Spectrally specific temporal analyses of spike-train responses to complex sounds: A unifying framework

Abstract

Significant scientific and translational questions remain in auditory neuroscience surrounding the neural correlates of perception. Relating perceptual and neural data collected from humans can be useful; however, human-based neural data are typically limited to evoked far-field responses, which lack anatomical and physiological specificity. Laboratory-controlled preclinical animal models offer the advantage of comparing single-unit and evoked responses from the same animals. This ability provides opportunities to develop invaluable insight into proper interpretations of evoked responses, which benefits both basic-science studies of neural mechanisms and translational applications, e.g., diagnostic development. However, these comparisons have been limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike trains and evoked responses, which results because these response types are fundamentally different (point-process versus continuous-valued signals) even though the responses themselves are related. Here, we describe a unifying framework to study temporal coding of complex sounds that allows spike-train and evoked-response data to be analyzed and compared using the same advanced signal-processing techniques. The framework uses a set of peristimulus-time histograms computed from single-unit spike trains in response to polarity-alternating stimuli to allow advanced spectral analyses of both slow (envelope) and rapid (temporal fine structure) response components. Demonstrated benefits include: (1) novel spectrally specific temporal-coding measures that are less confounded by distortions due to hair-cell transduction, synaptic rectification, and neural stochasticity compared to previous metrics, e.g., the correlogram peak-height, (2) spectrally specific analyses of spike-train modulation coding (magnitude and phase), which can be directly compared to modern perceptually based models of speech intelligibility (e.g., that depend on modulation filter banks), and (3) superior spectral resolution in analyzing the neural representation of nonstationary sounds, such as speech and music. This unifying framework significantly expands the potential of preclinical animal models to advance our understanding of the physiological correlates of perceptual deficits in real-world listening following sensorineural hearing loss.

Understanding degraded speech leads to perceptual gating of a brainstem reflex in human listeners

“…Here, we took advantage of the MAP_BS’s output to present stimuli of opposing polarity and compare coincident events in the resulting stochastic spike trains. Consequently, by extracting and quantifying these envelope-sensitive components with sumcor analysis ( Fig 2C and 2E ), we could identify how envelope encoding was affected by the suppression of cochlear gain for differently degraded words [ 73 – 76 ] and provide a coherent rationale for our experimental observations ( Fig 2E and 2F ). The ASR, on the other hand, has been previously used as a proxy for human hearing, predicting the intelligibility of test digit sequences from their AN spike probabilities using a hidden Markov model [ 58 – 60 , 62 , 139 ].…”

“…Sumcor Test Nat is the average of the SCC (Average of normalised all-order histograms for Nat+ versus Test+ and Nat- versus Test-) and the cross-polarity correlogram (Average of normalised all-order histograms for Nat- versus Test+ and Nat+ versus Test-) between natural and Voc8/BN5/SSN3 conditions. All high-frequency oscillations (> characteristic frequency of AN fibre), associated with fine-structure leakage, were removed from sumcor s [ 73 , 76 ]. ρENV values ranged from 0 to 1 where 0 represents completely dissimilar spike trains and 1 represents identical spike patterns [ 50 , 73 ].…”

“… Changes to TFS encoding were calculated for masked speech (not for noise-vocoded stimuli given their scrambled fine structure [ 50 , 73 , 76 , 178 ]) using Natural conditions with MOC reflex as control templates to calculate both ρTFS AN and ρTFS MOC . Adding the MOC reflex produced a mean improvement in TFS encoding for both BN5 and SSN3 (mean ΔρTFS for BN5 for freqs >1.5 kHz = 0.31 ± 0.16%, [Z(99) = 2.61, p = 0.009, r = 0.26]); mean ΔρENV for SSN3 >1.5 kHz = 1.06 ± 0.15, [Z (99) = 5.95, p < 0.001, r = 0.59]).…”

Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength

“…A large CI value indicates highly reproducible spiking responses over repeated trials (Joris et al, 2006 ). In contrast to VS, which is narrowband (i.e., only the spectral component at frequency f is considered), CI is generally broadband, as it is a collective measure of multiple frequency components (Parida et al, 2021 ). SACs and CIs can be applied to both periodic and aperiodic signals and were used for characterizing the temporal coding properties of auditory nerve (AN) fibers (Dreyer and Delgutte, 2006 ; Heinz and Swaminathan, 2009 ; Huet et al, 2018 ; Heeringa et al, 2019 ), various neurons in the cochlear nuclei recorded in vivo (Gai and Carney, 2008 ; Steinberg and Peña, 2011 ; Recio-Spinoso, 2012 ; Keine et al, 2016 ) and in vitro (Street and Manis, 2007 ; Kreeger et al, 2012 ), and neurons in the auditory midbrain (Shackleton et al, 2009 ; Zheng and Escabí, 2013 ).…”

“…Previous analyses of physiological data suggested that they should covary according to some monotonic, non-linear relationship (Dreyer and Delgutte, 2006 ; Joris et al, 2006 ). Parida and colleagues mathematically related peristimulus time histograms (PSTHs) to VS and CI under very general conditions (Parida et al, 2021 ). In the present study, we derive a mathematically rigorous relationship between VS and CI under certain assumptions for phase-locking.…”