed_north,self.speed_inputs_north)/self.N_e
tot_speed_input_south=np.dot(self.W_speed_south,self.speed_inputs_south)/self.N_e
self.tot_speed_input_all_padded[:self.N_e,0]=\
tot_speed_input_east+tot_speed_input_west+\
tot_speed_input_north+tot_speed_input_south
if self.use_eight_directions is True:
tot_speed_input_north_east=np.dot(self.W_speed_north_east,
self.speed_inputs_north_east)/self.N_e
tot_speed_input_north_west=np.dot(self.W_speed_north_west,
self.speed_inputs_north_west)/self.N_e
tot_speed_input_south_east=np.dot(self.W_speed_south_east,
self.speed_inputs_south_east)/self.N_e
tot_speed_input_south_west=np.dot(self.W_speed_south_west,
self.speed_inputs_south_west)/self.N_e
self.tot_speed_input_all_padded[:self.N_e,0]+=\
tot_speed_input_north_east+tot_speed_input_north_west+\
tot_speed_input_south_east+tot_speed_input_south_west
else:
# diagonal move with four directions
if abs(curr_v[0])>0 and abs(curr_v[1])>0:
self.tot_speed_input_all_padded[:self.N_e,0]*=.5
def update_recurrent_weights_step(self):
"""
Update step to learn recurrent weights
"""
# update weights: hebbian term
self.delta_Wee=self.learn_rate*(self.rr[0:self.N_e]-self.input_mean)*\
(self.rr[0:self.N_e].T-self.input_mean)
self.W_ee+=self.dt*self.delta_Wee
# update weights: normalize to fixed mean of incoming and outgoing weights
self.W_ee-=(self.W_ee.mean(axis=1)-self.W_av_star)[:,np.newaxis]
self.W_ee-=(self.W_ee.mean(axis=0)-self.W_av_star)[np.newaxis,:]
# clip weights
self.W_ee=np.clip(self.W_ee,0,self.W_max_ee)
# update excitatory weights in the big weight matrix
self.W[:self.N_e,:self.N_e]=self.W_ee
def run_recurrent_dynamics_with_walk(self,
walk_time,
num_snaps,
theta_sigma,
learn_recurrent_weights=False,
learn_speed_weights=False,
track_bump_evo=False,
track_cell_evo=False,
track_cell_idx=0,
run_in_circle=False,
sweep=False,
fixed_position=False,
use_recurrent_input=True,
use_theta_modulation=False,
theta_freq=10.,
use_tuning_switch=False,
switch_off_feedforward=False,
feed_forward_off_value=0.,
rec_gain_with_no_feedforward=1.,
switch_off_times=[],
switch_on_times=[],
tuning_time=1.,
evo_idxs=[],
force_walk=False,
periodic_walk=False,
init_p=np.array([0.,0.]),
init_theta=0.0,
interpolate_inputs=False,
activation='tanh',
r_max=100.,
position_dt=None,
synaptic_filter=False,
tau_synaptic=0.2,
walk_speed=None
):
# we cannot learn both recurrent weights and speed weights at the same time
assert(learn_recurrent_weights is False or learn_speed_weights is False)
# at most one of these flag can be true
assert ((int(run_in_circle)+int(sweep)+int(fixed_position)<2))
self.run_in_circle=run_in_circle
self.sweep=sweep
self.fixed_position=fixed_position
self.rec_gain_with_no_feedforward=rec_gain_with_no_feedforward
self.synaptic_filter=synaptic_filter
self.tau_synaptic=tau_synaptic
self.feed_forward_off_value=feed_forward_off_value
self.walk_speed = walk_speed if walk_speed is not None else self.speed
# copy initial weights in case they are rescaled to compensate the absence of feed-forward inputs
self.Wee_nogain=self.W_ee.copy()
self.W_nogain=self.W.copy()
# initialize swithing times (in case we are turning off feed-forward input or their tuning)
if len(switch_on_times)>0:
curr_switch_on_time=switch_on_times.pop(0)
else:
curr_switch_on_time=None
if len(switch_off_times)>0:
curr_switch_off_time=switch_off_times.pop(0)
else:
curr_switch_off_time=None
# activation function
activation_fun=get_activation_fun(activation,r_max)
### ============= LEARNING RECURRENT WEIGHTS ===============================
if learn_recurrent_weights is True:
# we cannot learn recurrent weights with speed input
assert(self.use_speed_input is False)
print 'Learning recurrent weights with random walk'
# initialize connections to the excitatory neurons
self.W_ee0=np.zeros((self.N_e,self.N_e))
# initial weights are random
if self.start_with_zero_connectivity is False:
print 'Initializing weights at random to the upper bound'
for row_idx in xrange(self.N_e):
idxs=np.arange(self.N_e)
np.random.shuffle(idxs)
self.W_ee0[row_idx,idxs[0:self.num_conns_ee]]=self.W_max_ee
# initial weights are set to zero
else:
print 'Initializing weights to zero'
# initializations
self.learn_snap_idx=0
self.learn_walk_step_idx=0
self.W_ee=self.W_ee0
self.W[:self.N_e,:self.N_e]=self.W_ee
self.rr=np.zeros((self.N,1))
# weight evolution vectors
if len(evo_idxs) == 0 :
self.Wee_evo=np.zeros((self.N_e,num_snaps))
else:
self.Wee_evo=np.zeros((len(evo_idxs),self.N_e,num_snaps))
self.mean_rr_evo=np.zeros(num_snaps)
### ============= LEARNING SPEED WEIGHTS ===================================
elif learn_speed_weights is True:
# we cannot learn speed weights with speed input
assert(self.use_speed_input is False)
print 'Learning speed weights with random walk'
self.W_speed_east_evo=np.zeros((self.N_e,num_snaps))
# target mean weight for input and output connections
self.W_av_star=(np.float(self.W_max_e)*self.num_conns_ee)/self.N_e
# initialize speed weights to zero
# row 1 has the weights of speed cells to grid cell 1
self.W_speed_east=np.zeros_like(self.W_ee)
self.W_speed_west=np.zeros_like(self.W_ee)
self.W_speed_north=np.zeros_like(self.W_ee)
self.W_speed_south=np.zeros_like(self.W_ee)
if self.use_eight_directions is True:
self.W_speed_north_east=np.zeros_like(self.W_ee)
self.W_speed_north_west=np.zeros_like(self.W_ee)
self.W_speed_south_east=np.zeros_like(self.W_ee)
self.W_speed_south_west=np.zeros_like(self.W_ee)
### ============= RUN DYNAMICS WITHOUT LEARNING ============================
else:
print 'Recurrent dynamics with random walk'
print 'use_speed_input: %s'%self.use_speed_input
self.num_walk_steps = int(walk_time/self.dt)
# rate at which we shall update the position (there is the option to interpolate inputs between updates)
if position_dt is None:
self.position_dt=self.L/self.nx/self.speed
else:
self.position_dt=position_dt
self.pos_dt_scale=int(self.position_dt/self.dt)
self.walk=RandomWalk(sl.map_merge(self.paramMap,{ 'walk_time':walk_time,
'position_dt':self.position_dt,
'theta_sigma':theta_sigma,
'sweep':sweep,
'init_p':init_p,
'init_theta':init_theta,
'periodic_walk':periodic_walk,
'speed':self.walk_speed,
}),
force=force_walk,
#init_p=init_p,
#init_theta=init_theta,
)
self.delta_snap = int(np.floor(float(self.num_walk_steps)/(num_snaps)))
assert(self.delta_snap>0)
print 'pos_dt_scale: %d'%self.pos_dt_scale
print 'delta_snap: %d'%self.delta_snap
self.start_clock=time.time()
self.startTime=datetime.datetime.fromtimestamp(time.time())
self.startTimeStr=self.startTime.strftime('%Y-%m-%d %H:%M:%S')
# initializations
self.snap_idx=0
self.walk_step_idx=0
self.rr=np.zeros((self.N,1))
self.start_clock=time.time()
self.r_e_walk_map=np.zeros((self.N_e,self.NX))
self.visits_map=np.zeros(self.NX)
if self.use_speed_input or learn_speed_weights:
self.speed_inputs_east=np.zeros(self.N_e)
self.speed_inputs_west=np.zeros(self.N_e)
self.speed_inputs_north=np.zeros(self.N_e)
self.speed_inputs_south=np.zeros(self.N_e)
if self.use_eight_directions is True:
self.speed_inputs_north_east=np.zeros(self.N_e)
self.speed_inputs_north_west=np.zeros(self.N_e)
self.speed_inputs_south_east=np.zeros(self.N_e)
self.speed_inputs_south_west=np.zeros(self.N_e)
self.tot_speed_input_all_padded=np.zeros((self.N,1))
if track_bump_evo is True:
self.bump_peak_evo=np.zeros((2,num_snaps))
self.bump_hh_peak_evo=np.zeros((2,num_snaps))
self.bump_evo=np.zeros((self.N_e,num_snaps))
self.bump_hh_evo=np.zeros((self.N_e,num_snaps))
self.bump_rec_evo=np.zeros((self.N_e,num_snaps))
self.bump_speed_evo=np.zeros((self.N_e,num_snaps))
if track_cell_evo is True:
self.cell_rr_evo=np.zeros(num_snaps)
self.cell_hh_evo=np.zeros(num_snaps)
self.cell_rec_input_evo=np.zeros(num_snaps)
self.cell_rec_input_from_e_evo=np.zeros(num_snaps)
self.cell_rec_input_from_i_evo=np.zeros(num_snaps)
pos_idx=-1
curr_p=self.walk.pos[pos_idx]
self.hh_e=self.h_e[:,pos_idx]
self.hh_i=self.h_i[:,pos_idx]
# feed-forward input vector
self.hh=np.zeros((self.N_e+self.N_i,1))
self.next_hh=np.zeros((self.N_e+self.N_i,1))
tot_input=np.zeros_like(self.hh)
filtered_tot_input=np.zeros_like(self.hh)
# run the simulation
for step_idx in xrange(self.num_walk_steps):
#print 'step_idx: %d'%step_idx
if self.fixed_position is False:
# ==== start of updating rat position ===============================
if np.remainder(step_idx,self.pos_dt_scale)==0:
#print 'updating position, interpolate_inputs=%d'%interpolate_inputs
# if we are at the end of the walk we start again
if self.walk_step_idx>=self.walk.walk_steps:
self.walk_step_idx=0
# read inputs at this walk step
new_pos_idx= self.walk.pidx_vect[self.walk_step_idx]
# the position has really changed from the last walk step
# note that the position could still be the same because the rat moved less
# than the discretization step used for space, that is, L/nx.
# on straight trajectories position shall update every L/nx/speed
if not (new_pos_idx == pos_idx):
pos_idx=new_pos_idx
new_p=self.walk.pos[pos_idx]
# with speed input or learning speed weights we need to update current direction
if self.use_speed_input is True or learn_speed_weights is True:
if step_idx>0:
dp=new_p-curr_p
# if we are changing position update current direction
if not (dp[0]==0. and dp[1]==0):
curr_v=dp
self.update_speed_input_step(curr_v)
# compute total weighted speed inputs to add to the recurrent dynamics
if self.use_speed_input is True:
self.update_total_speed_input_step(curr_v)
# update speed weights
if learn_speed_weights is True:
self.update_speed_weights_step()
curr_p=new_p
# update the input at this position
self.hh[:self.N_e,0]=self.h_e[:,pos_idx]
self.hh[self.N_e:,0]=self.h_i[:,pos_idx]
# get the inputs at the next different position for interpolation
if interpolate_inputs:
j=1
while(j<1000):
# get inputs at the next different position (for interpolation)
next_walk_step_idx=self.walk_step_idx+j
if next_walk_step_idx>=self.walk.walk_steps:
next_walk_step_idx=0
next_pos_idx=self.walk.pidx_vect[next_walk_step_idx]
if not(next_pos_idx == pos_idx):
break
j+=1
# we found the next position
if j<1000:
self.next_hh[:self.N_e,0]=self.h_e[:,next_pos_idx]
self.next_hh[self.N_e:,0]=self.h_i[:,next_pos_idx]
# the next different position is j walk steps away
hh_increment=(self.next_hh-self.hh)/(self.pos_dt_scale*j)
# we did not find it, no increment
else:
hh_increment=np.zeros_like(self.hh)
else:
if interpolate_inputs:
### the position was the same -> interpolate
self.hh+=hh_increment