--- /dev/null
+'''
+Peter Duggins, Terry Stewart, Xuan Choo, Chris Eliasmith
+Effects of Guanfacine and Phenylephrine on a Spiking Neuron Model of Working Memory
+June-August 2016
+Main Model File
+'''
+
+def run(params):
+ import nengo
+ from nengo.rc import rc
+ import numpy as np
+ import pandas as pd
+ from helper import reset_gain_bias, primary_dataframe, firing_dataframe
+
+ decision_type=params[0]
+ drug_type=params[1]
+ drug = params[2]
+ trial = params[3]
+ seed = params[4]
+ P = params[5]
+ dt=P['dt']
+ dt_sample=P['dt_sample']
+ t_cue=P['t_cue']
+ t_delay=P['t_delay']
+ drug_effect_neural=P['drug_effect_neural']
+ drug_effect_functional=P['drug_effect_functional']
+ drug_effect_biophysical=P['drug_effect_biophysical']
+ enc_min_cutoff=P['enc_min_cutoff']
+ enc_max_cutoff=P['enc_max_cutoff']
+ sigma_smoothing=P['sigma_smoothing']
+ frac=P['frac']
+ neurons_inputs=P['neurons_inputs']
+ neurons_wm=P['neurons_wm']
+ neurons_decide=P['neurons_decide']
+ time_scale=P['time_scale']
+ cue_scale=P['cue_scale']
+ tau=P['tau']
+ tau_wm=P['tau_wm']
+ noise_wm=P['noise_wm']
+ noise_decision=P['noise_decision']
+ perceived=P['perceived']
+ cues=P['cues']
+
+ if drug_type == 'biophysical': rc.set("decoder_cache", "enabled", "False") #don't try to remember old decoders
+ else: rc.set("decoder_cache", "enabled", "True")
+
+ def cue_function(t):
+ if t < t_cue and perceived[trial]!=0:
+ return cue_scale * cues[trial]
+ else: return 0
+
+ def time_function(t):
+ if t > t_cue:
+ return time_scale
+ else: return 0
+
+ def noise_bias_function(t):
+ import numpy as np
+ if drug_type=='neural':
+ return np.random.normal(drug_effect_neural[drug],noise_wm)
+ else:
+ return np.random.normal(0.0,noise_wm)
+
+ def noise_decision_function(t):
+ import numpy as np
+ if decision_type == 'default':
+ return np.random.normal(0.0,noise_decision)
+ elif decision_type == 'basal_ganglia':
+ return np.random.normal(0.0,noise_decision,size=2)
+
+ def inputs_function(x):
+ return x * tau_wm
+
+ def wm_recurrent_function(x):
+ if drug_type == 'functional':
+ return x * drug_effect_functional[drug]
+ else:
+ return x
+
+ def decision_function(x):
+ output=0.0
+ if decision_type=='default':
+ value=x[0]+x[1]
+ if value > 0.0: output = 1.0
+ elif value < 0.0: output = -1.0
+ elif decision_type=='basal_ganglia':
+ if x[0] > x[1]: output = 1.0
+ elif x[0] < x[1]: output = -1.0
+ return output
+
+ def BG_rescale(x): #rescales -1 to 1 into 0.3 to 1, makes 2-dimensional
+ pos_x = 0.5 * (x + 1)
+ rescaled = 0.4 + 0.6 * pos_x, 0.4 + 0.6 * (1 - pos_x)
+ return rescaled
+
+ '''model definition'''
+ with nengo.Network(seed=seed+trial) as model:
+
+ #Ensembles
+ cue = nengo.Node(output=cue_function)
+ time = nengo.Node(output=time_function)
+ inputs = nengo.Ensemble(neurons_inputs,2)
+ noise_wm_node = nengo.Node(output=noise_bias_function)
+ noise_decision_node = nengo.Node(output=noise_decision_function)
+ wm = nengo.Ensemble(neurons_wm,2)
+ decision = nengo.Ensemble(neurons_decide,2)
+ output = nengo.Ensemble(neurons_decide,1)
+
+ #Connections
+ nengo.Connection(cue,inputs[0],synapse=None)
+ nengo.Connection(time,inputs[1],synapse=None)
+ nengo.Connection(inputs,wm,synapse=tau_wm,function=inputs_function)
+ wm_recurrent=nengo.Connection(wm,wm,synapse=tau_wm,function=wm_recurrent_function)
+ nengo.Connection(noise_wm_node,wm.neurons,synapse=tau_wm,transform=np.ones((neurons_wm,1))*tau_wm)
+ wm_to_decision=nengo.Connection(wm[0],decision[0],synapse=tau)
+ nengo.Connection(noise_decision_node,decision[1],synapse=None)
+ nengo.Connection(decision,output,function=decision_function)
+
+ #Probes
+ probe_wm=nengo.Probe(wm[0],synapse=0.01,sample_every=dt_sample)
+ probe_spikes=nengo.Probe(wm.neurons, 'spikes', sample_every=dt_sample)
+ probe_output=nengo.Probe(output,synapse=None,sample_every=dt_sample)
+
+
+
+
+ '''SIMULATION'''
+ print 'Running drug \"%s\", trial %s...' %(drug,trial+1)
+ with nengo.Simulator(model,dt=dt) as sim:
+ if drug_type == 'biophysical': sim=reset_gain_bias(
+ P,model,sim,wm,wm_recurrent,wm_to_decision,drug)
+ sim.run(t_cue+t_delay)
+ df_primary=primary_dataframe(P,sim,drug,trial,probe_wm,probe_output)
+ df_firing=firing_dataframe(P,sim,drug,trial,sim.data[wm],probe_spikes)
+ return [df_primary, df_firing]
+
+
+
+'''MAIN'''
+def main():
+ import matplotlib.pyplot as plt
+ import seaborn as sns
+ import pandas as pd
+ import numpy as np
+ from helper import make_cues, empirical_dataframe
+ from pathos.helpers import freeze_support #for Windows
+ # import ipdb
+
+ '''Import Parameters from File'''
+ P=eval(open('parameters.txt').read()) #parameter dictionary
+ seed=P['seed'] #sets tuning curves equal to control before drug application
+ n_trials=P['n_trials']
+ drug_type=str(P['drug_type'])
+ decision_type=str(P['decision_type'])
+ drugs=P['drugs']
+ trials, perceived, cues = make_cues(P)
+ P['timesteps']=np.arange(0,int((P['t_cue']+P['t_delay'])/P['dt_sample']))
+ P['cues']=cues
+ P['perceived']=perceived
+
+ '''Multiprocessing'''
+ print "drug_type=%s, decision_type=%s, trials=%s..." %(drug_type,decision_type,n_trials)
+ freeze_support()
+ exp_params=[]
+ for drug in drugs:
+ for trial in trials:
+ exp_params.append([decision_type, drug_type, drug, trial, seed, P])
+ df_list=[run(exp_params[0]),run(exp_params[-1])]
+ primary_dataframe = pd.concat([df_list[i][0] for i in range(len(df_list))], ignore_index=True)
+ firing_dataframe = pd.concat([df_list[i][1] for i in range(len(df_list))], ignore_index=True)
+
+ '''Plot and Export'''
+ print 'Exporting Data...'
+ primary_dataframe.to_pickle('primary_data.pkl')
+ firing_dataframe.to_pickle('firing_data.pkl')
+ param_df=pd.DataFrame([P])
+ param_df.reset_index().to_json('params.json',orient='records')
+
+ print 'Plotting...'
+ emp_dataframe=empirical_dataframe()
+ sns.set(context='poster')
+ figure, (ax1, ax2) = plt.subplots(2, 1)
+ sns.tsplot(time="time",value="wm",data=primary_dataframe,unit="trial",condition='drug',ax=ax1,ci=95)
+ sns.tsplot(time="time",value="correct",data=primary_dataframe,unit="trial",condition='drug',ax=ax2,ci=95)
+ sns.tsplot(time="time",value="accuracy",data=emp_dataframe,unit='trial',condition='drug',
+ interpolate=False,ax=ax2)
+ sns.tsplot(time="time",value="accuracy",data=emp_dataframe, unit='trial',condition='drug',
+ interpolate=True,ax=ax2, legend=False)
+ ax1.set(xlabel='',ylabel='decoded $\hat{cue}$',xlim=(0,9.5),ylim=(0,1),
+ title="drug_type=%s, decision_type=%s, trials=%s" %(drug_type,decision_type,n_trials))
+ ax2.set(xlabel='time (s)',xlim=(0,9.5),ylim=(0.5,1),ylabel='DRT accuracy')
+ figure.savefig('primary_plots.png')
+
+ figure2, (ax3, ax4) = plt.subplots(1, 2)
+ if len(firing_dataframe.query("tuning=='weak'"))>0:
+ sns.tsplot(time="time",value="firing_rate",unit="neuron-trial",condition='drug',ax=ax3,ci=95,
+ data=firing_dataframe.query("tuning=='weak'").reset_index())
+ if len(firing_dataframe.query("tuning=='nonpreferred'"))>0:
+ sns.tsplot(time="time",value="firing_rate",unit="neuron-trial",condition='drug',ax=ax4,ci=95,
+ data=firing_dataframe.query("tuning=='nonpreferred'").reset_index())
+ ax3.set(xlabel='time (s)',xlim=(0.0,9.5),ylim=(0,250),ylabel='Normalized Firing Rate',title='Preferred Direction')
+ ax4.set(xlabel='time (s)',xlim=(0.0,9.5),ylim=(0,250),ylabel='',title='Nonpreferred Direction')
+ figure2.savefig('firing_plots.png')
+
+ plt.show()
+
+if __name__=='__main__':
+ main()
\ No newline at end of file
projects / norepinephrine_wm.git / blobdiff