9 research outputs found
First Passage Time Densities in Non-Markovian Models with Subthreshold Oscillations
Motivated by the dynamics of resonant neurons we consider a differentiable,
non-Markovian random process and particularly the time after which it
will reach a certain level . The probability density of this first passage
time is expressed as infinite series of integrals over joint probability
densities of and its velocity . Approximating higher order terms
of this series through the lower order ones leads to closed expressions in the
cases of vanishing and moderate correlations between subsequent crossings of
. For a linear oscillator driven by white or coloured Gaussian noise,
which models a resonant neuron, we show that these approximations reproduce the
complex structures of the first passage time densities characteristic for the
underdamped dynamics, where Markovian approximations (giving monotonous first
passage time distribution) fail
First Passage Time Densities in Resonate-and-Fire Models
Motivated by the dynamics of resonant neurons we discuss the properties of
the first passage time (FPT) densities for nonmarkovian differentiable random
processes. We start from an exact expression for the FPT density in terms of an
infinite series of integrals over joint densities of level crossings, and
consider different approximations based on truncation or on approximate
summation of this series. Thus, the first few terms of the series give good
approximations for the FPT density on short times. For rapidly decaying
correlations the decoupling approximations perform well in the whole time
domain.
As an example we consider resonate-and-fire neurons representing stochastic
underdamped or moderately damped harmonic oscillators driven by white Gaussian
or by Ornstein-Uhlenbeck noise. We show, that approximations reproduce all
qualitatively different structures of the FPT densities: from monomodal to
multimodal densities with decaying peaks. The approximations work for the
systems of whatever dimension and are especially effective for the processes
with narrow spectral density, exactly when markovian approximations fail.Comment: 11 pages, 8 figure
Spectra and waiting-time densities in firing resonant and nonresonant neurons
The response of a neural cell to an external stimulus can follow one of the
two patterns: Nonresonant neurons monotonously relax to the resting state after
excitation while resonant ones show subthreshold oscillations. We investigate
how do these subthreshold properties of neurons affect their suprathreshold
response. Vice versa we ask: Can we distinguish between both types of neuronal
dynamics using suprathreshold spike trains? The dynamics of neurons is given by
stochastic FitzHugh-Nagumo and Morris-Lecar models with either having a focus
or a node as the stable fixpoint. We determine numerically the spectral power
density as well as the interspike interval density in response to a random
(noise-like) signals. We show that the information about the type of dynamics
obtained from power spectra is of limited validity. In contrast, the interspike
interval density gives a very sensitive instrument for the diagnostics of
whether the dynamics has resonant or nonresonant properties. For the latter
value we formulate a fit formula and use it to reconstruct theoretically the
spectral power density, which coincides with the numerically obtained spectra.
We underline that the renewal theory is applicable to analysis of
suprathreshold responses even of resonant neurons.Comment: 7 pages, 8 figure
Evaluation of the Oscillatory Interference Model of Grid Cell Firing through Analysis and Measured Period Variance of Some Biological Oscillators
Models of the hexagonally arrayed spatial activity pattern of grid cell firing in the literature generally fall into two main categories: continuous attractor models or oscillatory interference models. Burak and Fiete (2009, PLoS Comput Biol) recently examined noise in two continuous attractor models, but did not consider oscillatory interference models in detail. Here we analyze an oscillatory interference model to examine the effects of noise on its stability and spatial firing properties. We show analytically that the square of the drift in encoded position due to noise is proportional to time and inversely proportional to the number of oscillators. We also show there is a relatively fixed breakdown point, independent of many parameters of the model, past which noise overwhelms the spatial signal. Based on this result, we show that a pair of oscillators are expected to maintain a stable grid for approximately t = 5µ3/(4πσ)2 seconds where µ is the mean period of an oscillator in seconds and σ2 its variance in seconds2. We apply this criterion to recordings of individual persistent spiking neurons in postsubiculum (dorsal presubiculum) and layers III and V of entorhinal cortex, to subthreshold membrane potential oscillation recordings in layer II stellate cells of medial entorhinal cortex and to values from the literature regarding medial septum theta bursting cells. All oscillators examined have expected stability times far below those seen in experimental recordings of grid cells, suggesting the examined biological oscillators are unfit as a substrate for current implementations of oscillatory interference models. However, oscillatory interference models can tolerate small amounts of noise, suggesting the utility of circuit level effects which might reduce oscillator variability. Further implications for grid cell models are discussed
Interspike interval densities of resonate and fire neurons
The subthreshold dynamics of a neuron can follow one of the two patterns: resonant neurons generate intrinsic subthreshold membrane potential oscillations, whereas in nonresonant neurons these oscillations are not observed. Here, we investigate how these subthreshold behaviors affect the suprathreshold response. Both types of neurons are described by a resonate and fire model, with the stable fixpoint being either a focus or a node. Using analytic expression for a linear oscillator model with threshold and reset, we calculate the multimodal interspike interval densities. We show that a change in model parameters induces qualitative changes in the interspike interval densities
Spectral properties of the fractional Fokker-Planck operator for the Lévy flight in a harmonic potential
Stochastic Hierarchical Systems: Excitable Dynamics
We present a discrete model of stochastic excitability by a low-dimensional set of delayed integral equations governing the probability in the rest state, the excited state, and the refractory state. The process is a random walk with discrete states and nonexponential waiting time distributions, which lead to the incorporation of memory kernels in the integral equations. We extend the equations of a single unit to the system of equations for an ensemble of globally coupled oscillators, derive the mean field equations, and investigate bifurcations of steady states. Conditions of destabilization are found, which imply oscillations of the mean fields in the stochastic ensemble. The relation between the mean field equations and the paradigmatic Kuramoto model is shown