Учебники / Hearing - From Sensory Processing to Perception Kollmeier 2007

Fig. 2 Firing rates to 40-Hz cosine amplitude-modulated pulse trains of 320 and 640 pps

solely due to the adaptive mechanisms in the IC model. When high-rate pulse trains are amplitude modulated, ITD sensitivity to on-going stimulus is observed in a strong response (Fig. 2). This result supports the hypothesis that an adaptation mechanism with amplitude-modulation enhances ITD sensitivity.

4Cell Membrane Factors in Modulation Effects

In contrast with the Jeffress (1948) coincidence model for ITD sensitivity, real coincidence mechanisms involve neurons with refractory properties that prevent neural firings and degrade ITD tuning for high frequency inputs (Cook et al. 2003; Dasika et al. 2005). Refractory behavior may also play a significant role in restoring neural discharges with amplitude modulation. Explored here are effects of synaptic parameters and sinusoidally amplitude modulated (SAM) pulse-train stimuli on the ITD sensitivity of a model neuron with an active membrane.

4.1Model Description

The neural model and stimulus generation are similar to a multi-compartment MSO model used in a previous study (Zhou et al. 2005) with two modifications: 1) the cell model has only a soma compartment with the set of ionic channels, and 2) inputs to the model simulate the peripheral responses to amplitude-modulated electrical-pulse stimulations instead of tones. With a probability proportional to the amplitude of the pulse, a pulse triggers a change in post-synaptic conductance with maximum value Ge. The input

Fig. 4 ITD tuning for unmodulated pulse trains (500 and 1000 pps) for several values of synaptic conductance Ge. Tuning is very sensitive to synaptic strengths (Ge). For the sub-threshold Ge condition, membrane refractoriness decreases model responses to in-phase inputs (relative to near-threshold Gecase), resulting in no ITD-tuning. For the near-threshold Ge condition, membrane refractoriness decreases model responses to out-of-phase inputs, resulting in better ITD tuning. For the super-threshold Ge condition, summed inputs overcome membrane refractoriness at most ITD values, resulting in saturated ITD tunings

threshold levels. Simulation results indicate that refractory processes lead to reduced discharges for in-phase inputs with the sub-threshold Ge and for out-of-phase inputs with the near-threshold Ge, and has less effect on model discharges with a super-threshold Ge. As a result, the corresponding ITD tuning at these three conditions show different overall activities, dynamic ranges, and tuning widths.

Amplitude modulating the inputs reduces the effective synaptic strength (and therefore neural firing) during half of the modulation cycle, thereby minimizing the effects of refractoriness on subsequent inputs. However, the resultant ITD tuning can be either improved or degraded dependent upon the synaptic strength level. Figure 5 shows that amplitude modulation improves ITD tuning at sub-threshold Ge condition and degrades ITD tuning at near-threshold Ge condition. This is due to neural release from the RRP for either in-phase or out-of-phase inputs. For the sub-threshold Ge condition (A and B), amplitude modulation releases in-phase responses from membrane refractoriness and ITD tuning is improved relative to that to un-modulated pulse-trains. For the near-threshold Ge condition (C and D), amplitude modulation releases out-of-phase responses from membrane refractoriness and ITD tuning is degraded relative to that to un-modulated pulse-trains.

Finally, adding amplitude variation decreases the effective synaptic strength by reducing the coincidence of unilateral inputs, and therefore could be used to improve ITD tuning. Results (not shown) indicate that an increase in jitter (larger σ) decreases the response rate such that ITD dependence may increase or decrease with increases in σ.

Fig. 5 Amplitude modulation (ƒm=50 or 100 Hz) can either improve or degrade ITD tuning to pulse-train stimuli (500 or 1000 pps): A,B the sub-threshold Ge condition; C,D the nearthreshold Ge condition

5Effects of Input Rate and Phase on ITD Sensitivity

The output of real a coincidence detector is also highly dependent on the rate and synchrony of its inputs. Principal MSO neurons are believed to derive ITD sensitivity by coincidence detection of their bilateral inputs, which are phase locked to auditory stimuli. This section explores why higher-rate, more highly synchronized responses in the auditory nerve during electrical stimulation may produce a loss in ITD sensitivity. Simulations of MSO neurons under acoustic and electrical stimulation of the auditory periphery were compared using the single compartment model.

5.1Model Description

The single-compartment model MSO cell here is nearly identical to that in Sect. 4, with identical channel dynamics, conductances, and capacitance (Zhou et al. 2005), and Ih calculated in the manner of Rothman and Manis (2003). The model MSO cell has 20 bilateral inputs (10 per side) from explicitly modeled auditory nerve fibers. A 500-Hz tone was the driving stimulus for both acoustic and electric input models. Each stimulus period for each fiber, it is determined if a spike occurs and when according to a Gaussian distribution with σ set by the nominal synchronization index (SI) (Zhou et al. 2005). Average input spike rate and SI were set according to condition, and phase delays between electric inputs were introduced as described below.

5.2Results

Figure 6 shows the rate-ITD characteristics of the model MSO cell for acoustic inputs and three variations of electric inputs. Synaptic strength was set and maintained such that simulated acoustic inputs (120 spikes/s for each input, SI=0.7) produced rate-ITD sensitivity similar to real MSO cells (Goldberg and Brown 1969; Yin and Chan 1990). Electrical stimulation was modeled as 250 spikes/s for each input, with SI=0.99. In simulated electrical stimulation without phase-dispersion (top curve), the high-rate, highly phase-locked inputs produced entrainment and saturation of the rate-ITD curve at good phase, and the resulting “monaural coincidences” produced high discharge rates at all phases, including bad phase. To simulate the possible effect of neural delays that may normally compensate for the cochlear traveling wave delay, phase dispersion between inputs was introduced such that added phases were equally spaced across the stimulus period (bottom curve). With this high degree of phase-dispersion, ITD sensitivity was eliminated, and few output discharges occurred across the range of ITD. To simulate the possible effect of neural plasticity after cochlear implantation, phase dispersion was limited to half the stimulus period. With this moderate phase dispersion, enhanced ITD sensitivity occurred in terms of the amplitude of the rate-ITD curve, however with slightly broader peaks (broader tuning) than for the lower rate acoustic stimulation.

ITD (ms)

Fig. 6 ITD sensitivity of a model MSO neuron in response to acoustic and electric stimulation

54 Neural and Behavioral Sensitivities to Azimuth Degrade with Distance in Reverberant Environments

SASHA DEVORE1, ANTJE IHLEFELD2, BARBARA G. SHINN-CUNNINGHAM2,

AND BERTRAND DELGUTTE1

1Introduction

Reverberation poses a challenge to sound localization in rooms. In an anechoic space, the only energy reaching a listener’s ears arrives directly from the sound source. In reverberant environments, however, acoustic reflections interfere with the direct sound and distort the ongoing directional cues, leading to fluctuations in interaural time and level differences (ITD and ILD) over the course of the stimulus (Shinn-Cunningham et al. 2005). These effects become more severe as the distance from sound source to listener increases, which causes the ratio of direct to reverberant energy (D/R) to decrease (Hartmann et al. 2005; Shinn-Cunningham et al. 2005).

Few neurophysiological and psychophysical studies have systematically examined sensitivity to sound source azimuth as a function of D/R (Rakerd and Hartmann 2005). Here we report the results of two closely-integrated studies aimed at characterizing the influence of acoustic reflections like those present in typical classrooms on both the directional sensitivity of auditory neurons and the localization performance of human listeners. We used low-frequency stimuli to emphasize ITDs, which are the most important binaural cue for sounds containing low-frequency energy (MacPherson and Middlebrooks 2002; Wightman and Kistler 1992). We find that reverberation reduces the directional sensitivity of low-frequency, ITD-sensitive neurons in the cat inferior colliculus (IC), and that this degradation becomes more severe with decreasing D/R (increasing distance). We show parallel degradations in human sensitivity to the azimuth of low-frequency noise.

1Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston, MA, USA, sashad@mit.edu, Bertrand_Delgutte@meei.harvard.edu

2Hearing Research Center, Boston University, Boston, MA, USA, ihlefeld@bu.edu, shinn@bu.edu

Hearing – From Sensory Processing to Perception

B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007

Fig. 1 A Mean neural response rate (±s.e.m.) as a function of source azimuth for two neurons in the cat IC. B Mean (large filled symbols) relative response range as a function of D/R. Small symbols indicate relative response range for individual units

Fig. 2 Mean (large filled symbols) rIT as a function of D/R. Small symbols show rIT for individual units

than 1, indicating a compression of the rate-azimuths curves; furthermore, relative range decreases with decreasing D/R (Fig. 1B).

Neural sensitivity to azimuth depends not only on the response range, but also on the variability in responses at each azimuth. To quantify sensitivity, we computed the mutual information (MI) between the source azimuth and the neural spike counts for each neuron and room condition. MI characterizes the precision with which the source azimuth can be estimated from the neural spike counts without making additional assumptions about the neural code. To compare across experiments using different numbers of stimuli, MI was normalized by the stimulus entropy to get the relative information transfer (rIT). Figure 2 shows that the rIT systematically decreases with decreasing D/R.