+++ /dev/null
-def make_addon(N):
- import string
- import random
- addon=str(''.join(random.choice(string.ascii_uppercase+string.digits) for _ in range(N)))
- return addon
-
-def ch_dir():
- #change directory for data and plot outputs
- import os
- import sys
- root=os.getcwd()
- addon=make_addon(9)
- datadir=''
- if sys.platform == "linux" or sys.platform == "linux2" or sys.platform == "darwin":
- datadir=root+'/data/'+addon+'/' #linux or mac
- elif sys.platform == "win32":
- datadir=root+'\\data\\'+addon+'\\' #windows
- os.makedirs(datadir)
- os.chdir(datadir)
- return datadir
-
-def empirical_dataframe():
- import numpy as np
- import pandas as pd
- columns=('time','drug','accuracy','trial')
- emp_times = [3.0,5.0,7.0,9.0]
- emp_dataframe = pd.DataFrame(columns=columns,index=np.arange(0, 12))
- pre_PHE=[0.972, 0.947, 0.913, 0.798]
- pre_GFC=[0.970, 0.942, 0.882, 0.766]
- post_GFC=[0.966, 0.928, 0.906, 0.838]
- post_PHE=[0.972, 0.938, 0.847, 0.666]
- q=0
- for t in range(len(emp_times)):
- emp_dataframe.loc[q]=[emp_times[t],'control (empirical)',np.average([pre_GFC[t],pre_PHE[t]]),0]
- emp_dataframe.loc[q+1]=[emp_times[t],'PHE (empirical)',post_PHE[t],0]
- emp_dataframe.loc[q+2]=[emp_times[t],'GFC (empirical)',post_GFC[t],0]
- q+=3
- return emp_dataframe
-
-def make_cues(P):
- import numpy as np
- trials=np.arange(P['n_trials'])
- perceived=np.ones(P['n_trials']) #list of correctly perceived (not necessarily remembered) cues
- rng=np.random.RandomState(seed=P['seed'])
- cues=2*rng.randint(2,size=P['n_trials'])-1 #whether the cues is on the left or right
- for n in range(len(perceived)):
- if rng.rand()<P['misperceive']: perceived[n]=0
- return trials, perceived, cues
-
-
-'''drug approximations'''
-import nengo
-class MySolver(nengo.solvers.Solver):
- #When the simulator builds the network, it looks for a solver to calculate the decoders
- #instead of the normal least-squares solver, we define our own, so that we can return
- #the old decoders
- def __init__(self,weights): #feed in old decoders
- self.weights=False #they are not weights but decoders
- self.my_weights=weights
- def __call__(self,A,Y,rng=None,E=None): #the function that gets called by the builder
- return self.my_weights.T, dict()
-
-def reset_gain_bias(P,model,sim,wm,wm_recurrent,wm_to_decision,drug):
- #set gains and biases as a constant multiple of the old values
- wm.gain = sim.data[wm].gain * P['drug_effect_biophysical'][drug][0]
- wm.bias = sim.data[wm].bias * P['drug_effect_biophysical'][drug][1]
- #set the solver of each of the connections coming out of wm using the custom MySolver class
- #with input equal to the old decoders. We use the old decoders because we don't want the builder
- #to optimize the decoders to the new gainbias/bias values, otherwise it would "adapt" to the drug
- wm_recurrent.solver = MySolver(sim.model.params[wm_recurrent].weights)
- wm_to_decision.solver=MySolver(sim.model.params[wm_to_decision].weights)
- #rebuild the network to affect the gain/bias change
- sim=nengo.Simulator(model,dt=P['dt'])
- return sim
-
-'''dataframe initialization'''
-def primary_dataframe(P,sim,drug,trial,probe_wm,probe_output):
- import numpy as np
- import pandas as pd
- columns=('time','drug','wm','output','correct','trial')
- df_primary = pd.DataFrame(columns=columns, index=np.arange(0,len(P['timesteps'])))
- i=0
- for t in P['timesteps']:
- wm_val=np.abs(sim.data[probe_wm][t][0])
- output_val=sim.data[probe_output][t][0]
- correct=get_correct(P['cues'][trial],output_val)
- rt=t*P['dt_sample']
- df_primary.loc[i]=[rt,drug,wm_val,output_val,correct,trial]
- i+=1
- return df_primary
-
-def firing_dataframe(P,sim,drug,trial,sim_wm,probe_spikes):
- import numpy as np
- import pandas as pd
- columns=('time','drug','neuron-trial','tuning','firing_rate')
- df_firing = pd.DataFrame(columns=columns, index=np.arange(0,len(P['timesteps'])*\
- int(P['neurons_wm']*P['frac'])))
- t_width = 0.2
- t_h = np.arange(t_width / P['dt']) * P['dt'] - t_width / 2.0
- h = np.exp(-t_h ** 2 / (2 * P['sigma_smoothing'] ** 2))
- h = h / np.linalg.norm(h, 1)
- j=0
- for nrn in range(int(P['neurons_wm']*P['frac'])):
- enc = sim_wm.encoders[nrn]
- tuning = get_tuning(P,trial,enc)
- spikes = sim.data[probe_spikes][:,nrn]
- firing_rate = np.convolve(spikes,h,mode='same')
- for t in P['timesteps']:
- rt=t*P['dt_sample']
- df_firing.loc[j]=[rt,drug,nrn+trial*P['neurons_wm'],tuning,firing_rate[t]]
- j+=1
- # print 'appending dataframe for neuron %s' %f
- return df_firing
-
-def get_correct(cue,output_val):
- if (cue > 0.0 and output_val > 0.0) or (cue < 0.0 and output_val < 0.0): correct=1
- else: correct=0
- return correct
-
-def get_tuning(P,trial,enc):
- cue=P['cues'][trial]
- enc_min_cutoff=P['enc_min_cutoff']
- enc_max_cutoff=P['enc_max_cutoff']
- if (cue > 0.0 and 0.0 < enc[0] < enc_min_cutoff) or \
- (cue < 0.0 and 0.0 > enc[0] > -1.0*enc_min_cutoff): tuning='superweak'
- if (cue > 0.0 and enc_min_cutoff < enc[0] < enc_max_cutoff) or \
- (cue < 0.0 and -1.0*enc_max_cutoff < enc[0] < -1.0*enc_min_cutoff): tuning='weak'
- elif (cue > 0.0 and enc[0] > enc_max_cutoff) or \
- (cue < 0.0 and enc[0] < -1.0*enc_max_cutoff): tuning='strong'
- else: tuning='nonpreferred'
- return tuning
\ No newline at end of file
projects / norepinephrine_wm.git / blobdiff