1,819 research outputs found
A point process framework for modeling electrical stimulation of the auditory nerve
Model-based studies of auditory nerve responses to electrical stimulation can
provide insight into the functioning of cochlear implants. Ideally, these
studies can identify limitations in sound processing strategies and lead to
improved methods for providing sound information to cochlear implant users. To
accomplish this, models must accurately describe auditory nerve spiking while
avoiding excessive complexity that would preclude large-scale simulations of
populations of auditory nerve fibers and obscure insight into the mechanisms
that influence neural encoding of sound information. In this spirit, we develop
a point process model of the auditory nerve that provides a compact and
accurate description of neural responses to electric stimulation. Inspired by
the framework of generalized linear models, the proposed model consists of a
cascade of linear and nonlinear stages. We show how each of these stages can be
associated with biophysical mechanisms and related to models of neuronal
dynamics. Moreover, we derive a semi-analytical procedure that uniquely
determines each parameter in the model on the basis of fundamental statistics
from recordings of single fiber responses to electric stimulation, including
threshold, relative spread, jitter, and chronaxie. The model also accounts for
refractory and summation effects that influence the responses of auditory nerve
fibers to high pulse rate stimulation. Throughout, we compare model predictions
to published physiological data and explain differences in auditory nerve
responses to high and low pulse rate stimulation. We close by performing an
ideal observer analysis of simulated spike trains in response to sinusoidally
amplitude modulated stimuli and find that carrier pulse rate does not affect
modulation detection thresholds.Comment: 1 title page, 27 manuscript pages, 14 figures, 1 table, 1 appendi
A Model of Electrically Stimulated Auditory Nerve Fiber Responses with Peripheral and Central Sites of Spike Generation
A computational model of cat auditory nerve fiber (ANF) responses to electrical stimulation is presented. The model assumes that (1) there exist at least two sites of spike generation along the ANF and (2) both an anodic (positive) and a cathodic (negative) charge in isolation can evoke a spike. A single ANF is modeled as a network of two exponential integrate-and-fire point-neuron models, referred to as peripheral and central axons of the ANF. The peripheral axon is excited by the cathodic charge, inhibited by the anodic charge, and exhibits longer spike latencies than the central axon; the central axon is excited by the anodic charge, inhibited by the cathodic charge, and exhibits shorter spike latencies than the peripheral axon. The model also includes subthreshold and suprathreshold adaptive feedback loops which continuously modify the membrane potential and can account for effects of facilitation, accommodation, refractoriness, and spike-rate adaptation in ANF. Although the model is parameterized using data for either single or paired pulse stimulation with monophasic rectangular pulses, it correctly predicts effects of various stimulus pulse shapes, stimulation pulse rates, and level on the neural response statistics. The model may serve as a framework to explore the effects of different stimulus parameters on psychophysical performance measured in cochlear implant listeners
A phenomenological model of myelinated nerve with a dynamic threshold
To evaluate coding strategies for cochlear implants a model of the human cochlear nerve is required. Nerve models based on voltage-clamp experiments, such as the Frankenhaeuser-Huxley model of myelinated nerve, can have over forty parameters and are not amenable for fitting to physiological data from a different animal or type of nerve. Phenomenological nerve models, such as leaky integrate-and-fire (LIF) models, have fewer parameters but have not been validated with a wide range of stimuli. In the absence of substantial cochlear nerve data, we have used data from a toad sciatic nerve for validation (50 Hz to 2 kHz with levels up to 20 dB above threshold). We show that the standard LIF model with fixed refractory properties and a single set of parameters cannot adequately predict the toad rate-level functions. Given the deficiency of this standard model, we have abstracted the dynamics of the sodium inactivation variable in the Frankenhaeuser-Huxley model to develop a phenomenological LIF model with a dynamic threshold. This nine-parameter model predicts the physiological rate-level functions much more accurately than the standard LIF model. Because of the low number of parameters, we expect to be able to optimize the model parameters so that the model is more appropriate for cochlear implant simulations
Neural Circuit Reconfiguration by Social Status
The social rank of an animal is distinguished by its behavior relative to others in its community. Although social-status-dependent differences in behavior must arise because of differences in neural function, status-dependent differences in the underlying neural circuitry have only begun to be described. We report that dominant and subordinate crayfish differ in their behavioral orienting response to an unexpected unilateral touch, and that these differences correlate with functional differences in local neural circuits that mediate the responses. The behavioral differences correlate with simultaneously recorded differences in leg depressor muscle EMGs and with differences in the responses of depressor motor neurons recorded in reduced, in vitro preparations from the same animals. The responses of local serotonergic interneurons to unilateral stimuli displayed the same status-dependent differences as the depressor motor neurons. These results indicate that the circuits and their intrinsic serotonergic modulatory components are configured differently according to social status, and that these differences do not depend on a continuous descending signal from higher centers
A Bio-Polymer Transistor: Electrical Amplification by Microtubules
Microtubules (MTs) are important cytoskeletal structures, engaged in a number
of specific cellular activities, including vesicular traffic, cell
cyto-architecture and motility, cell division, and information processing
within neuronal processes. MTs have also been implicated in higher neuronal
functions, including memory, and the emergence of "consciousness". How MTs
handle and process electrical information, however, is heretofore unknown. Here
we show new electrodynamic properties of MTs. Isolated, taxol-stabilized
microtubules behave as bio-molecular transistors capable of amplifying
electrical information. Electrical amplification by MTs can lead to the
enhancement of dynamic information, and processivity in neurons can be
conceptualized as an "ionic-based" transistor, which may impact among other
known functions, neuronal computational capabilities.Comment: This is the final submitted version. The published version should be
downloaded from Biophysical Journa
A Model of Electrically Stimulated Auditory Nerve Fiber Responses with Peripheral and Central Sites of Spike Generation
A computational model of cat auditory nerve fiber (ANF) responses to electrical stimulation is presented. The model assumes that (1) there exist at least two sites of spike generation along the ANF and (2) both an anodic (positive) and a cathodic (negative) charge in isolation can evoke a spike. A single ANF is modeled as a network of two exponential integrateand-fire point-neuron models, referred to as peripheral and central axons of the ANF. The peripheral axon is excited by the cathodic charge, inhibited by the anodic charge, and exhibits longer spike latencies than the central axon; the central axon is excited by the anodic charge, inhibited by the cathodic charge, and exhibits shorter spike latencies than the peripheral axon. The model also includes subthreshold and suprathreshold adaptive feedback loops which continuously modify the membrane potential and can account for effects of facilitation, accommodation, refractoriness, and spike-rate adaptation in ANF. Although the model is parameterized using data for either single or paired pulse stimulation with monophasic rectangular pulses, it correctly predicts effects of various stimulus pulse shapes, stimulation pulse rates, and level on the neural response statistics. The model may serve as a framework to explore the effects of different stimulus parameters on psychophysical performance measured in cochlear implant listeners
A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP
Humans perform remarkably well in many cognitive tasks including pattern
recognition. However, the neuronal mechanisms underlying this process are not
well understood. Nevertheless, artificial neural networks, inspired in brain
circuits, have been designed and used to tackle spatio-temporal pattern
recognition tasks. In this paper we present a multineuronal spike pattern
detection structure able to autonomously implement online learning and
recognition of parallel spike sequences (i.e., sequences of pulses belonging to
different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike
latency, that enables neurons to fire spikes with a certain delay and
heterosynaptic plasticity, that allows the own regulation of synaptic weights.
From the perspective of the information representation, the structure allows
mapping a spatio-temporal stimulus into a multidimensional, temporal, feature
space. In this space, the parameter coordinate and the time at which a neuron
fires represent one specific feature. In this sense, each feature can be
considered to span a single temporal axis. We applied our proposed scheme to
experimental data obtained from a motor inhibitory cognitive task. The test
exhibits good classification performance, indicating the adequateness of our
approach. In addition to its effectiveness, its simplicity and low
computational cost suggest a large scale implementation for real time
recognition applications in several areas, such as brain computer interface,
personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc
Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis
We show how the Equation-Free approach for multi-scale computations can be
exploited to systematically study the dynamics of neural interactions on a
random regular connected graph under a pairwise representation perspective.
Using an individual-based microscopic simulator as a black box coarse-grained
timestepper and with the aid of simulated annealing we compute the
coarse-grained equilibrium bifurcation diagram and analyze the stability of the
stationary states sidestepping the necessity of obtaining explicit closures at
the macroscopic level. We also exploit the scheme to perform a rare-events
analysis by estimating an effective Fokker-Planck describing the evolving
probability density function of the corresponding coarse-grained observables
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