<jupyter_start><jupyter_text>Example 1This is an example that reproduces Figures 2,3 from the main text.We generate synthetic data from the ramping dynamics (with linear potential), visualize the generated spikes and latent trajectories, and optimize a model on the generated data. We also demonstrate the importance of various non-stationary components for accurate model inference.<jupyter_code>import neuralflow
import numpy as np
import matplotlib.pyplot as plt, matplotlib.gridspec as gridspec
from neuralflow.utilities.visualization_functions import plot_spikes, plot_fitting_results
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)<jupyter_output><empty_output><jupyter_text>Step 1: Generate synthetic data1) Specify the ground-truth model for data generation, see the implementation of the `EnergyModel` class for available options. As described in the paper, we generate the data from the ramping dynamics, with noise magnitude $D=0.56$, and narrow zero-centered initial distribution of the latent states. We use absorbing boundary mode so that each trial ends at the decision boundary. All these parameters are specified at the initialization of EnergyModel class.2) Specify additional data generation parameters. Here we will generate 200 trials of data, each trial will have 100 seconds maximum duration (however, all of them will terminate earlier due to absorption at the decision boundary, with the average trial duration time less than 1 second). We will use time step `deltaT = 0.0001` for the Euler–Maruyama numerical integration of the Langevin equation. We also set the parameter `last_event_is_spike` to False in order to record the trial termination time.  3) Perform data generation4) (OPTIONAL) Visualize latent trajectories and spike rasters of the generated data. First of all, for each of the 200 generated latent trajectories, find the termination side (left or right boundary). Select 3 example trials where the latent trajectory terminated on the left, and 6 trials where the latent trajectory terminated on the right boundary. Plot latent trajectories and the corresponding spike rasters for the example nine trials.<jupyter_code>EnergyModelParams={'pde_solve_param':{'method':{'name': 'SEM', 'gridsize': {'Np': 8, 'Ne': 64}}}, 
               'Nv': 447,  
               'peq_model':{"model": "linear_pot", "params": {"slope": -2.65}},
               'D0': 0.56,
               'p0_model':{"model": "single_well", "params": {"miu": 200, "xmin": 0}},
               'boundary_mode':'absorbing', 
               'num_neuron':1,
               'firing_model':[{"model": "linear", "params": {"r_slope": 50, "r_bias": 60}}],
               'verbose':True
               }
em_gt=neuralflow.EnergyModel(**EnergyModelParams)

data_gen_params={'deltaT':0.0001, 'time_epoch':  [(0,100)]*200, 'last_event_is_spike':False}

data, time_bins, diff_traj, metadata=em_gt.generate_data(**data_gen_params)
#(OPTIONAL): Visualize the latent trajectories and spike rasters of the generated data
fig=plt.figure(figsize=(20,5))
gs=gridspec.GridSpec(1,2,wspace=0.5)
colors=[[0, 0.5804, 0.5922], [0.0353, 0.5216, 0.5569], [0.0667, 0.4745, 0.5255],
[0.1137, 0.4000, 0.4784], [0.1569, 0.3373, 0.4353], [0.1922, 0.2706, 0.4000],
[0.2275, 0.2235, 0.3686], [0.2824, 0.2000, 0.3922], [0.3608, 0.1804, 0.4118]]
colors.reverse()



#Find where each of the latent trajectories teminate (1 if the right boundary, -1 if the left boundary):
bnd=np.array([1 if diff_traj[i][-1]>0 else -1 for i in range(len(data_gen_params['time_epoch']))])

# Take 3 example trajectories that terminate on the left boundary, and 6 that terminate on the right boundary
if np.where(bnd==-1)[0].size<3:
    print('Due to bad luck there are less than 3 trials out of 200 that terminate on the left boundary. Please rerun the previous cell to generate new data, and then rerun this cell')
traj_indeces=np.concatenate((np.where(bnd==-1)[0][:3],np.where(bnd==1)[0][:6]))

#Plot latent trajectories
ax = plt.subplot(gs[0])
for i in range(9):
    plt.plot(time_bins[traj_indeces[i]]*1000,diff_traj[traj_indeces[i]],color=colors[i])
plt.xlabel('Time (ms)',fontsize=15)
plt.ylabel(r'Latent states, $x$',fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=15)

ax = plt.subplot(gs[1])

#Plot spikes
plot_spikes(data[list(traj_indeces)],ax,colors)

plt.xlabel('Time (ms)',fontsize=15)
plt.ylabel('Trials',fontsize=15)
ax.tick_params(axis='both', which='major', labelsize=15)<jupyter_output><empty_output><jupyter_text>The code above should produce the following image:![Fig3_1](images/Example1_1.png)  Step 2: Optimize model potential using the generated data 1) Specify the EnergyModel variable for fitting. All parameters are the same as the ground-truth, but the initial guess for potential is the constant potential (uniform peq model). To accelerate the evaluation of this cell, we also reduce grid resolution.2) Specify options. Here we want to optimize the model with all of the non-stationary components: absorbing boundary mode, with the correct p0 distribution, and with the absorption operator (`absorption_event=True`). To accelerate the computation, we set max_iteration to 50, however, you may want to increase it.3) Optimize the model with the selected parameters.4) Visualize the results.<jupyter_code>EnergyModelParams={'pde_solve_param':{'method':{'name': 'SEM', 'gridsize': {'Np': 8, 'Ne': 16}}}, 
               'Nv': 111,  
               'peq_model':{"model": "uniform", "params": {}},
               'D0': 0.56,
               'p0_model':{"model": "single_well", "params": {"miu": 200, "xmin": 0}},
               'boundary_mode':'absorbing', 
               'num_neuron':1,
               'firing_model':[{"model": "linear", "params": {"r_slope": 50, "r_bias": 60}}],
               'verbose':True
               }
em_fit=neuralflow.EnergyModel(**EnergyModelParams)

options={}
options['data']={'dataTR':data}
options['save']=None
options['inference']={'metadataTR':{'last_event_is_spike':False,'absorption_event':True}}
options['optimization']={'gamma':{'F':0.005},'max_iteration':50,'loglik_tol':0,'etaf':0}

em_fit.fit('GD',options);
#Visualise the results
fig=plt.figure(figsize=(20,5))
gs=gridspec.GridSpec(1,2,wspace=0.5)
colors=[[0.9294, 0.5608, 0.6157], [0.9686, 0.3765, 0.4745], [1.0000, 0.0235, 0.3176]]

#Plot the loglikelihood on iterations 4,17 and the one where fitted loglik matches the ground-truth one
ll_gt=em_gt.score(options['data']['dataTR'],metadata=options['inference']['metadataTR'])
rel_lls = (ll_gt-em_fit.iterations_GD_['logliks'])/ll_gt
example_iterations=[4,17,np.where(rel_lls<0)[0][0] if np.where(rel_lls<0)[0].size>0 else rel_lls.size-1]
plot_fitting_results(gs[0],gs[1],em_fit,em_gt,options,example_iterations,colors)<jupyter_output><empty_output><jupyter_text>The code above should produce the following image (note that 200 trials is relatively small amount of data, so the fitted potential may differ from the ground-truth near the boundaries):![Fig3_2](images/Example1_2.png)  Step 3: Testing the contributions of non-stationary componentsHere we will use the same data as in the previous step to demonstrate the importance of various non-stationary components. Note that the purpose of this task is to demonstrate the deterioration of the fitting results when one or more non-stationary components are missing (and, therefore, this setting should not be used for data analysis when the goal is inferring the correct model).1) Same as in Step 2, but the inference is performed without the absorption operator.2) Same as in Step 2, but the inference is performed with reflective boundary conditions (and without the absorption operator).3) Same as in Step 2, but the inference is performed with reflective boundary conditions, and starting from the equilibrium probability distribution density of the latent states.Visualize the results. Note that Figure 3 in the main text was generated under slightly different ground-truth model parameters so that the position of the ground-truth loglikelihood (dashed line) can differ. However, the spurious features of the potential are qualitatively similar to Figure 3 in the paper.<jupyter_code>#Create a big figure to plot all the results
fig=plt.figure(figsize=(20,30))
gs=gridspec.GridSpec(4,2,wspace=0.5,hspace=0.8);
#Plot the results from the previous cell
plot_fitting_results(gs[0],gs[1],em_fit,em_gt,options,example_iterations,colors)
fig.text(0.5,0.9,'The inference with all of the non-stationary components',fontsize=18,horizontalalignment='center',fontweight='bold')

#Optimize the model without the absorption operator
options['inference']['metadataTR']['absorption_event']=False
print('Starting optimization 1 (no absorption operator)...')
em_fit=neuralflow.EnergyModel(**EnergyModelParams)
em_fit.fit('GD',options)
plot_fitting_results(gs[2],gs[3],em_fit,em_gt,options,example_iterations,colors)
fig.text(0.5,0.69,'The inference without the absorption operator',fontsize=18,horizontalalignment='center',fontweight='bold')

EnergyModelParams['boundary_mode']='reflecting'
print('Starting optimization 2 (no absorption operator, reflecting BCs)...')
em_fit=neuralflow.EnergyModel(**EnergyModelParams)
em_fit.fit('GD',options)
plot_fitting_results(gs[4],gs[5],em_fit,em_gt,options,example_iterations,colors)
fig.text(0.5,0.48,'The inference without the absorption operator, and with the reflecting BCs',fontsize=18,horizontalalignment='center',fontweight='bold')

EnergyModelParams['p0_model']=None
print('Starting optimization 3 (no absorption operator, reflecting BCs, trials start at equilibrium)...')
em_fit=neuralflow.EnergyModel(**EnergyModelParams)
em_fit.fit('GD',options)
plot_fitting_results(gs[6],gs[7],em_fit,em_gt,options,example_iterations,colors)
fig.text(0.5,0.27,'The inference without the absorption operator, reflecting BCs, and with equilibrium probability distribution of the initial latent states',fontsize=18,horizontalalignment='center',fontweight='bold')<jupyter_output><empty_output>
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