analysis.snr¶
SNRAnalyzer
¶

class
nitime.analysis.snr.
SNRAnalyzer
(input=None, bandwidth=None, adaptive=False, low_bias=False)¶ Bases:
nitime.analysis.base.BaseAnalyzer
Calculate SNR for a response to repetitions of the same stimulus, according to (Borst, 1999) (Figure 2) and (Hsu, 2004).
Hsu A, Borst A and Theunissen, FE (2004) Quantifying variability in neural responses ans its application for the validation of model predictions. Network: Comput Neural Syst 15:91109
Borst A and Theunissen FE (1999) Information theory and neural coding. Nat Neurosci 2:947957
Attributes
Methods

__init__
(input=None, bandwidth=None, adaptive=False, low_bias=False)¶ Initializer for the multi_taper_SNR object
Parameters: input: TimeSeries object
bandwidth: float,
The bandwidth of the windowing function will determine the number tapers to use. This parameters represents tradeoff between frequency resolution (lower main lobe bandwidth for the taper) and variance reduction (higher bandwidth and number of averaged estimates). Per default will be set to 4 times the fundamental frequency, such that NW=4
adaptive: bool, default to False
Whether to set the weights for the tapered spectra according to the adaptive algorithm (Thompson, 2007).
low_bias : bool, default to False
Rather than use 2NW tapers, only use the tapers that have better than 90% spectral concentration within the bandwidth (still using a maximum of 2NW tapers)
Notes
Thompson, DJ (2007) Jackknifing multitaper spectrum estimates. IEEE Signal Processing Magazing. 24: 2030

correlation
()¶ The correlation between all combinations of trials
Returns: (r,e) : tuple
r is the mean correlation and e is the mean error of the correlation (with df = n_trials  1)

mt_coherence
()¶

mt_frequencies
()¶

mt_information
()¶

mt_noise_psd
()¶

mt_signal_psd
()¶

mt_snr
()¶


nitime.analysis.snr.
signal_noise
(response)¶ Signal and noise as defined in Borst and Theunissen 1999, Figure 2
Parameters: response: nitime TimeSeries object
The data here are individual responses of a single unit to the same stimulus, with repetitions being the first dimension and time as the last dimension