Mode-locked spike trains in responses of ventral cochlear nucleus chopper and onset neurons to periodic stimuli

Musical neurodynamics

A great deal of research in the neuroscience of music suggests that neural oscillations synchronize with musical stimuli. Although neural synchronization is a well-studied mechanism underpinning expectation, it has even more far-reaching implications for music. In this Perspective, we survey the literature on the neuroscience of music, including pitch, harmony, melody, tonality, rhythm, metre, groove and affect. We describe how fundamental dynamical principles based on known neural mechanisms can explain basic aspects of music perception and performance, as summarized in neural resonance theory. Building on principles such as resonance, stability, attunement and strong anticipation, we propose that people anticipate musical events not through predictive neural models, but because brain-body dynamics physically embody musical structure. The interaction of certain kinds of sounds with ongoing pattern-forming dynamics results in patterns of perception, action and coordination that we collectively experience as music. Statistically universal structures may have arisen in music because they correspond to stable states of complex, pattern-forming dynamical systems. This analysis of empirical findings from the perspective of neurodynamic principles sheds new light on the neuroscience of music and what makes music powerful.

Local targets of T-stellate cells in the ventral cochlear nucleus

T-stellate cells in the ventral cochlear nucleus (VCN) are known to have local axon collaterals that terminate in the vicinity of their dendrites and cell bodies within the same isofrequency lamina in parallel with the auditory nerve fibers that innervate them. Excitatory synaptic connections between stellate cells within an isofrequency lamina are hypothesized to be involved in the nitric oxide-mediated upregulation of T-stellate responses to their auditory input. This could serve as a mechanism of variable gain control in the enhancement of responses to vowel spectral peaks. Previous studies have provided indirect evidence for these possible synaptic interconnections between T-stellate cells, but unequivocal identification has yet to be established. Here, we used retrograde neuronal tracing with adeno-associated viral vector or biotinylated dextran amine injected into the inferior colliculus (IC) to detect the postsynaptic target of T-stellate cells within the VCN. We show that backfilled T-stellate cell axons make monosynapatic connections on the labeled cell bodies and dendrites of other labeled T-stellate cells within an isofrequency lamina. Electron microscopy revealed that T-stellate terminals can also make synapses on structures not retrogradely labeled from the IC. Glycine antibodies combined with the viral labeling indicated that these nonbackfilled structures that the labeled T-stellate terminals were synapsing on are most likely the cell bodies and dendrites of two size categories of glycinergic VCN cells, whose sizes and relative numbers indicated they are the D- and L-stellate cells. These cells are known to provide inhibitory inputs back onto T-stellate cells. Our data indicate that, in addition to their auditory nerve input, T-stellate cells provide a second modulatable excitatory input to both inhibitory and excitatory cells in a VCN isofrequency lamina and may play a significant role in acoustic information processing.

Characterizing signal encoding and transmission in class I and class II neurons via ordinal time-series analysis

Neurons encode and transmit information in spike sequences. However, despite the effort devoted to understand the encoding and transmission of information, the mechanisms underlying the neuronal encoding are not yet fully understood. Here, we use a nonlinear method of time-series analysis (known as ordinal analysis) to compare the statistics of spike sequences generated by applying an input signal to the neuronal model of Morris-Lecar. In particular, we consider two different regimes for the neurons which lead to two classes of excitability: class I, where the frequency-current curve is continuous and class II, where the frequency-current curve is discontinuous. By applying ordinal analysis to sequences of inter-spike-intervals (ISIs) our goals are (1) to investigate if different neuron types can generate spike sequences which have similar symbolic properties; (2) to get deeper understanding on the effects that electrical (diffusive) and excitatory chemical (i.e., excitatory synapse) couplings have; and (3) to compare, when a small-amplitude periodic signal is applied to one of the neurons, how the signal features (amplitude and frequency) are encoded and transmitted in the generated ISI sequences for both class I and class II type neurons and electrical or chemical couplings. We find that depending on the frequency, specific combinations of neuron/class and coupling-type allow a more effective encoding, or a more effective transmission of the signal.

Phase-locking patterns in a resonate and fire neural model with periodic drive

In this paper we studied a resonate and fire relaxation oscillator subject to time dependent modulation to investigate phase-locking phenomena occurring in neurophysiological systems. The neural model (denoted LFHN) was obtained by linearization of the FitzHugh-Nagumo neural model near an hyperbolic fixed point and then by introducing an integrate-and-fire mechanism for spike generation. By employing specific tools to study circle maps, we showed that this system exhibits several phase-locking patterns in the presence of periodic perturbations. Moreover, both the amplitude and frequency of the modulation strongly impact its phase-locking properties. In addition, general conditions for the generation of firing activity were also obtained. In addition, it was shown that for moderate noise levels the phase-locking patterns of the LFHN persist. Moreover, in the presence of noise, the rotation number changes smoothly as the stimulation current increases. Then, the statistical properties of the firing map were investigated too. Lastly, the results obtained with the forced LFHN suggest that such neural model could be used to fit specific experimental data on the firing times of neurons.

Nitric Oxide-Mediated Plasticity of Interconnections Between T-Stellate cells of the Ventral Cochlear Nucleus Generate Positive Feedback and Constitute a Central Gain Control in the Auditory System

T-stellate cells in the ventral cochlear nucleus (VCN) form an ascending pathway that conveys spectral information from the cochlea to brainstem nuclei, the inferior colliculi, and the thalamus. The tonotopic array of T-stellate cells enhances the encoding of spectral peaks relative to their auditory nerve fiber inputs. The alignment of local collaterals and T-stellate cell dendrites within the isofrequency lamina suggests that the cells make connections within the isofrequency lamina in which they reside. Recordings from pairs of T-stellate cells in mice of both sexes revealed that firing in the presynaptic cell evoked responses in the postsynaptic cell when presynaptic firing was paired with depolarization of the postsynaptic cell. After such experimental coactivation, presynaptic firing evoked EPSCs of uniform amplitude whose frequency depended on the duration of depolarization and diminished over minutes. Nitric oxide (NO) donors evoked EPSCs in T-stellate cells but not in the other types of principal cells. Blockers of neuronal nitric oxide synthase (nNOS) and of NMDA receptors blocked potentiation, indicating that NO mediates potentiation. nNOS and its receptor, guanylate cyclase (NO-GC), are expressed in somata of T-stellate cells. Excitatory interconnections were bidirectional and polysynaptic, indicating that T-stellate cells connect in networks. Positive feedback provided by temporarily potentiated interconnections between T-stellate cells could enhance the gain of auditory nerve excitation in proportion to the excitation, generating a form of short-term central gain control that could account for the ability of T-stellate cells to enhance the encoding of spectral peaks.SIGNIFICANCE STATEMENT T-stellate cells are interconnected through synapses that have a previously undescribed form of temporary, nitric oxide-mediated plasticity. Coactivation of neighboring cells enhances the activation of an excitatory network that feeds back on itself by enhancing the probability of EPSCs. Although there remain gaps in our understanding of how the interconnections revealed in slices contribute to hearing, our findings have interesting implications. Positive feedback through a network of interconnections could account for how T-stellate cells are able to encode spectral peaks over a wider range of intensities than many of their auditory nerve inputs (Blackburn and Sachs, 1990; May et al., 1998). The magnitude of the gain may itself be plastic because neuronal nitric oxide synthase increases when animals have tinnitus (Coomber et al., 2015).

Mode locking in periodically forced gradient frequency neural networks

We study mode locking in a canonical model of gradient frequency neural networks under periodic forcing. The canonical model is a generic mathematical model for a network of nonlinear oscillators tuned to a range of distinct frequencies. It is mathematically more tractable than biological neuron models and allows close analysis of mode-locking behaviors. Here we analyze individual modes of synchronization for a periodically forced canonical model and present a complete set of driven behaviors for all parameter regimes available in the model. Using a closed-form approximation, we show that the Arnold tongue (i.e., locking region) for k:m synchronization gets narrower as k and m increase. We find that numerical simulations of the canonical model closely follow the analysis of individual modes when forcing is weak, but they deviate at high forcing amplitudes for which oscillator dynamics are simultaneously influenced by multiple modes of synchronization.

Sub-threshold signal encoding in coupled FitzHugh-Nagumo neurons

Despite intensive research, the mechanisms underlying the neural code remain poorly understood. Recent work has focused on the response of a single neuron to a weak, sub-threshold periodic signal. By simulating the stochastic FitzHugh-Nagumo (FHN) model and then using a symbolic method to analyze the firing activity, preferred and infrequent spike patterns (defined by the relative timing of the spikes) were detected, whose probabilities encode information about the signal. As not individual neurons but neuronal populations are responsible for sensory coding and information transfer, a relevant question is how a second neuron, which does not perceive the signal, affects the detection and the encoding of the signal, done by the first neuron. Through simulations of two stochastic FHN neurons we show that the encoding of a sub-threshold signal in symbolic spike patterns is a plausible mechanism. The neuron that perceives the signal fires a spike train that, despite having an almost random temporal structure, has preferred and infrequent patterns which carry information about the signal. Our findings could be relevant for sensory systems composed by two noisy neurons, when only one detects a weak external input.

Enhancement of phase-locking in rodents. I. An axonal recording study in gerbil

The trapezoid body (TB) contains axons of neurons in the anteroventral cochlear nucleus projecting to monaural and binaural nuclei in the superior olivary complex (SOC). Characterization of these monaural inputs is important for the interpretation of response properties of SOC neurons. In particular, understanding of the sensitivity to interaural time differences (ITDs) in neurons of the medial and lateral superior olive requires knowledge of the temporal firing properties of the monaural excitatory and inhibitory inputs to these neurons. In recent years, studies of ITD sensitivity of SOC neurons have made increasing use of small animal models with good low-frequency hearing, particularly the gerbil. We presented stimuli as used in binaural studies to monaural neurons in the TB and studied their temporal coding. We found that general trends as have been described in the cat are present in gerbil, but with some important differences. Phase-locking to pure tones tends to be higher in TB axons and in neurons of the medial nucleus of the TB (MNTB) than in the auditory nerve for neurons with characteristic frequencies (CFs) below 1 kHz, but this enhancement is quantitatively more modest than in cat. Stronger enhancement is common when TB neurons are stimulated at low frequencies below CF. It is rare for TB neurons in gerbil to entrain to low-frequency stimuli, i.e., to discharge a well-timed spike on every stimulus cycle. Also, complex phase-locking behavior, with multiple modes of increased firing probability per stimulus cycle, is common in response to low frequencies below CF.NEW & NOTEWORTHY Phase-locking is an important property of neurons in the early auditory pathway: it is critical for the sensitivity to time differences between the two ears enabling spatial hearing. Studies in cat have shown an improvement in phase-locking from the peripheral to the central auditory nervous system. We recorded from axons in an output tract of the cochlear nucleus and show that a similar but more limited form of temporal enhancement is present in gerbil.

Mode-locking behavior of Izhikevich neurons under periodic external forcing

Many neurons in the auditory system of the brain must encode periodic signals. These neurons under periodic stimulation display rich dynamical states including mode locking and chaotic responses. Periodic stimuli such as sinusoidal waves and amplitude modulated sounds can lead to various forms of n:m mode-locked states, in which a neuron fires n action potentials per m cycles of the stimulus. Here, we study mode-locking in the Izhikevich neurons, a reduced model of the Hodgkin-Huxley neurons. The Izhikevich model is much simpler in terms of the dimension of the coupled nonlinear differential equations compared with other existing models, but excellent for generating the complex spiking patterns observed in real neurons. We obtained the regions of existence of the various mode-locked states on the frequency-amplitude plane, called Arnold tongues, for the Izhikevich neurons. Arnold tongue analysis provides useful insight into the organization of mode-locking behavior of neurons under periodic forcing. We find these tongues for both class-1 and class-2 excitable neurons in both deterministic and noisy regimes.

A Dynamical Model of Pitch Memory Provides an Improved Basis for Implied Harmony Estimation

Tonal melody can imply vertical harmony through a sequence of tones. Current methods for automatic chord estimation commonly use chroma-based features extracted from audio signals. However, the implied harmony of unaccompanied melodies can be difficult to estimate on the basis of chroma content in the presence of frequent nonchord tones. Here we present a novel approach to automatic chord estimation based on the human perception of pitch sequences. We use cohesion and inhibition between pitches in auditory short-term memory to differentiate chord tones and nonchord tones in tonal melodies. We model short-term pitch memory as a gradient frequency neural network, which is a biologically realistic model of auditory neural processing. The model is a dynamical system consisting of a network of tonotopically tuned nonlinear oscillators driven by audio signals. The oscillators interact with each other through nonlinear resonance and lateral inhibition, and the pattern of oscillatory traces emerging from the interactions is taken as a measure of pitch salience. We test the model with a collection of unaccompanied tonal melodies to evaluate it as a feature extractor for chord estimation. We show that chord tones are selectively enhanced in the response of the model, thereby increasing the accuracy of implied harmony estimation. We also find that, like other existing features for chord estimation, the performance of the model can be improved by using segmented input signals. We discuss possible ways to expand the present model into a full chord estimation system within the dynamical systems framework.

Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience

The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear-for example, heteroclinic network attractors. In this review we present a set of mathematical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical framework for further successful applications of mathematics to understanding network dynamics in neuroscience.

A Phenomenological Model of the Electrically Stimulated Auditory Nerve Fiber: Temporal and Biphasic Response Properties

We present a phenomenological model of electrically stimulated auditory nerve fibers (ANFs). The model reproduces the probabilistic and temporal properties of the ANF response to both monophasic and biphasic stimuli, in isolation. The main contribution of the model lies in its ability to reproduce statistics of the ANF response (mean latency, jitter, and firing probability) under both monophasic and cathodic-anodic biphasic stimulation, without changing the model's parameters. The response statistics of the model depend on stimulus level and duration of the stimulating pulse, reproducing trends observed in the ANF. In the case of biphasic stimulation, the model reproduces the effects of pseudomonophasic pulse shapes and also the dependence on the interphase gap (IPG) of the stimulus pulse, an effect that is quantitatively reproduced. The model is fitted to ANF data using a procedure that uniquely determines each model parameter. It is thus possible to rapidly parameterize a large population of neurons to reproduce a given set of response statistic distributions. Our work extends the stochastic leaky integrate and fire (SLIF) neuron, a well-studied phenomenological model of the electrically stimulated neuron. We extend the SLIF neuron so as to produce a realistic latency distribution by delaying the moment of spiking. During this delay, spiking may be abolished by anodic current. By this means, the probability of the model neuron responding to a stimulus is reduced when a trailing phase of opposite polarity is introduced. By introducing a minimum wait period that must elapse before a spike may be emitted, the model is able to reproduce the differences in the threshold level observed in the ANF for monophasic and biphasic stimuli. Thus, the ANF response to a large variety of pulse shapes are reproduced correctly by this model.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing.

A Structural Theory of Pitch(1,2,3)

Musical notes can be ordered from low to high along a perceptual dimension called "pitch". A characteristic property of these sounds is their periodic waveform, and periodicity generally correlates with pitch. Thus, pitch is often described as the perceptual correlate of the periodicity of the sound's waveform. However, the existence and salience of pitch also depends in a complex way on other factors, in particular harmonic content. For example, periodic sounds made of high-order harmonics tend to have a weaker pitch than those made of low-order harmonics. Here we examine the theoretical proposition that pitch is the perceptual correlate of the regularity structure of the vibration pattern of the basilar membrane, across place and time-a generalization of the traditional view on pitch. While this proposition also attributes pitch to periodic sounds, we show that it predicts differences between resolved and unresolved harmonic complexes and a complex domain of existence of pitch, in agreement with psychophysical experiments. We also present a possible neural mechanism for pitch estimation based on coincidence detection, which does not require long delays, in contrast with standard temporal models of pitch.

Dynamic musical communication of core affect

Is there something special about the way music communicates feelings? Theorists since Meyer (1956) have attempted to explain how music could stimulate varied and subtle affective experiences by violating learned expectancies, or by mimicking other forms of social interaction. Our proposal is that music speaks to the brain in its own language; it need not imitate any other form of communication. We review recent theoretical and empirical literature, which suggests that all conscious processes consist of dynamic neural events, produced by spatially dispersed processes in the physical brain. Intentional thought and affective experience arise as dynamical aspects of neural events taking place in multiple brain areas simultaneously. At any given moment, this content comprises a unified "scene" that is integrated into a dynamic core through synchrony of neuronal oscillations. We propose that (1) neurodynamic synchrony with musical stimuli gives rise to musical qualia including tonal and temporal expectancies, and that (2) music-synchronous responses couple into core neurodynamics, enabling music to directly modulate core affect. Expressive music performance, for example, may recruit rhythm-synchronous neural responses to support affective communication. We suggest that the dynamic relationship between musical expression and the experience of affect presents a unique opportunity for the study of emotional experience. This may help elucidate the neural mechanisms underlying arousal and valence, and offer a new approach to exploring the complex dynamics of the how and why of emotional experience.

Mode-locking neurodynamics predict human auditory brainstem responses to musical intervals

The auditory nervous system is highly nonlinear. Some nonlinear responses arise through active processes in the cochlea, while others may arise in neural populations of the cochlear nucleus, inferior colliculus and higher auditory areas. In humans, auditory brainstem recordings reveal nonlinear population responses to combinations of pure tones, and to musical intervals composed of complex tones. Yet the biophysical origin of central auditory nonlinearities, their signal processing properties, and their relationship to auditory perception remain largely unknown. Both stimulus components and nonlinear resonances are well represented in auditory brainstem nuclei due to neural phase-locking. Recently mode-locking, a generalization of phase-locking that implies an intrinsically nonlinear processing of sound, has been observed in mammalian auditory brainstem nuclei. Here we show that a canonical model of mode-locked neural oscillation predicts the complex nonlinear population responses to musical intervals that have been observed in the human brainstem. The model makes predictions about auditory signal processing and perception that are different from traditional delay-based models, and may provide insight into the nature of auditory population responses. We anticipate that the application of dynamical systems analysis will provide the starting point for generic models of auditory population dynamics, and lead to a deeper understanding of nonlinear auditory signal processing possibly arising in excitatory-inhibitory networks of the central auditory nervous system. This approach has the potential to link neural dynamics with the perception of pitch, music, and speech, and lead to dynamical models of auditory system development.

Neurodynamics, tonality, and the auditory brainstem response

Tonal relationships are foundational in music, providing the basis upon which musical structures, such as melodies, are constructed and perceived. A recent dynamic theory of musical tonality predicts that networks of auditory neurons resonate nonlinearly to musical stimuli. Nonlinear resonance leads to stability and attraction relationships among neural frequencies, and these neural dynamics give rise to the perception of relationships among tones that we collectively refer to as tonal cognition. Because this model describes the dynamics of neural populations, it makes specific predictions about human auditory neurophysiology. Here, we show how predictions about the auditory brainstem response (ABR) are derived from the model. To illustrate, we derive a prediction about population responses to musical intervals that has been observed in the human brainstem. Our modeled ABR shows qualitative agreement with important features of the human ABR. This provides a source of evidence that fundamental principles of auditory neurodynamics might underlie the perception of tonal relationships, and forces reevaluation of the role of learning and enculturation in tonal cognition.

Sensitivity of cochlear nucleus neurons to spatio-temporal changes in auditory nerve activity

The spatio-temporal pattern of auditory nerve (AN) activity, representing the relative timing of spikes across the tonotopic axis, contains cues to perceptual features of sounds such as pitch, loudness, timbre, and spatial location. These spatio-temporal cues may be extracted by neurons in the cochlear nucleus (CN) that are sensitive to relative timing of inputs from AN fibers innervating different cochlear regions. One possible mechanism for this extraction is "cross-frequency" coincidence detection (CD), in which a central neuron converts the degree of coincidence across the tonotopic axis into a rate code by preferentially firing when its AN inputs discharge in synchrony. We used Huffman stimuli (Carney LH. J Neurophysiol 64: 437-456, 1990), which have a flat power spectrum but differ in their phase spectra, to systematically manipulate relative timing of spikes across tonotopically neighboring AN fibers without changing overall firing rates. We compared responses of CN units to Huffman stimuli with responses of model CD cells operating on spatio-temporal patterns of AN activity derived from measured responses of AN fibers with the principle of cochlear scaling invariance. We used the maximum likelihood method to determine the CD model cell parameters most likely to produce the measured CN unit responses, and thereby could distinguish units behaving like cross-frequency CD cells from those consistent with same-frequency CD (in which all inputs would originate from the same tonotopic location). We find that certain CN unit types, especially those associated with globular bushy cells, have responses consistent with cross-frequency CD cells. A possible functional role of a cross-frequency CD mechanism in these CN units is to increase the dynamic range of binaural neurons that process cues for sound localization.

Rhythm-induced spike-timing patterns characterized by 1D firing maps

We explore patterns in the spike timing of neurons receiving periodic inputs, with an emphasis on stable characteristics which are realized in both models and in-vitro whole-cell recordings. We report on whole-cell recordings of pyramidal CA1 cells from rat hippocampus and entorhinal cortex and compare this data to model simulations. Cells were injected with a constant current to induce a steady firing rate and then a modest rhythm was added which altered the spike times and their corresponding phases relative to the rhythm. For both experiment and theory the relationship between consecutive spike phases is characterized by a probability distribution with peaks concentrated near a one-dimensional firing map. As is well-known, stable fixed points of this map correspond to the neuron phase-locking to the rhythm. We show that the interaction between noise and sufficiently steep maps can also cause a new kind of spike-time organization, in which consecutive spike time pairs organize into discrete clusters, with transitions between these clusters proceeding in a fixed sequence. This structure is not just a vestige of the noise-free dynamics. This slow dynamics and temporal organization in the relationship between consecutive spike phases is not evident in either the neuron's voltage traces or single phase or interspike interval histograms. Furthermore, the consecutive spike relationship is also evident in consecutive ISIs, and hence this ordering can be observed without detailed knowledge of the rhythm (e.g. without concurrent LFP recordings).

Neuronal spike-train responses in the presence of threshold noise

The variability of neuronal firing has been an intense topic of study for many years. From a modelling perspective it has often been studied in conductance based spiking models with the use of additive or multiplicative noise terms to represent channel fluctuations or the stochastic nature of neurotransmitter release. Here we propose an alternative approach using a simple leaky integrate-and-fire model with a noisy threshold. Initially, we develop a mathematical treatment of the neuronal response to periodic forcing using tools from linear response theory and use this to highlight how a noisy threshold can enhance downstream signal reconstruction. We further develop a more general framework for understanding the responses to large amplitude forcing based on a calculation of first passage times. This is ideally suited to understanding stochastic mode-locking, for which we numerically determine the Arnol'd tongue structure. An examination of data from regularly firing stellate neurons within the ventral cochlear nucleus, responding to sinusoidally amplitude modulated pure tones, shows tongue structures consistent with these predictions and highlights that stochastic, as opposed to deterministic, mode-locking is utilised at the level of the single stellate cell to faithfully encode periodic stimuli.

Response of Morris-Lecar neurons to various stimuli

We studied the responses of three classes of Morris-Lecar neurons to sinusoidal inputs and synaptic pulselike stimuli with deterministic and random interspike intervals (ISIs). It was found that the responses of the output frequency of class 1 and 2 neurons showed similar evolution properties by varying input amplitudes and frequencies, whereas class 3 neuron exhibited substantially different properties. Specifically, class 1 and 2 neurons display complicated phase locking (p : q, p > q, denoting output action potentials per input spikes) in low-frequency sinusoidal input area when the input amplitude is above their threshold, but a class 3 neuron does not fire action potentials in this area even if the amplitude is much higher. In the case of the deterministic ISI synaptic injection, all the three classes of neurons oscillate spikes with an arbitrary small frequency. When increasing the input frequency (both sinusoidal and deterministic ISI synaptic inputs), all neurons display 1 : 1 phase locking, whereas the response frequency decreases even fall to zero in the high-frequency input area. When the random ISI synaptic pulselike stimuli are injected into the neurons, one can clearly see the low-pass filter behaviors from the return map. The output ISI distribution depends on the mean ISI of input train as well as the ISI variation. Such different responses of three classes of neurons result from their distinct dynamical mechanisms of action potential initiation. It was suggested that the intrinsic dynamical cellular properties are very important to neuron information processing.

Hearing loss alters quantal release at cochlear nucleus stellate cells

OBJECTIVES/HYPOTHESIS

Auditory nerve synapses in ventral cochlear nucleus end on two principal cell types, bushy and stellate cells. Although the effects of hearing loss on bushy cells have been well studied, little is known about the effects of hearing loss on synaptic input to the stellate cells. Based on prior observations in bushy cells, we hypothesized that noise-induced hearing loss (NIHL) would decrease quantal release onto stellate cells.

STUDY DESIGN

Prospective, randomized animal study.

METHODS

CBA/CaJ mice were exposed for 2 hours to 98 dB sound pressure level (SPL) 8- to 16-kHz noise to produce a temporary threshold shift (TTS) or 114 dB SPL to produce a permanent threshold shift (PTS). Spontaneous miniature excitatory postsynaptic currents (mEPSCs) were then measured in stellate cells in brain slices of the cochlear nucleus.

RESULTS

Click auditory brainstem evoked response thresholds were elevated by 35 dB in both TTS and PTS mice. Spontaneous mEPSC frequency was found to be five-fold higher than normal in stellate cells of TTS mice and three-fold higher in PTS mice. The mEPSC amplitude was also larger in PTS mice. The mEPSC time course was not different between experimental and control groups.

CONCLUSIONS

The dramatic increase in mEPSC frequency after NIHL was not expected. The increase in mEPSC amplitude in PTS mice suggests a postsynaptic remodeling process. Both of these effects could contribute to increased spontaneous firing in the cochlear nucleus in the absence of sound. Our results also suggest that hearing loss may have different effects at auditory nerve synapses on bushy and stellate cells in the VCN.