Phase Locking of Auditory-Nerve Fibers Reveals Stereotyped Distortions and an Exponential Transfer Function with a Level-Dependent Slope

Signal processing and stimulation potential within the ascending auditory pathway: a review

The human auditory system encodes sound with a high degree of temporal and spectral resolution. When hearing fails, existing neuroprosthetics such as cochlear implants may partially restore hearing through stimulation of auditory neurons at the level of the cochlea, though not without limitations inherent to electrical stimulation. Novel approaches to hearing restoration, such as optogenetics, offer the potential of improved performance. We review signal processing in the ascending auditory pathway and the current state of conventional and emerging neural stimulation strategies at various levels of the auditory system.

Beat encoding at mistuned octaves within single electrosensory neurons

Beats are slow periodic amplitude modulations resulting from the superposition of two spectrally close periodic signals. The difference frequency between the signals sets the frequency of the beat. A field study in the electric fish Apteronotus rostratus showed the behavioral relevance of very high difference frequencies. Contrary to expectations from previous studies, our electrophysiological data show strong responses of p-type electroreceptor afferents whenever the difference frequency approaches integer multiples (mistuned octaves) of the fish's own electric field frequency (carrier). Mathematical reasoning and simulations show that common approaches to extract amplitude modulations, such as Hilbert transform or half-wave rectification, are not sufficient to explain the responses at carrier octaves. Instead, half-wave rectification needs to be smoothed out, for example by a cubic function. Because electroreceptive afferents share many properties with auditory nerve fibers, these mechanisms may underly the human perception of beats at mistuned octaves as described by Ohm and Helmholtz.

More than the end: OFF response plasticity as a mnemonic signature of a sound’s behavioral salience

In studying how neural populations in sensory cortex code dynamically varying stimuli to guide behavior, the role of spiking after stimuli have ended has been underappreciated. This is despite growing evidence that such activity can be tuned, experience-and context-dependent and necessary for sensory decisions that play out on a slower timescale. Here we review recent studies, focusing on the auditory modality, demonstrating that this so-called OFF activity can have a more complex temporal structure than the purely phasic firing that has often been interpreted as just marking the end of stimuli. While diverse and still incompletely understood mechanisms are likely involved in generating phasic and tonic OFF firing, more studies point to the continuing post-stimulus activity serving a short-term, stimulus-specific mnemonic function that is enhanced when the stimuli are particularly salient. We summarize these results with a conceptual model highlighting how more neurons within the auditory cortical population fire for longer duration after a sound’s termination during an active behavior and can continue to do so even while passively listening to behaviorally salient stimuli. Overall, these studies increasingly suggest that tonic auditory cortical OFF activity holds an echoic memory of specific, salient sounds to guide behavioral decisions.

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

Information processing in the nervous system critically relies on temporally precise spiking activity. In the auditory system, various degrees of phase-locking can be observed from the auditory nerve to cortical neurons. The classical metric for quantifying phase-locking is the vector strength (VS), which captures the periodicity in neuronal spiking. More recently, another metric, called the correlation index (CI), was proposed to quantify the temporally reproducible response characteristics of a neuron. The CI is defined as the peak value of a normalized shuffled autocorrelogram (SAC). Both VS and CI have been used to investigate how temporal information is processed and propagated along the auditory pathways. While previous analyses of physiological data in cats suggested covariation of these two metrics, general characterization of their connection has never been performed. In the present study, we derive a rigorous relationship between VS and CI. To model phase-locking, we assume Poissonian spike trains with a temporally changing intensity function following a von Mises distribution. We demonstrate that VS and CI are mutually related via the so-called concentration parameter that determines the degree of phase-locking. We confirm that these theoretical results are largely consistent with physiological data recorded in the auditory brainstem of various animals. In addition, we generate artificial phase-locked spike sequences, for which recording and analysis parameters can be systematically manipulated. Our analysis results suggest that mismatches between empirical data and the theoretical prediction can often be explained with deviations from the von Mises distribution, including skewed or multimodal period histograms. Furthermore, temporal relations of spike trains across trials can contribute to higher CI values than predicted mathematically based on the VS. We find that, for most applications, a SAC bin width of 50 ms seems to be a favorable choice, leading to an estimated error below 2.5% for physiologically plausible conditions. Overall, our results provide general relations between the two measures of phase-locking and will aid future analyses of different physiological datasets that are characterized with these metrics.

A convolutional neural-network framework for modelling auditory sensory cells and synapses

In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.

Drakopoulos et al developed a machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Focusing on auditory neurons and synapses, they showed that their DNN-model architecture could be extended to a variety of existing analytical models and to other neuron and synapse types, thus potentially assisting the development of large-scale brain networks and DNN-based treatments.

A simplified physiological model of rate-level functions of auditory-nerve fibers

Several approaches have been used to describe the rate-level functions of auditory-nerve fibers (ANFs). One approach uses descriptive models that can be fitted easily to data. Another derives rate-level functions from comprehensive physiological models of auditory peripheral processing. Here, we seek to identify the minimal set of components needed to provide a physiologically plausible account of rate-level functions. Our model consists of a first-order Boltzmann mechanoelectrical transducer function relating the instantaneous stimulus pressure to an instantaneous output, followed by a lowpass filter that eliminates the AC component, followed by an exponential synaptic transfer function relating the DC component to the mean spike rate. This is perhaps the simplest physiologically plausible model capable of accounting for rate-level functions under the assumption that the model parameters for a given ANF and stimulus frequency are level-independent. We find that the model typically accounts well for rate-level functions from cat ANFs for all stimulus frequencies. More complicated model variants having saturating synaptic transfer functions do not perform significantly better, implying the system operates far away from synaptic saturation. Rate saturation in the model is caused by saturation of the DC component of the filter output (e.g., the receptor potential), which in turn is due to the saturation of the transducer function. The maximum mean spike rate is approximately constant across ANFs, such that the slope parameter of the exponential synaptic transfer function decreases with increasing spontaneous rate. If the synaptic parameters for a given ANF are assumed to be constant across stimulus frequencies, then frequency- and level-dependent input nonlinearities are derived that are qualitatively similar to those reported in the literature. Contrary to assumptions in the literature, such nonlinearities are obtained even for ANFs having high spontaneous rates. Finally, spike-rate adaptation is examined and found to be accounted for by a decrease in the slope parameter of the synaptic transfer function over time following stimulus onset.

Developmentally Regulated Rebound Depolarization Enhances Spike Timing Precision in Auditory Midbrain Neurons

The inferior colliculus (IC) is an auditory midbrain structure involved in processing biologically important temporal features of sounds. The responses of IC neurons to these temporal features reflect an interaction of synaptic inputs and neuronal biophysical properties. One striking biophysical property of IC neurons is the rebound depolarization produced following membrane hyperpolarization. To understand how the rebound depolarization is involved in spike timing, we made whole-cell patch clamp recordings from IC neurons in brain slices of P9-21 rats. We found that the percentage of rebound neurons was developmentally regulated. The precision of the timing of the first spike on the rebound increased when the neuron was repetitively injected with a depolarizing current following membrane hyperpolarization. The average jitter of the first spikes was only 0.5 ms. The selective T-type Ca²⁺ channel antagonist, mibefradil, significantly increased the jitter of the first spike of neurons in response to repetitive depolarization following membrane hyperpolarization. Furthermore, the rebound was potentiated by one to two preceding rebounds within a few hundred milliseconds. The first spike generated on the potentiated rebound was more precise than that on the non-potentiated rebound. With the addition of a calcium chelator, BAPTA, into the cell, the rebound potentiation no longer occurred, and the precision of the first spike on the rebound was not improved. These results suggest that the postinhibitory rebound mediated by T-type Ca²⁺ channel promotes spike timing precision in IC neurons. The rebound potentiation and precise spikes may be induced by increases in intracellular calcium levels.

Phase Locking of Auditory Nerve Fibers: The Role of Lowpass Filtering by Hair Cells

Phase locking of auditory-nerve-fiber (ANF) responses to the temporal fine structure of acoustic stimuli, a hallmark of the auditory system's temporal precision, is important for many aspects of hearing. Previous work has shown that phase-locked period histograms are often well described by exponential transfer functions relating instantaneous stimulus pressure to instantaneous spike rate, with no observed clipping of the histograms. The operating points and slopes of these functions change with stimulus level. The mechanism underlying this apparent gain control is unclear but is distinct from mechanical compression, is independent of refractoriness and spike-rate adaptation, and is apparently instantaneous. Here we show that these findings can be accounted for by a model consisting of a static Boltzmann transducer function yielding a clipped output, followed by a lowpass filter and a static exponential transfer function. Using responses to tones of ANFs from cats of both sexes, we show that, for a given ANF, the period histograms obtained at all stimulus levels for a given stimulus frequency can be described using one set of level-independent model parameters. The model also accounts for changes in the maximum and minimum instantaneous spike rates with changes in stimulus level. Notably, the estimated cutoff frequency is lower for low- than for high-spontaneous-rate ANFs, implying a synapse-specific contribution to lowpass filtering. These findings advance our understanding of ANF phase locking by highlighting the role of peripheral filtering mechanisms in shaping responses of individual ANFs.SIGNIFICANCE STATEMENT Phase locking of auditory-nerve-fiber responses to the temporal fine structure of acoustic stimuli is important for many aspects of hearing. Period histograms typically retain an approximately sinusoidal shape across stimulus levels, with the peripheral auditory system operating as though its overall transfer function is an exponential function whose slope decreases with increasing stimulus level. This apparent gain control can be accounted for by a static saturating transducer function followed by a lowpass filter. In addition to attenuating the AC component, the filter approximately recovers the sinusoidal waveform of the stimulus. The estimated cutoff frequency varies with spontaneous rate, revealing a synaptic contribution to lowpass filtering. These findings highlight the significant impact of peripheral filtering mechanisms on phase locking.

Inconsistent effects of stochastic resonance on human auditory processing

It has been demonstrated that, while otherwise detrimental, noise can improve sensory perception under optimal conditions. The mechanism underlying this improvement is stochastic resonance. An inverted U-shaped relationship between noise level and task performance is considered as the signature of stochastic resonance. Previous studies have proposed the existence of stochastic resonance also in the human auditory system. However, the reported beneficial effects of noise are small, based on a small sample, and do not confirm the proposed inverted U-shaped function. Here, we investigated in two separate studies whether stochastic resonance may be present in the human auditory system by applying noise of different levels, either acoustically or electrically via transcranial random noise stimulation, while participants had to detect acoustic stimuli adjusted to their individual hearing threshold. We find no evidence for behaviorally relevant effects of stochastic resonance. Although detection rate for near-threshold acoustic stimuli appears to vary in an inverted U-shaped manner for some subjects, it varies in a U-shaped manner or in other manners for other subjects. Our results show that subjects do not benefit from noise, irrespective of its modality. In conclusion, our results question the existence of stochastic resonance in the human auditory system.