Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex.

By Didier A. Depireux, Jonathan Z. Simon, David J. Klein, Shihab A. Shamma. 2001

459 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

Normalization as a canonical neural computation (2011, neutral): “k m σ n + (Σk αjk I )p (Σk wjk Ik) + β (12)”
Rapid task-related plasticity of spectrotemporal receptive fields in primary auditory cortex (2003, methodology): “These stimuli were specifically designed to characterize the STRF of the cell under study in physiological experiments using the reverse-correlation metho”
Speaker Normalization in Speech Perception (2008, background): “Research on auditory processing in animals has demonstrated that patches of tissue in human auditory cortex display tuning for specific combinations of spectral and temporal modulation frequencies (e.g., Depireux et al., 2001; Wooley et al., 2005; Nagel & Doupe, 2008).”
The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli (2016, background): “Indeed, it is possible to deﬁne an mTRF that linearly maps a multivariate stimulus feature to each recording channel (Theunissen et al., 2000; Depireux et al., 2001).”
Nonlinearities and Contextual Influences in Auditory Cortical Responses Modeled with Multilinear Spectrotemporal Methods (2008, unknown): “Time-frequency inseparabilities in the STRF have been studied extensively (Kowalski et al., 1996a,b; Depireux et al., 2001; Linden et al., 2003) and related properties (such as temporal symmetry) (Simon et al., 2007) have been documented previously.”
Emergence of neural encoding of auditory objects while listening to competing speakers (2012, methodology): “To characterize the neural code that the decoder extracts information from, we analyze the neural encoding process via the spectrotemporal response function (STRF) for each MEG sensor (26, 27).”
Neural processing of natural sounds (2014, background): “progress in understanding the nature of the auditory computations that are found in the ascending processing stream of both birds [30,41,48,51,58-61] and mammals [38,62-65].”
Automatic speech emotion recognition using modulation spectral features (2011, background): “…advances in neuroscience suggest the existence of spectrotemporal (ST) receptive fields in mammalian auditory cortex which can extend up to temporal spans of hundreds of milliseconds and respond to modulations in the time-frequency domain (Depireux et al., 2001; Shamma, 2001; Chih et al., 2005).”
Multiresolution spectrotemporal analysis of complex sounds. (2005, background): “…in the more central structures of the Inferior Colliculus , Medial Geniculate Body, and the cortex have only recently begun to be uncovered [deRibaupierre and Rouiller, 1981, Kowalski et al., 1996, Schreiner and Urbas, 1988b, Miller et al., 2002, Lu et al., 2001, Eggermont, 2002, Ulanovsky…”