Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

Barbieri, Riccardo; Quirk, Michael C.; Frank, Loren M.; Wilson, Matthew A.; Brown, Emery N.

doi:10.1016/s0165-0270(00)00344-7

Cited by 180 publications

(192 citation statements)

References 28 publications

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“…Further development of this work is needed to make these methods a useful practical tool for spike train data analysis. First, because neural activity in many brain regions, including the hippocampus, is not best modeled as a Poisson process (26,27), applying the point process adaptive filter in the general (non-Poisson) framework in Eq. 2.8 is an important extension we are investigating.…”

Section: Discussionmentioning

confidence: 99%

An analysis of neural receptive field plasticity by point process adaptive filtering

Brown

Nguyen

Frank

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

167

115

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Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields.T he receptive fields of neurons are dynamic; that is, their responses to relevant stimuli change with experience. Experience-dependent change or plasticity has been documented in a number of brain regions (1-5). For example, in the cat visual system, retinal lesions lead to reorganization of cortical topography (3). Peripheral nerve sectioning can alter substantially the receptive fields of neurons in monkey somatosensory and motor cortices (6, 7). Similarly, the directional tuning of neural receptive fields in monkey motor cortex changes as the animal learns to compensate for an externally applied force field while moving a manipulandum (8). In the rat hippocampus, the system we study here, the pyramidal neurons in the CA1 region have spatial receptive fields. As a rat executes a behavioral task, a given CA1 neuron fires only in a restricted region of the experimental environment, termed the cell's spatial or place receptive field (9). Place fields change in a reliable manner as the animal executes its task (5, 10). When the experimental environment is a linear track, these spatial receptive fields on average migrate and skew in the direction opposite the cell's preferred direction of firing relative to the animal's movement and increase in scale and maximum firing rate (5, 10). Because receptive field plasticity is a characteristic of many neural systems, analysis of these dynamics from experimental measurements is crucial for understanding how different br...

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Section: Discussionmentioning

confidence: 99%

An analysis of neural receptive field plasticity by point process adaptive filtering

Brown

Nguyen

Frank

et al. 2001

Proc. Natl. Acad. Sci. U.S.A.

167

115

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“…At the beginning of INFORMATION AND STATISTICAL EFFICIENCY we noted that non-Poisson variation in spike trains is to be expected, and has been documented, under particular conditions (see also Barbieri et al 2001;Kass and Ventura 2001;Reich et al 1998; and the references therein). One phenomenon leading to non-Poisson behavior is the refractory period: immediately after a spike there is a short interval of time during which another spike is impossible and a longer interval of time during which the probability of a spike is reduced.…”

Section: P O I S S O N a N D N O N -P O I S S O N M O D E L Smentioning

confidence: 95%

“…It is also possible to use partially parametric fitting methods. Barbieri et al (2001) showed how Zernike polynomials may be used to characterize hippocampal place fields. These involve both Gaussian and non-Gaussian components, and are able to capture departures from Gaussian place field tuning.…”

Section: There Are Many Ways To Produce a Smooth Firing-rate Functionmentioning

confidence: 99%

“…When the ISIs are independent and from some particular (possibly non-exponential) distribution, the spike train, which is again homogeneous in time, is said to follow a renewal process. Barbieri et al (2001) showed how inhomogeneous versions of renewal processes may be constructed and fit to neuronal data by rescaling time (see also Brown et al 2002). Such time-rescaled probability models may be motivated from biophysical considerations, such as integrate-andfire (IF) or other models of the neuron's spiking mechanism.…”

Section: Non-poisson Data May Be Analyzed Using Probability Modelsmentioning

confidence: 99%

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Statistical Issues in the Analysis of Neuronal Data

Kass¹,

Ventura²,

Brown³

2005

Journal of Neurophysiology

250

239

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Kass, Robert E., Valérie Ventura, and Emery N. Brown. Statistical issues in the analysis of neuronal data. J Neurophysiol 94: 8 -25, 2005; doi:10.1152/jn.00648.2004. Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews wellestablished statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data. I N T R O D U C T I O NTechnical advances have made available new methods for collecting, storing, and manipulating electrophysiological data. Investigations may now not only characterize neuronal activity in anatomically well defined regions, but they can also examine dynamics of neuronal response and their relationship to behavior. Although elementary methods of data analysis [such as t-tests or visual examination of the peristimulus time histogram (PSTH)] remain useful for many purposes, the growing complexity of neuroscientific experiments, often examining subtle changes on a comparatively fine timescale, requires careful attention to statistical methods for data analysis. In this overview we discuss some of the fundamental data analytical issues that face researchers in neurophysiology, illustrating the general points with the problems of describing the evolution of a neuron's firing rate across time, finding accurate population codes, and assessing time-varying correlation between 2 neurons. In each case recent work has provided a statistical technique that outperforms previous methodology, boosting the scientific information as effectively as if the number of experimental trials, or the number of neurons, had been increased by a substantial factor. We also indicate some of the ways modern statistical procedures can accommodate important complexities, such as dynamic changes in temporal and spatial aspects of hippocampal place cell firing and trial-to-trial variability in cortical neurons recorded from behaving animals. Our review supplements the brief and general guidance offered by Curran-Everett and Benos (2004), and may be regarded as an update to the early work of Perkel et al. (1967a,b).The new field of computational neuroscience uses detailed biophysical models and artificial neural networks to study emergent behavior of neural systems and the way neural systems represent and transmit information (e.g...

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“…The fundamental difference between the models is the way the excitability interacts with the recovery function. In the m-IMI model, for example, the refractory period represented in the recovery function is not affected by excitability or firing rate variations, while in the TRRP model the refractory period is no longer fixed but is scaled by the firing rate (Reich et al, 1998;Barbieri et al, 2001). investigated the relationship of m-IMI and TRRP models to the stimulus-driven IF model.…”

Section: Approximating the If Model Via Simpler Point-process Modelsmentioning

confidence: 99%

Statistical Models of Spike Trains

Paninski¹,

Brown²,

Iyengar³

2009

Stochastic Methods in Neuroscience

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Construction and analysis of non-Poisson stimulus-response models of neural spiking activity

Cited by 180 publications

References 28 publications

An analysis of neural receptive field plasticity by point process adaptive filtering

An analysis of neural receptive field plasticity by point process adaptive filtering

Statistical Issues in the Analysis of Neuronal Data

Statistical Models of Spike Trains

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