+++ /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