Spectro-temporal receptive fields of midbrain auditory neurons in the rat obtained with frequency modulated stimulation
Section snippets
Acknowledgements
This work was supported in part by National Science Council (NSC 2320 B006 042), and Ministry of Education (Academic Excellence 89-B-FA08-1-4), Taiwan, Republic of China. The technical help of T.W. Chiu is gratefully acknowledged.
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Cited by (23)
Multiscale mapping of frequency sweep rate in mouse auditory cortex
2017, Hearing ResearchCitation Excerpt :In addition, a prior microelectrode study observed a preference for frequency modulations in UF (Stiebler et al., 1997), which may at least partially overlap with CSR. It is tempting to speculate about the relation of CSR to FM areas found in other species, such as the FM-FM area of bats (Suga et al., 1983), but first a deeper understanding of the neuronal computations producing FM selectivity (Sadagopan and Wang, 2009) and connectivity patterns between CSR and subcortical regions (Clopton and Winfield, 1974; Poon and Yu, 2000) are needed. Selectivity for FM sweep rate and direction could be inherited primarily from subcortical inputs (Covey and Casseday, 1999) or computed through intracortical circuits (Zhang et al., 2003).
Modeling complex responses of FM-sensitive cells in the auditory midbrain using a committee machine
2013, Brain ResearchCitation Excerpt :To generate an STRF, the instantaneous frequency time-profiles of the FM stimulus within a 40 ms pre-spike time window were added according to the conventional procedure. Specifically, on the spectro–temporal plane formed by a minimum of 126×201 pixels, the count of all the stimulus frequency time-profile passing through each pixel was registered to show the concentration of peri-spike sound energy (Poon and Yu, 2000). To better reveal the trigger features in the STRF, we applied a preprocessing procedure called ‘progressive thresholding’ (Chang et al., 2010b) followed by ‘de-jittering’ (Chang et al., 2005).
Modeling frequency modulated responses of midbrain auditory neurons based on trigger features and artificial neural networks
2012, Brain ResearchCitation Excerpt :Often the exact pattern of trigger features depends on the property of stimuli used to generate the STRF (Valentine and Eggermont, 2004). The host of stimuli used to generate STRFs ranges from random tonal stimuli to naturally-occurring complex sounds (e.g., Escabı́ and Schreiner, 2002; Poon and Yu, 2000; Theunissen et al., 2000). No attempt has yet been made to incorporate temporal information from STRF into an artificial neural network in predicting FM responses.
Should spikes be treated with equal weightings in the generation of spectro-temporal receptive fields?
2010, Journal of Physiology Paris