Can I limit the spike amplitude around 100uV while keeping the noise to be far neurons with 40-300uV?

[ZoeChen96]

Hi there, I have a question about dataset generation. I used to generate spikes with amplitude 40-300uV templates and with far neuron noises based on these templates. However, to validate the results better, I want to generate spikes with an amplitude of only around 100uV. In that case, I can't choose the 'far neurons noise model, because we don't have too many templates restricted to around 100uV ( I assume). Is that any possibility to set the spike amplitude and noise neuron amplitude separately? I will also attach the script now for generating a single channel, ~100uV spikes (3 spike classes). Thanks a lot!

[alejoe91]

Hi @ZoeChen96 Currently it's not possible, but I think it would be a good idea to allow to select different template files for the "recorded" neurons and the "noise" neurons.

[ZoeChen96]

import MEArec as mr
import numpy as np
import scipy.io
import spikeinterface.extractors as se
import warnings
from pprint import pprint
import sys
warnings.simplefilter("ignore")
####################################parameters-Bernardo's detection dataset global settings######################################
probe       = 'monotrode'
fs          = 30000
n_exc       = 0
n_inh       = 3
duration    = 3600 #seconds
filtering   = False #filtering True/false
ref_per     = 7
noise_type  = 'uncorrelated' #uncorrelated/distance-correlated/far-neurons
####################################################################################
original_stdout = sys.stdout # Save a reference to the original standard output
dir_full_templates = '/ia/templates300_32kHz_minampnull_'+ probe +'.h5'


tempgen = mr.load_templates(dir_full_templates)
recording_params = mr.get_default_recordings_params()
# Set parameters
recording_params['spiketrains']['n_exc'] = n_exc
recording_params['spiketrains']['n_inh'] = n_inh

recording_params['spiketrains']['st_inh'] = 0
recording_params['spiketrains']['min_rate'] = 0.1

recording_params['spiketrains']['seed'] = 0
recording_params['spiketrains']['ref_per'] = ref_per

recording_params['templates']['min_amp'] = 90
recording_params['templates']['max_amp'] = 110
recording_params['templates']['seed'] = 0

recording_params['recordings']['modulation'] = 'none'
recording_params['recordings']['sdrand'] = 0
recording_params['recordings']['noise_mode'] = noise_type
recording_params['recordings']['noise_color'] = True
recording_params['recordings']['sync_rate'] = 0

# use chunk options
recording_params['recordings']['chunk_conv_duration'] = 20
recording_params['recordings']['chunk_noise_duration'] = 20
recording_params['recordings']['chunk_filter_duration'] = 20
recording_params['recordings']['seed'] = 0

#filter settings
recording_params['recordings']['fs'] = fs
recording_params['recordings']['filter'] = filtering
recording_params['recordings']['filter_cutoff'] = [300, 6000] # highpass from 0

#seeds settings
recording_params['seeds']['spiketrains'] = 0
recording_params['seeds']['templates'] = 0
recording_params['seeds']['convolution'] = 0
recording_params['seeds']['noise'] = 0


#################################### set looping parameters and generate datasets ######################################
noise          = [10,15,20]
spiking_rate = [120,150]
duration = [600,600]
#spiking_rate   = [1,10,20,30,40]
#duration = [3600,1800,900,600,600]
for i in noise:
    counter = 0
    for j in spiking_rate:
        print('generating recording for noise', str(i),' spiking rate',str(j),' neuron number',str(n_inh), 'duration' ,str(duration[counter]) )
        recording_params['recordings']['noise_level'] = i
        recording_params['spiketrains']['f_inh'] = j/n_inh
        recording_params['spiketrains']['duration'] = duration[counter]
        set_name = 'n'+str(i) + '_fr'+str(j)+'_d'+str(duration[counter])
        
        dir_full_recordings = '/imec/other/macaw/projectdata/mearec_datasets/detection_datasets/set'+ set_name + '/set' + set_name + '_recording.h5'
        dir_full_gttimes = '/a/set'+ set_name + '/set' + set_name + '_gttimes.mat'
        filename =         '/a+set_name  + '/set' + set_name +'_info.txt'
        recgen = mr.gen_recordings(templates=dir_full_templates, params=recording_params, verbose=False)
        # save recording
        mr.save_recording_generator(recgen, dir_full_recordings)
        #save gt_times
        if (n_exc+n_inh >0):
            sorting_GT = se.MEArecSortingExtractor(dir_full_recordings)
            scipy.io.savemat(dir_full_gttimes, {'gt_times': sorting_GT.to_spike_vector()})
        with open(filename, "w") as f:    
            sys.stdout = f # Change the standard output to the file we created. 
            pprint(recgen.info)
        sys.stdout = original_stdout
        counter = counter+1