On the mechanism of response latencies in auditory nerve fibers

Despite the structural differences of the middle and inner ears, the latency pattern in auditory nerve fibers to an identical sound has been found similar across numerous species. Studies have shown the similarity in remarkable species with distinct cochleae or even without a basilar membrane. This stimulus-, neuron-, and species- independent similarity of latency cannot be simply explained by the concept of cochlear traveling waves that is generally accepted as the main cause of the neural latency pattern. An original concept of Fourier pattern is defined, intended to characterize a feature of temporal processing—specifically phase encoding—that is not readily apparent in more conventional analyses. The pattern is created by marking the first amplitude maximum for each sinusoid component of the stimulus, to encode phase information. The hypothesis is that the hearing organ serves as a running analyzer whose output reflects synchronization of auditory neural activity consistent with the Fourier pattern. A combined research of experimental, correlational and meta-analysis approaches is used to test the hypothesis. Manipulations included phase encoding and stimuli to test their effects on the predicted latency pattern. Animal studies in the literature using the same stimulus were then compared to determine the degree of relationship. The results show that each marking accounts for a large percentage of a corresponding peak latency in the peristimulus-time histogram. For each of the stimuli considered, the latency predicted by the Fourier pattern is highly correlated with the observed latency in the auditory nerve fiber of representative species. The results suggest that the hearing organ analyzes not only amplitude spectrum but also phase information in Fourier analysis, to distribute the specific spikes among auditory nerve fibers and within a single unit. This phase-encoding mechanism in Fourier analysis is proposed to be the common mechanism that, in the face of species differences in peripheral auditory hardware, accounts for the considerable similarities across species in their latency-by-frequency functions, in turn assuring optimal phase encoding across species. Also, the mechanism has the potential to improve phase encoding of cochlear implants

The what and where of adding channel noise to the Hodgkin-Huxley equations

One of the most celebrated successes in computational biology is the Hodgkin-Huxley framework for modeling electrically active cells. This framework, expressed through a set of differential equations, synthesizes the impact of ionic currents on a cell's voltage -- and the highly nonlinear impact of that voltage back on the currents themselves -- into the rapid push and pull of the action potential. Latter studies confirmed that these cellular dynamics are orchestrated by individual ion channels, whose conformational changes regulate the conductance of each ionic current. Thus, kinetic equations familiar from physical chemistry are the natural setting for describing conductances; for small-to-moderate numbers of channels, these will predict fluctuations in conductances and stochasticity in the resulting action potentials. At first glance, the kinetic equations provide a far more complex (and higher-dimensional) description than the original Hodgkin-Huxley equations. This has prompted more than a decade of efforts to capture channel fluctuations with noise terms added to the Hodgkin-Huxley equations. Many of these approaches, while intuitively appealing, produce quantitative errors when compared to kinetic equations; others, as only very recently demonstrated, are both accurate and relatively simple. We review what works, what doesn't, and why, seeking to build a bridge to well-established results for the deterministic Hodgkin-Huxley equations. As such, we hope that this review will speed emerging studies of how channel noise modulates electrophysiological dynamics and function. We supply user-friendly Matlab simulation code of these stochastic versions of the Hodgkin-Huxley equations on the ModelDB website (accession number 138950) and http://www.amath.washington.edu/~etsb/tutorials.html.Comment: 14 pages, 3 figures, review articl

The psychophysics of absolute threshold and signal duration: A probabilistic approach

The absolute threshold for a tone depends on its duration; longer tones have lower thresholds. This effect has traditionally been explained in terms of ?temporal integration? involving the summation of energy or perceptual information over time. An alternative probabilistic explanation of the process is formulated in terms of simple equations that predict not only the time=duration dependence but also the shape of the psychometric function at absolute threshold. It also predicts a tight relationship between these two functions. Measurements made using listeners with either normal or impaired hearing show that the probabilistic equations adequately fit observed threshold-duration functions and psychometric functions. The mathematical formulation implies that absolute threshold can be construed as a two-valued function: (a) gain and (b) sensory threshold, and both parameters can be estimated from threshold-duration data. Sensorineural hearing impairment is sometimes associated with a smaller threshold=duration effect and sometimes with steeper psychometric functions. The equations explain why these two effects are expected to be linked. The probabilistic approach has the potential to discriminate between hearing deficits involving gain reduction and those resulting from a raised sensory threshold

Temporal Coding of Periodicity Pitch in the Auditory System: An Overview

This paper outlines a taxonomy of neural pulse codes and reviews neurophysiological evidence for interspike interval-based representations for pitch and timbre in the auditory nerve and cochlear nucleus. Neural pulse codes can be divided into channel-based codes, temporal-pattern codes, and time-of-arrival codes. Timings of discharges in auditory nerve fibers reflect the time structure of acoustic waveforms, such that the interspike intervals that are produced precisely convey information concerning stimulus periodicities. Population-wide inter-spike interval distributions are constructed by summing together intervals from the observed responses of many single Type I auditory nerve fibers. Features in such distributions correspond closely with pitches that are heard by human listeners. The most common all-order interval present in the auditory nerve array almost invariably corresponds to the pitch frequency, whereas the relative fraction of pitchrelated intervals amongst all others qualitatively corresponds to the strength of the pitch. Consequently, many diverse aspects of pitch perception are explained in terms of such temporal representations. Similar stimulus-driven temporal discharge patterns are observed in major neuronal populations of the cochlear nucleus. Population-interval distributions constitute an alternative time-domain strategy for representing sensory information that complements spatially organized sensory maps. Similar autocorrelation-like representations are possible in other sensory systems, in which neural discharges are time-locked to stimulus waveforms

Cortical And Subcortical Mechanisms For Sound Processing

The auditory cortex is essential for encoding complex and behaviorally relevant sounds. Many questions remain concerning whether and how distinct cortical neuronal subtypes shape and encode both simple and complex sound properties. In chapter 2, we tested how neurons in the auditory cortex encode water-like sounds perceived as natural by human listeners, but that we could precisely parametrize. The stimuli exhibit scale-invariant statistics, specifically temporal modulation within spectral bands scaled with the center frequency of the band. We used chronically implanted tetrodes to record neuronal spiking in rat primary auditory cortex during exposure to our custom stimuli at different rates and cycle-decay constants. We found that, although neurons exhibited selectivity for subsets of stimuli with specific statistics, over the population responses were stable. These results contribute to our understanding of how auditory cortex processes natural sound statistics. In chapter 3, we review studies examining the role of different cortical inhibitory interneurons in shaping sound responses in auditory cortex. We identify the findings that support each other and the mechanisms that remain unexplored. In chapter 4, we tested how direct feedback from auditory cortex to the inferior colliculus modulated sound responses in the inferior colliculus. We optogenetically activated or suppressed cortico-collicular feedback while recording neuronal spiking in the mouse inferior colliculus in response to pure tones and dynamic random chords. We found that feedback modulated sound responses by reducing sound selectivity by decreasing responsiveness to preferred frequencies and increasing responsiveness to less preferred frequencies. Furthermore, we tested the effects of perturbing intra-cortical inhibitory-excitatory networks on sound responses in the inferior colliculus. We optogenetically activated or suppressed parvalbumin-positive (PV) and somatostatin-positive (SOM) interneurons while recording neuronal spiking in mouse auditory cortex and inferior colliculus. We found that modulation of neither PV- nor SOM-interneurons affected sound-evoked responses in the inferior colliculus, despite significant modulation of cortical responses. Our findings imply that cortico-collicular feedback can modulate responses to simple and complex auditory stimuli independently of cortical inhibitory interneurons. These experiments elucidate the role of descending auditory feedback in shaping sound responses. Together these results implicate the importance of the auditory cortex in sound processing

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

Role of Inhibition in Binaural Processing

The medial and lateral superior olives (MSO, LSO) are the lowest order cell groups in the mammalian auditory circuit to receive massive binaural input. The MSO functions in part to encode interaural time differences (ITD), the predominant cue for localization of low frequency sounds. Binaural inputs to the MSO consist of excitatory projections from the cochlear nuclei (CN) and inhibitory projections from both the medial nucleus of the trapezoid body (MNTB) and lateral nucleus of the trapezoid body (LNTB). The interaction of excitatory and inhibitory currents within an MSO cell\u27s soma and dendrites over the backdrop of its intrinsic ionic conductances imbues ITD sensitivity to these neurons. Lloyd Jeffress proposed a coincidence detection circuit in which arrays of neurons receive sub-threshold excitatory inputs via delay lines that represent sound location as a place code of activity patterns within the cell group (Jeffress, 1948). The Jeffress place code model later found a neural instantiation in the MSO. Recent in vivo (McAlpine et al., 2001; Brand et al., 2002) studies have shown that peak discharge rates do not fall within the ecological range as the Jeffress model predicts but instead ITD is coded by changes in discharge rate. The timing of inhibition relative to excitation modulates the discharge rates of MSO cells (Brand et al., 2002; Chirila et al., 2007); however, the details of this circuit, such as the onset time of inhibition, are not well known. Although the MNTB and LNTB have been investigated in vivo and in vitro , they have not been well characterized with respect to their function in ITD processing in larger mammals. Additionally, inhibition is modulated by anesthesia and confounds in vivo experiments that examine the careful interplay of excitatory and inhibitory effects in the MSO. For this reason, these physiological experiments were performed on decerebrate unanaesthetized animals. Further investigation of the anatomical organization of inhibitory inputs was carried out as the basis for a comprehensive model of the MSO that incorporates the effects of binaural inhibiting projections to MSO neurons.;Unbiased stereological counts of the MNTB, MSO and subdivisions of the LNTB showed that the MSO and MNTB contain approximately the same number of cells. The main (m)LNTB, posteroventral (pv)LNTB and the hilus (h)LNTB are estimated to contain 3800, 1400, and 200 neurons respectively. Tonotopic organization of the MNTB and MSO show that in the low frequency area, MSO cells outnumber MNTB cells 2 to 1, suggesting a divergent innervation of the MSO from the MNTB. Injection of the retrograde tracer, biotinylated dextrane amine, in the MSO, labeled cells in the MNTB, pvLNTB and mLNTB and defines the important role that these sub-nuclei, and in particular the pvLNTB, have in ITD coding. Computational modeling of a single MSO cell suggests that when two sources of inhibition temporally frame excitation the coincidence detection window is refined and less sensitive to temporal fluctuations that otherwise might degrade ITD sensitivity. Finally, physiological properties of MNTB cells reveal a heterogeneous population of responses and less precise temporal coding than are found in their inputs, globular bushy cells

Dynamical laser spike processing

Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate that graphene-coupled laser systems offer a unified low-level spike optical processing paradigm that goes well beyond previously studied laser dynamics. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation---fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system, but the addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.Comment: 13 pages, 7 figure