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After hearing loss retrograde degeneration of spiral ganglion neurons (SGNs) has been described. Studies modeling the effects of degeneration mostly omitted peripheral processes (dendrites). Recent experimental observations indicated that degenerating SGNs manifested also a reduced diameter of their dendrites. We simulated populations of 400 SGNs inside a high resolution cochlear model with a cochlear implant, based on μCT scans of a human temporal bone. Cochlear implant stimuli were delivered as biphasic pulses in a monopolar configuration. Three SGN situations were simulated, based on our previous measurements of human SGN dendrites: (A) SGNs with intact dendrites (before degeneration), (B) degenerating SGNs, dendrites with a smaller diameter but original length, (C) degenerating SGNs, dendrites omitted. SGN fibers were mapped to characteristic frequency, and place pitch was estimated from excitation profiles. Results from degenerating SGNs (B, C) were similar. Most action potentials were initiated in the somatic area for all cases (A, B, C), except for areas near stimulating electrodes in the apex with intact SGNs (A), where action potentials were initiated in the distal dendrite. In most cases, degenerating SGNs had lower thresholds than intact SGNs (A) (down to –2 dB). Excitation profiles showed increased ectopic activation, i.e., activation of unintended neuronal regions, as well as similar neuronal regions excited by different apical electrodes, for degenerating SGNs (B, C). The estimated pitch showed cases of pitch reversals in apical electrodes for intact SGNs (A), as well as mostly identical pitches evoked by the four most apical electrodes for degenerating SGNs (B, C). In conclusion, neuronal excitation profiles to electrical stimulation exhibited similar traits in both ways of modeling SGN degeneration. Models showed degeneration of dendrites caused increased ectopic activation, as well as similar excitation profiles and pitch evoked by different apical electrodes. Therefore, insertion of electrodes beyond approximately 450° may not provide any benefit if SGN dendrites are degenerated.

Auditory nerve fibers (ANF) convey information about sound to the central nervous system. For both normal hearing subjects and cochlear implant patients the most drastic step of sound coding for neuronal processing is when the analog signal is converted into discrete nerve-action potentials. As any information lost during this process is no longer available for neural processing, it is important to understand the underlying principles of sound coding in the intact auditory system and the limitations in the case of direct electrical stimulation of the auditory nerve. Here we focus on a model of spiral ganglion type I neurons with Hodgkin-Huxley type ion channels, which are also found in cochlear nucleus neurons. Depending on the task, we model the neurons at different levels of detail. Our results show that for acoustic stimuli, the model provides realistic refractoriness and generates more realistic spike trains compared to a spike generator with absolute and exponentially decaying refractoriness added. Not surprisingly, speech discrimination in electrical hearing is lower than in acoustic hearing. On the other hand, the analysis of transmitted information shows that the temporal precision of coding seems to be very high because at levels well above threshold, action potentials are elicited quasi-deterministic by the electrical stimuli. We argue that CIS strategies a) waste as much as 50

The auditory pathway is an excellent system to study temporal aspects of neuronal processing. Unlike other sensory systems, temporal cues cover an extremely wide range of information: for sound localization, interaural time differences with a precision of tens of microseconds are extracted. Phase-locking of auditory nerve responses, which is related to the coding of the temporal fine structure, occurs from the lowest audible frequencies probably up to 3 kHz in humans. Amplitude modulations in speech signals are processed in the ms to tens of ms range. And finally, the energy of spoken speech itself is modulated with a frequency of about 4 Hz, corresponding to a syllable frequency in the order of few hundreds of ms. To extract temporal cues at all timescales, it is important to understand how temporal information is coded. We investigate temporal coding of speech signals using the methods of information theory and a model of the human inner ear. The model is based on a traveling-wave model, a nonlinear compression stage which mimics the function of the "cochlear amplifier", a model of the sensory cells, the afferent synapse and spike generation (Sumner ) which we extended to replicate "offset adaptation" (Zhang). We used the action potentials of the auditory nerve to drive Hodgkin-Huxley-type point models of various neurons in the cochlear nucleus. In this investigation we only report data from onset neurons, which exhibit extraordinary fast membrane time-constants below 1 ms. Onset neurons are known for their precise temporal processing. They achieve precisely timed action potentials by coincidence detection: they fire only if at least 10% of the auditory nerve fibers which innervate them fire synchronously. With information theory, we analyzed the transmitted information rate coded in neural spike trains of modeled neurons in the cochlear nucleus for vowels. We found that onset neurons are able to code temporal information with sub-millisecond precision (

Our study focuses on biophysical modeling of the auditory periphery and initial stages of neural processing. We examined in detail synaptic excitation between inner hair cells and spiral ganglion type I neurons. Spiral ganglion neurons encode and convey information about sound to the central nervous system in the form of action potentials. For the purpose of our study we utilized a biophysical model of the auditory periphery proposed by Sumner (2002). It consists of outer/middle ear filters, a basilar membrane filter bank, an inner hair cell model coupled with complex vesicle pool dynamics at the presynaptic membrane. Finally, fusion of vesicles, modelled with a probabilistic function, releases neurotransmitter into the synaptic cleft. Response of auditory nerve fibers is modeled with a spike generator. The absolute refractory period is set to 0.75 ms and the relative refractory period is modelled with an exponentially decaying function. In our approach we substituted the artificial spike generation and refraction model with a more realistic spiral ganglion neuron model with Hodgkin-Huxley type ion channels proposed by Negm and Bruce (2008). The model included several channels also found in cochlear nucleus neurons (K_A, K_ht, K_lt). Our model consisted of the postsynaptic bouton (1.5x1.7µm) from high-spontaneous rate fibers. We coupled the model of the synapse with the spiral ganglion neuron using a synaptic excitation model fitted to results from Glowatzki and Fuchs' (2002) experiments, who conducted patch clamp measurements at the afferent postsynapse. We verified our hybrid model against various experiments, mostly pure tone stimulation. Rate intensity functions fitted experimental data well, rates varied from about 40 spikes/s to a maximum of 260 spikes/s. Adaptation properties were investigated with peri-stimulus time histograms (PSTH). As adaptation is mainly governed by vesicle pool dynamics, only small changes occurred compared with the statistical spike generation model and adaptation was consistent with experiments. Interestingly, Hodgkin-Huxley models of spiral ganglion neurons exhibited a notch visible in the PSTH after rapid adaptation that could also be observed in experiments. This was not revealed by the statistical spike generator. The fiber's refractory period was investigated using inter-spike interval histograms. The refractory period varied with simulus intensity from 1ms (spontaneous activity) to 0.7ms (84dB_SPL). We also analyzed phase locking with the synchronization index. It was slightly lower compared to the statistical spike generator. By varying the density of K_lt and K_A channels, we could replicate heterogenity of auditory nerve fibers as shown by Adamson et al. (2002). In summary, replacing the statistical spike generation model with a more realistic model of the postsynaptic membrane obsoletes the introduction of non-physiologic parameters for absolute and relative refraction. It improves the refractory behaviour and provides more realistic spike trains of the auditory nerve. Acknowledgments: Supported by within the Munich Bernstein Center for Computational Neuroscience by the German Federal Ministry of Education and Research (reference numbers 01GQ0441 and 01GQ0443).