--- /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