analysis.snr¶
Module: analysis.snr
¶
Inheritance diagram for nitime.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 15:91-109
Borst A and Theunissen FE (1999) Information theory and neural coding. Nat Neurosci 2:947-957
- __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 trade-off 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: 20-30
- 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