An investigation of dendritic delay in octopus cells of the mammalian cochlear nucleus

Asymmetry in the Perception of Electrical Chirps Presented to Cochlear Implant Listeners

INTRODUCTION

Although a broadband acoustic click is physically the shortest duration sound we can hear, its peripheral neural representation is not as short because of cochlear filtering. The traveling wave imposes frequency-dependent delays to the sound waveform so that in response to a click, apical nerve fibers, coding for low frequencies, are excited several milliseconds after basal fibers, coding for high frequencies. Nevertheless, a click sounds like a click and these across-fiber delays are not perceived. This suggests that they may be compensated by the central auditory system, rendering our perception consistent with the external world. This explanation is difficult to evaluate in normal-hearing listeners because the contributions of peripheral and central auditory processing cannot easily be disentangled. Here, we test this hypothesis in cochlear implant listeners for whom cochlear mechanics is bypassed.

METHOD

Eight cochlear implant users ranked in perceived duration 12 electrical chirps of various physical durations and spanning the cochlea in the apex-to-base or base-to-apex direction (Exp. 1). Late-latency cortical potentials were also recorded in response to a subset of these chirps (Exp. 2).

RESULTS

We show that an electrical chirp spanning the cochlea from base-to-apex is perceived as shorter than the same chirp spanning the cochlea in the opposite direction despite having the same physical duration. Cortical potentials also provide neural correlates of this asymmetry in perception.

CONCLUSION

These results demonstrate that the central auditory system processes frequency sweeps differently depending on the direction of the frequency change and that this processing difference is not simply the result of peripheral filtering.

Integrate-and-fire-type models of the lateral superior olive

Neurons of the lateral superior olive (LSO) in the auditory brainstem play a fundamental role in binaural sound localization. Previous theoretical studies developed various types of neuronal models to study the physiological functions of the LSO. These models were usually tuned to a small set of physiological data with specific aims in mind. Therefore, it is unclear whether and how they can be related to each other, how widely applicable they are, and which model is suitable for what purposes. In this study, we address these questions for six different single-compartment integrate-and-fire (IF) type LSO models. The models are divided into two groups depending on their subthreshold responses: passive (linear) models with only the leak conductance and active (nonlinear) models with an additional low-voltage-activated potassium conductance that is prevalent among the auditory system. Each of these two groups is further subdivided into three subtypes according to the spike generation mechanism: one with simple threshold-crossing detection and voltage reset, one with threshold-crossing detection plus a current to mimic spike shapes, and one with a depolarizing exponential current for spiking. In our simulations, all six models were driven by identical synaptic inputs and calibrated with common criteria for binaural tuning. The resulting spike rates of the passive models were higher for intensive inputs and lower for temporally structured inputs than those of the active models, confirming the active function of the potassium current. Within each passive or active group, the simulated responses resembled each other, regardless of the spike generation types. These results, in combination with the analysis of computational costs, indicate that an active IF model is more suitable than a passive model for accurately reproducing temporal coding of LSO. The simulation of realistic spike shapes with an extended spiking mechanism added relatively small computational costs.

A Computational Model of Auditory Chirp-Velocity Sensitivity and Amplitude-Modulation Tuning in Inferior Colliculus Neurons

We demonstrate a model of chirp-velocity sensitivity in the inferior colliculus (IC) that retains the tuning to amplitude modulation (AM) that was established in earlier models. The mechanism of velocity sensitivity is sequence detection by octopus cells of the posteroventral cochlear nucleus, which have been proposed in physiological studies to respond preferentially to the order of arrival of cross-frequency inputs of different amplitudes. Model architecture is based on coincidence detection of a combination of excitatory and inhibitory inputs. Chirp-sensitivity of the IC output is largely controlled by the strength and timing of the chirp-sensitive octopus-cell inhibitory input. AM tuning is controlled by inhibition and excitation that are tuned to the same frequency. We present several example neurons that demonstrate the feasibility of the model in simulating realistic chirp-sensitivity and AM tuning for a wide range of characteristic frequencies. Additionally, we explore the systematic impact of varying parameters on model responses. The proposed model can be used to assess the contribution of IC chirp-velocity sensitivity to responses to complex sounds, such as speech.

Enhanced Place Specificity of the Parallel Auditory Brainstem Response: An Electrophysiological Study

Purpose

This study investigates the effect of parallel stimulus presentation on the place specificity of the auditory brainstem response (ABR) in human listeners. Frequency-specific stimuli do not guarantee a response from the place on the cochlea corresponding only to that characteristic frequency - especially for brief and high-level stimuli. Adding masking noise yields responses that are more place specific, and a prior modeling study has suggested similar effects when multiple frequency-specific stimuli are presented in parallel. We tested this hypothesis experimentally here, comparing the place specificity of responses to serial and parallel stimuli at two stimulus frequencies and three stimulus rates.

Methods

Parallel ABR (pABR) stimuli were presented alongside high-pass filtered noise with a varied cutoff frequency. Serial presentation was also tested by isolating and presenting single-frequency stimulus trains from the pABR ensemble. Latencies of the ABRs were examined to assess place specificity of responses. Response bands were derived by subtracting responses from different high pass noise conditions. The response amplitude from each derived response band was then used to determine how much individual frequency regions of the auditory system were contributing to the overall response.

Results

We found that parallel presentation improves place specificity of ABRs for the lower stimulus frequency and at higher stimulus rates. At a higher stimulus frequency, serial and parallel presentation were equally place specific.

Conclusion

Parallel presentation can provide more place specific responses than serial for lower stimulus frequencies. The improvement increases with higher stimulus rates and is in addition to the pABR's primary benefit of faster test times.

An Anatomical and Physiological Basis for Coincidence Detection Across Time Scales in the Auditory System

Coincidence detection is a common neural computation that identifies co-occurring stimuli by integration of inputs. In the auditory system, octopus cells act as coincidence detectors for complex sounds that include both synchronous and sequenced combinations of frequencies. Octopus cells must detect coincidence on both the millisecond and submillisecond time scale, unlike the average neuron, which integrates inputs over time on the order of tens of milliseconds. Here, we show that octopus cell computations in the cell body are shaped by inhibition in the dendrites, which adjusts the strength and timing of incoming signals to achieve submillisecond acuity. This mechanism is crucial for the fundamental process of integrating the synchronized frequencies of natural auditory signals over time.

Mammalian octopus cells are direction selective to frequency sweeps by excitatory synaptic sequence detection

Octopus cells are remarkable projection neurons of the mammalian cochlear nucleus, with extremely fast membranes and wide-frequency tuning. They are considered prime examples of coincidence detectors but are poorly characterized in vivo. We discover that octopus cells are selective to frequency sweep direction, a feature that is absent in their auditory nerve inputs. In vivo intracellular recordings reveal that direction selectivity does not derive from across-frequency coincidence detection but hinges on the amplitudes and activation sequence of auditory nerve inputs tuned to clusters of hot spot frequencies. A simple biophysical octopus cell model excited with real nerve spike trains recreates direction selectivity through interaction of intrinsic membrane conductances with the activation sequence of clustered excitatory inputs. We conclude that octopus cells are sequence detectors, sensitive to temporal patterns across cochlear frequency channels. The detection of sequences rather than coincidences is a much simpler but powerful operation to extract temporal information.

A phenomenological spiking model for octopus cells in the posterior-ventral cochlear nucleus

Octopus cells in the posteroventral cochlear nucleus exhibit characteristic onset responses to broad band transients but are little investigated in response to more complex sound stimuli. In this paper, we propose a phenomenological, but biophysically motivated, modeling approach that allows to simulate responses of large populations of octopus cells to arbitrary sound pressure waves. The model depends on only few parameters and reproduces basic physiological characteristics like onset firing and phase locking to amplitude modulations. Simulated responses to speech stimuli suggest that octopus cells are particularly sensitive to high-frequency transients in natural sounds and their sustained firing to phonemes provides a population code for sound level.

Periodicity Pitch Perception

This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.

Cost of auditory sharpness: Model-Based estimate of energy use by auditory brainstem "octopus" neurons

Octopus cells (OCs) of the mammalian auditory brainstem precisely encode timing of fast transient sounds and tone onsets. Sharp temporal fidelity of OCs relies on low resting membrane resistance, which suggests high energy expenditure on maintaining ion gradients across plasma membrane. We provide a model-based estimate of energy consumption in resting and spiking OCs. Our results predict that a resting OC consumes up to 2.6 × 10⁹ ATP molecules (ATPs) per second which remarkably exceeds energy consumption of other CNS neurons. Glucose usage by all OCs in the rat is nevertheless low due to their low number. Major part of the OCs energy use results from the ion mechanisms providing for the low membrane resistance: hyperpolarization-activated mixed cation conductance and low-voltage activated K⁺-conductance. Spatially ordered synapses-a feature of the OCs allowing them to compensate for asynchrony of the synaptic input-brings only a 12% energy saving to OCs excitability cost. Only 13% of total OC energy used for an AP generation (1.5 × 10⁷ ATPs) is associated with the AP generation in the axon initial segment, 64%-with synaptic currents processing and 23%-with keeping resting potential.

Dendritic and Axonal Propagation Delays May Shape Neuronal Networks With Plastic Synapses

Biological neuronal networks are highly adaptive and plastic. For instance, spike-timing-dependent plasticity (STDP) is a core mechanism which adapts the synaptic strengths based on the relative timing of pre- and postsynaptic spikes. In various fields of physiology, time delays cause a plethora of biologically relevant dynamical phenomena. However, time delays increase the complexity of model systems together with the computational and theoretical analysis burden. Accordingly, in computational neuronal network studies propagation delays were often neglected. As a downside, a classic STDP rule in oscillatory neurons without propagation delays is unable to give rise to bidirectional synaptic couplings, i.e., loops or uncoupled states. This is at variance with basic experimental results. In this mini review, we focus on recent theoretical studies focusing on how things change in the presence of propagation delays. Realistic propagation delays may lead to the emergence of neuronal activity and synaptic connectivity patterns, which cannot be captured by classic STDP models. In fact, propagation delays determine the inventory of attractor states and shape their basins of attractions. The results reviewed here enable to overcome fundamental discrepancies between theory and experiments. Furthermore, these findings are relevant for the development of therapeutic brain stimulation techniques aiming at shifting the diseased brain to more favorable attractor states.

Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells

Pitch is an essential category for musical sensations. Models of pitch perception are vividly discussed up to date. Most of them rely on definitions of mathematical methods in the spectral or temporal domain. Our proposed pitch perception model is composed of an active auditory model extended by octopus cells. The active auditory model is the same as used in the Stimulation based on Auditory Modeling (SAM), a successful cochlear implant sound processing strategy extended here by modeling the functional behavior of the octopus cells in the ventral cochlear nucleus and by modeling their connections to the auditory nerve fibers (ANFs). The neurophysiological parameterization of the extended model is fully described in the time domain. The model is based on latency-phase en- and decoding as octopus cells are latency-phase rectifiers in their local receptive fields. Pitch is ubiquitously represented by cascaded firing sweeps of octopus cells. Based on the firing patterns of octopus cells, inter-spike interval histograms can be aggregated, in which the place of the global maximum is assumed to encode the pitch.

Compensation for Traveling Wave Delay Through Selection of Dendritic Delays Using Spike-Timing-Dependent Plasticity in a Model of the Auditory Brainstem

Asynchrony among synaptic inputs may prevent a neuron from responding to behaviorally relevant sensory stimuli. For example, “octopus cells” are monaural neurons in the auditory brainstem of mammals that receive input from auditory nerve fibers (ANFs) representing a broad band of sound frequencies. Octopus cells are known to respond with finely timed action potentials at the onset of sounds despite the fact that due to the traveling wave delay in the cochlea, synaptic input from the auditory nerve is temporally diffuse. This paper provides a proof of principle that the octopus cells' dendritic delay may provide compensation for this input asynchrony, and that synaptic weights may be adjusted by a spike-timing dependent plasticity (STDP) learning rule. This paper used a leaky integrate and fire model of an octopus cell modified to include a “rate threshold,” a property that is known to create the appropriate onset response in octopus cells. Repeated audio click stimuli were passed to a realistic auditory nerve model which provided the synaptic input to the octopus cell model. A genetic algorithm was used to find the parameters of the STDP learning rule that reproduced the microscopically observed synaptic connectivity. With these selected parameter values it was shown that the STDP learning rule was capable of adjusting the values of a large number of input synaptic weights, creating a configuration that compensated the traveling wave delay of the cochlea.

A biophysical modelling platform of the cochlear nucleus and other auditory circuits: From channels to networks

Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, biophysically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. Our model incorporates most of the quantitatively characterized intrinsic cell properties, synaptic properties, and connectivity available in the literature, and also aims to reproduce the known response properties of the canonical cochlear nucleus cell types. Although we currently lack the empirical data to completely constrain this model, our intent is for the model to continue to incorporate new experimental results as they become available.

Learning Pitch with STDP: A Computational Model of Place and Temporal Pitch Perception Using Spiking Neural Networks

Pitch perception is important for understanding speech prosody, music perception, recognizing tones in tonal languages, and perceiving speech in noisy environments. The two principal pitch perception theories consider the place of maximum neural excitation along the auditory nerve and the temporal pattern of the auditory neurons’ action potentials (spikes) as pitch cues. This paper describes a biophysical mechanism by which fine-structure temporal information can be extracted from the spikes generated at the auditory periphery. Deriving meaningful pitch-related information from spike times requires neural structures specialized in capturing synchronous or correlated activity from amongst neural events. The emergence of such pitch-processing neural mechanisms is described through a computational model of auditory processing. Simulation results show that a correlation-based, unsupervised, spike-based form of Hebbian learning can explain the development of neural structures required for recognizing the pitch of simple and complex tones, with or without the fundamental frequency. The temporal code is robust to variations in the spectral shape of the signal and thus can explain the phenomenon of pitch constancy.

Pitch is the perceptual correlate of sound frequency. Our auditory system has a sophisticated mechanism to process and perceive the neural information corresponding to pitch. This mechanism employs both the place and the temporal pattern of pitch-evoked neural events. Based on the known functions of the auditory system, we develop a computational model of pitch perception using a network of neurons with modifiable connections. We demonstrate that a well-known neural learning rule that is based on the timing of the neural events can identify and strengthen the neuronal connections that are most effective for the extraction of pitch. By providing an insight into how the auditory system interprets pitch information, the results of our study can be used to develop improved sound processing strategies for cochlear implants. In cochlear implant hearing, auditory percept is generated by stimulating the auditory neurons with controlled electrical impulses, enhancing which with the help of the model would lead to a better representation of pitch and would subsequently improve music perception and speech understanding in noisy environments in cochlear implant users.

Broadband onset inhibition can suppress spectral splatter in the auditory brainstem

In vivo intracellular responses to auditory stimuli revealed that, in a particular population of cells of the ventral nucleus of the lateral lemniscus (VNLL) of rats, fast inhibition occurred before the first action potential. These experimental data were used to constrain a leaky integrate-and-fire (LIF) model of the neurons in this circuit. The post-synaptic potentials of the VNLL cell population were characterized using a method of triggered averaging. Analysis suggested that these inhibited VNLL cells produce action potentials in response to a particular magnitude of the rate of change of their membrane potential. The LIF model was modified to incorporate the VNLL cells’ distinctive action potential production mechanism. The model was used to explore the response of the population of VNLL cells to simple speech-like sounds. These sounds consisted of a simple tone modulated by a saw tooth with exponential decays, similar to glottal pulses that are the repeated impulses seen in vocalizations. It was found that the harmonic component of the sound was enhanced in the VNLL cell population when compared to a population of auditory nerve fibers. This was because the broadband onset noise, also termed spectral splatter, was suppressed by the fast onset inhibition. This mechanism has the potential to greatly improve the clarity of the representation of the harmonic content of certain kinds of natural sounds.