A Point Process Model for Auditory Neurons Considering Both Their Intrinsic Dynamics and the Spectrotemporal Properties of an Extrinsic Signal

Med. Sch., Neurosci. Stat. Res. Lab., Harvard Univ., Boston, MA, USA
IEEE Transactions on Biomedical Engineering (Impact Factor: 2.35). 07/2011; 58(6):1507 - 1510. DOI: 10.1109/TBME.2011.2113349
Source: IEEE Xplore

ABSTRACT We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron's baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron's intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF.

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    • "Our approach is largely motivated by a specific family of point processes: generalized linear models (GLMs) (Paninski 2004; Truccolo et al. 2005). GLMs have emerged as an essential tool for modeling physiological data and investigating the coding and computational properties of neurons (Paninski 2004; Paninski et al. 2007), including auditory neurons (Trevino et al. 2010; Plourde et al. 2011). Moreover, they hold particular promise for applications to sensory neural prostheses because they have useful mathematical properties that permit efficient parameter fitting to spike train data (Paninski 2004), they can be used to optimally decode spike trains (Paninski et al. 2007), and they can be used in connection with real-time optimization methods to identify stimulation patters that control the timing of evoked spikes (Ahmadi et al. 2011). "
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    ABSTRACT: Model-based studies of responses of auditory nerve fibers 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 spiking activity 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 individual auditory nerve fibers 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 semianalytical 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 of response to high and low pulse rate stimulation. We find that the model, although constructed to fit data from single and paired pulse experiments, can accurately predict responses to unmodulated and modulated pulse train stimuli. 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.
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    ABSTRACT: Neuron spiking typically reflects both the intrinsic dynamics of the neuron as well as characteristics of extrinsic stimuli. In this paper, we compare the importance of the neuron’s internal dynamics and the spectro-temporal characteristics of an input speech stimulus in a previously developed point process model for auditory nerve spiking. Using a relative deviance measure and Kolmogorov-Smirnov (KS) analysis, we show that, for low spontaneous rate neurons, the spectro-temporal characteristics of the input speech stimulus are much more important than the neuron’s internal dynamics and, in fact, sufficient to explain the neuron spiking. However, for higher spontaneous rates, both are shown to be almost equally important to the spiking. The relative importance of one of these two aspects for spiking in the auditory nerve and their relevance in the neuron firing model therefore depends on the spontaneous rate of the neuron.
    International IEEE/EMBS Conference on Neural Engineering 01/2011; DOI:10.1109/NER.2011.5910477
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    ABSTRACT: Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.
    Network Computation in Neural Systems 03/2011; 22(1-4):97-125. DOI:10.3109/0954898X.2011.637606 · 0.87 Impact Factor
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