An Improved Model for the Rate–Level Functions of Auditory-Nerve Fibers

Heil, Peter; Neubauer, Heinrich; Irvine, Dexter R. F.

doi:10.1523/jneurosci.1638-11.2011

Cited by 47 publications

(57 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The derivation is based on a probabilistic model of monaural absolute threshold and its dependence on stimulus duration and temporal envelope (Heil and Neubauer 2003;Heil 2004, 2008;Heil et al 2008Heil et al , 2011Heil et al , 2013bPohl et al 2013) and on the assumption of perfect binaural summation. Binaural thresholds measured in a sizable sample of subjects, much larger than in previous studies of this topic, and for a range of stimuli were compared with those predicted by the model for different values of the exponent.…”

Section: Discussionmentioning

confidence: 99%

“…The observed binaural advantages would then seem to require the early neural signals to be proportional to the stimulus amplitude raised to an exponent of 1.5. However, an exponent of about 3 governs the relevant behavior of auditory-nerve fibers, such as the rate-level function (Heil et al 2011) and the dependence of the first-spike latency on stimulus amplitude and temporal envelope Heil et al 2008). The data and model presented here might also help to improve models of binaural loudness summation (e.g., Whilby et al 2004;Moore and Glasberg 2007).…”

mentioning

confidence: 83%

“…Again, an exponent of 1.5 would have to be assumed to bring those data in line with the "integration model." However, early neural representations (at the level of the auditory nerve) of the type of stimuli used here are governed by an exponent of α=3, or one close to it Heil et al 2008Heil et al , 2011, with the intriguing possibility that this exponent results from the Ca 2+ cooperativity of the Ca 2+ sensor mediating fast exocytosis from inner hair cells (e.g., Heil and Neubauer 2010;Heil et al 2008Heil et al , 2011. Still, a dismissal of the "integration model" would be premature as long as the shapes of the psychometric functions are not known in detail.…”

Section: Relationships To Models For Combining Multiple Observationsmentioning

confidence: 99%

“…In contrast, estimates of its value by Heil and colleagues are close to 3 (Heil and Neubauer 2003;Neubauer and Heil 2004;Heil et al 2013b, Pohl et al 2013). This exponent likely originates in the auditory periphery, because detailed analyses show that an exponent of about 3 governs the growth of the mean spike rate of auditory-nerve fibers with stimulus amplitude (Heil et al 2011) and the dependence of the timing of the first spike on stimulus amplitude and onset envelope Heil et al 2008). An intriguing possibility is that the exponent results from the Ca 2+ sensor mediating exocytosis from inner hair cells, as discussed in detail elsewhere (Heil and Neubauer 2010;Heil et al 2011).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Towards a Unifying Basis of Auditory Thresholds: Binaural Summation

Heil

2014

JARO

View full text Add to dashboard Cite

Absolute auditory threshold decreases with increasing sound duration, a phenomenon explainable by the assumptions that the sound evokes neural events whose probabilities of occurrence are proportional to the sound's amplitude raised to an exponent of about 3 and that a constant number of events are required for threshold (Heil and Neubauer, Proc Natl Acad Sci USA 100: [6151][6152][6153][6154][6155][6156] 2003). Based on this probabilistic model and on the assumption of perfect binaural summation, an equation is derived here that provides an explicit expression of the binaural threshold as a function of the two monaural thresholds, irrespective of whether they are equal or unequal, and of the exponent in the model. For exponents 90, the predicted binaural advantage is largest when the two monaural thresholds are equal and decreases towards zero as the monaural threshold difference increases. This equation is tested and the exponent derived by comparing binaural thresholds with those predicted on the basis of the two monaural thresholds for different values of the exponent. The thresholds, measured in a large sample of human subjects with equal and unequal monaural thresholds and for stimuli with different temporal envelopes, are compatible only with an exponent close to 3. An exponent of 3 predicts a binaural advantage of 2 dB when the two ears are equally sensitive. Thus, listening with two (equally sensitive) ears rather than one has the same effect on absolute threshold as doubling duration. The data suggest that perfect binaural summation occurs at threshold and that peripheral neural signals are governed by an exponent close to 3. They might also shed new light on mechanisms underlying binaural summation of loudness.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 83%

Section: Relationships To Models For Combining Multiple Observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards a Unifying Basis of Auditory Thresholds: Binaural Summation

Heil

2014

JARO

View full text Add to dashboard Cite

show abstract

“…Here, we investigate whether detection of near-threshold auditory stimuli in quiet, whose underlying mechanisms are still debated (see, e.g., Viemeister and Wakefield, 1991; Eddins and Green, 1995; Heil and Neubauer, 2003; Verhey, 2010; Heil et al, 2011, 2013a,b; Meddis and Lecluyse, 2011; Pohl et al, 2013), depends on the phase of EEG oscillations in humans. Based on the aforementioned findings, we expected detection to vary with the phase of EEG oscillations, particularly in the δ-, θ-, and α-bands.…”

Section: Introductionmentioning

confidence: 99%

Detection of Near-Threshold Sounds is Independent of EEG Phase in Common Frequency Bands

Zoefel¹,

Heil²

2013

Front. Psychol.

View full text Add to dashboard Cite

Low-frequency oscillations in the electroencephalogram (EEG) are thought to reflect periodic excitability changes of large neural networks. Consistent with this notion, detection probability of near-threshold somatosensory, visual, and auditory targets has been reported to co-vary with the phase of oscillations in the EEG. In audition, entrainment of δ-oscillations to the periodic occurrence of sounds has been suggested to function as a mechanism of attentional selection. Here, we examine in humans whether the detection of brief near-threshold sounds in quiet depends on the phase of EEG oscillations. When stimuli were presented at irregular intervals, we did not find a systematic relationship between detection probability and phase. When stimuli were presented at regular intervals (2-s), reaction times were significantly shorter and we observed phase entrainment of EEG oscillations corresponding to the frequency of stimulus presentation (0.5 Hz), revealing an adjustment of the system to the regular stimulation. The amplitude of the entrained oscillation was higher for hits than for misses, suggesting a link between entrainment and stimulus detection. However, detection was independent of phase at frequencies ≥1 Hz. Furthermore, we show that when the data are analyzed using acausal, though common, algorithms, an apparent “entrainment” of the δ-phase to presented stimuli emerges and detection probability appears to depend on δ-phase, similar to reports in the literature. We show that these effects are artifacts from phase distortion at stimulus onset by contamination with the event-related potential, which differs markedly for hits and misses. This highlights the need to carefully deal with this common problem, since otherwise it might bias and mislead this exciting field of research.

show abstract

Spike timing in auditory‐nerve fibers during spontaneous activity and phase locking

Heil

Peterson

2016

Synapse

View full text Add to dashboard Cite

In vertebrates, all acoustic information transmitted from the inner ear to the central auditory system is relayed by primary auditory afferents (auditory-nerve fibers; ANFs). These neurons are also the most peripheral elements to use action potentials (spikes) to encode the acoustic information. Here, we review what is known about the spiking of ANFs during spontaneous activity, when spike timing might be regarded as largely random, and during stimulation by low-frequency sounds, when spikes are phase locked to the stimulus waveform, a phenomenon generally considered a hallmark of temporal precision and speed in the auditory system. We focus on mammals, in which each ANF is driven by a single ribbon synapse in a single receptor cell, but also cover relevant research on ANFs of vertebrates from other classes. For spontaneous activity, we highlight several spike-history effects in interspike interval distributions, hazard-rate functions, serial interval correlations, and spike-count statistics. We also review models that have attempted to account for these properties. For phase locking, we focus on the responses to low-frequency tones, rather than to low-frequency components of broadband signals such as noise or clicks. We critically review the measures commonly used to quantify phase locking and urge caution when interpreting such measures with respect to spike-timing precision. We also review the dependence of phase locking on stimulus amplitude and frequency. Finally, we identify some open questions.

show abstract

An Improved Model for the Rate–Level Functions of Auditory-Nerve Fibers

Cited by 47 publications

References 84 publications

Towards a Unifying Basis of Auditory Thresholds: Binaural Summation

Towards a Unifying Basis of Auditory Thresholds: Binaural Summation

Detection of Near-Threshold Sounds is Independent of EEG Phase in Common Frequency Bands

Spike timing in auditory‐nerve fibers during spontaneous activity and phase locking

Contact Info

Product

Resources

About