Phenomenological model of auditory nerve population responses to cochlear implant stimulation.

BACKGROUND: Models of auditory nerve fiber (ANF) responses to electrical stimulation are helpful to develop advanced coding for cochlear implants (CIs). A phenomenological model of ANF population responses to CI electrical stimulation with a lower computational complexity compared to a biophysical model would be beneficial to evaluate new CI coding strategies. NEW METHOD: This study presents a phenomenological model which combines four temporal characteristics of ANFs (refractoriness, facilitation, accommodation and spike rate adaptation) in addition to a spatial spread of the electric field. RESULTS: The model predicts the performances of CI subjects in the melodic contour identification (MCI) experiment. The simulations for the MCI experiment were consistent with CI recipients' experimental outcomes that were not predictable from the electrical stimulation patterns themselves. COMPARISON WITH EXISTING METHODS: Previously, no phenomenological population model of ANFs has combined all four aforementioned temporal phenomena. CONCLUSIONS: The proposed model would help the further investigations of ANFs responses to different electrical stimulation patterns and comparison of different sound coding strategies in CIs.

Hypsarrhythmia in epileptic spasms: Synchrony in chaos.

PURPOSE: Hypsarrhythmia is an electroencephalographic pattern associated with epileptic spasms and West syndrome. West syndrome is a devastating epileptic encephalopathy, originating in infancy. Hypsarrhythmia has been deemed to be the interictal brain activity, while the electrodecremental event associated with the spasms is denoted as the ictal event. Though characterized as chaotic, asynchronous and disorganized based on visual inspection of the EEG, little is known of the dynamics of hypsarrhythmia and how it impacts the developmental arrest of these infants. METHODS: As an exploratory and feasibility study, we explored the dynamics of both hypsarrhythmia and electrodecremental events with EEG phase synchronization methods, and in a convenience sample of three outpatients with epileptic spasms. As ictal events are associated with prolonged phase synchronization, we hypothesized that if hypsarrhythmia was indeed the interictal brain activity that it would have lower phase synchronization than the electrodecremental event (ictal phase). RESULTS: We calculated both the phase synchronization index and the temporal variability of the index in three patients with infantile spasms. Two patients had hypsarrhythmia and electrodecremental events and one had hemi-hypsarrhythmia. We found that the hypsarrhythmia pattern was a more synchronized state than the electrodecremental event. CONCLUSIONS: We have observed that the hypsarrhythmia pattern may represent a more synchronized state than the electrodecremental event in infants with epileptic spasms. However, larger studies are needed to replicate and validate these findings. Additionally, further inquiry is required to determine the impact that increased synchronization may have on developmental outcomes in infants with epileptic spasms.

Predictions of the Contribution of HCN Half-Maximal Activation Potential Heterogeneity to Variability in Intrinsic Adaptation of Spiral Ganglion Neurons.

Spiral ganglion neurons (SGNs) exhibit a wide range in their strength of intrinsic adaptation on a timescale of 10s to 100s of milliseconds in response to electrical stimulation from a cochlear implant (CI). The purpose of this study was to determine how much of that variability could be caused by the heterogeneity in half-maximal activation potentials of hyperpolarization-activated cyclic nucleotide-gated cation (HCN) channels, which are known to produce intrinsic adaptation. In this study, a computational membrane model of cat type I SGN was developed based on the Hodgkin-Huxley model plus HCN and low-threshold potassium (KLT) conductances in which the half-maximal activation potential of the HCN channel was varied and the response of the SGN to pulse train and paired-pulse stimulation was simulated. Physiologically plausible variation of HCN half-maximal activation potentials could indeed determine the range of adaptation on the timescale of 10s to 100s of milliseconds and recovery from adaptation seen in the physiological data while maintaining refractoriness within physiological bounds. This computational model demonstrates that HCN channels may play an important role in regulating the degree of adaptation in response to pulse train stimulation and therefore contribute to variable constraints on acoustic information coding by CIs. This finding has broad implications for CI stimulation paradigms in that cell-to-cell variation of HCN channel properties are likely to significantly alter SGN excitability and therefore auditory perception.

Temporal Considerations for Stimulating Spiral Ganglion Neurons with Cochlear Implants.

A wealth of knowledge about different types of neural responses to electrical stimulation has been developed over the past 100 years. However, the exact forms of neural response properties can vary across different types of neurons. In this review, we survey four stimulus-response phenomena that in recent years are thought to be relevant for cochlear implant stimulation of spiral ganglion neurons (SGNs): refractoriness, facilitation, accommodation, and spike rate adaptation. Of these four, refractoriness is the most widely known, and many perceptual and physiological studies interpret their data in terms of refractoriness without incorporating facilitation, accommodation, or spike rate adaptation. In reality, several or all of these behaviors are likely involved in shaping neural responses, particularly at higher stimulation rates. A better understanding of the individual and combined effects of these phenomena could assist in developing improved cochlear implant stimulation strategies. We review the published physiological data for electrical stimulation of SGNs that explores these four different phenomena, as well as some of the recent studies that might reveal the biophysical bases of these stimulus-response phenomena.

Learning a stick-balancing task involves task-specific coupling between posture and hand displacements.

Theories of motor learning argue that the acquisition of novel motor skills requires a task-specific organization of sensory and motor subsystems. We examined task-specific coupling between motor subsystems as subjects learned a novel stick-balancing task. We focused on learning-induced changes in finger movements and body sway and investigated the effect of practice on their coupling. Eight subjects practiced balancing a cylindrical wooden stick for 30 min a day during a 20 day learning period. Finger movements and center of pressure trajectories were recorded in every fifth practice session (4 in total) using a ten camera VICON motion capture system interfaced with two force platforms. Motor learning was quantified using average balancing trial lengths, which increased with practice and confirmed that subjects learned the task. Nonlinear time series and phase space reconstruction methods were subsequently used to investigate changes in the spatiotemporal properties of finger movements, body sway and their progressive coupling. Systematic increases in subsystem coupling were observed despite reduced autocorrelation and differences in the temporal properties of center of pressure and finger trajectories. The average duration of these coupled trajectories increased systematically across the learning period. In short, the abrupt transition between coupled and decoupled subsystem dynamics suggested that stick balancing is regulated by a hierarchical control mechanism that switches from collective to independent control of the finger and center of pressure. In addition to traditional measures of motor performance, dynamical analyses revealed changes in motor subsystem organization that occurred when subjects learned a novel stick-balancing task.

Contributions of delayed visual feedback and cognitive task load to postural dynamics.

In this experiment, we examined the extent to which postural control is influenced by visual and cognitive task performance. Fourteen healthy young participants performed a balance task in eyes-open (EO) and delayed visual feedback (DVF) conditions. DVF was presented at delays ranging from 0 to 1200ms in 300ms increments. Cognitive load was implemented by a simple, serial arithmetic task. High and low-pass filtering (f(c)=0.3Hz) distinguished LOW and HIGH frequency components, which were used to compute the variability of Anteroposterior (AP) Center of Pressure (COP) trajectories on fast (>0.3Hz) and slow (<0.3Hz) times cales. Imposed visual delay increased sway variability at both LOW and HIGH components. Cognitive task performance, however, influenced only the variability of fast (HIGH) sway components. Our results support distinct timescale mechanisms for postural control, but also demonstrate that vision predominantly influences low frequency components of postural sway. Moment-to-moment COP fluctuations are dependent on cognitive performance during delayed visual feedback postural control.

Stochastic two-delay differential model of delayed visual feedback effects on postural dynamics.

We report on experiments and modelling involving the 'visuo-postural control loop' in the upright stance. We experimentally manipulated an artificial delay to the visual feedback during standing, presented at delays ranging from 0 to 1 s in increments of 250 ms. Using stochastic delay differential equations, we explicitly modelled the centre-of-pressure (COP) and centre-of-mass (COM) dynamics with two independent delay terms for vision and proprioception. A novel 'drifting fixed point' hypothesis was used to describe the fluctuations of the COM with the COP being modelled as a faster, corrective process of the COM. The model was in good agreement with the data in terms of probability density functions, power spectral densities, short- and long-term correlations (Hurst exponents) as well the critical time between the two ranges.