442 research outputs found
Inference of a mesoscopic population model from population spike trains
To understand how rich dynamics emerge in neural populations, we require models exhibiting a wide range of activity patterns while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single neuron scale to empirical population data. To close this gap, we propose to fit such data at a meso scale, using a mechanistic but low-dimensional and hence statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous `pools' of neurons, and modelling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived, and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent to a mesoscopic population model impact the accuracy of the inferred single-neuron parameters
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Nonlinear dynamics of neural delayed feedback
Neural delayed feedback is a property shared by many circuits in the central and peripheral nervous systems. The evolution of the neural activity in these circuits depends on their present state as well as on their past states, due to finite propagation time of neural activity along the feedback loop. These systems are often seen to undergo a change from a quiescent state characterized by low level fluctuations to an oscillatory state. We discuss the problem of analyzing this transition using techniques from nonlinear dynamics and stochastic processes. Our main goal is to characterize the nonlinearities which enable autonomous oscillations to occur and to uncover the properties of the noise sources these circuits interact with. The concepts are illustrated on the human pupil light reflex (PLR) which has been studied both theoretically and experimentally using this approach. 5 refs., 3 figs
Heidegger and the Poetics of Time
Heidegger’s engagement with the poet Friedrich Hölderlin often dwells on the issue of temporality. For Heidegger, Hölderlin is the most futural thinker (zukünftigster Denker) whose poetry is necessary for us now and must be wrested from being buried in the past. Heidegger frames his reading of Hölderlin in terms of past, present, and future and, more importantly, describes him as being able to poetize time. This paper examines what it means to poetize time and why Hölderlin’s poetry in particular allows us to understand temporality as the interplay of presence and non-presence
Resonance between Noise and Delay
We propose here a stochastic binary element whose transition rate depends on
its state at a fixed interval in the past. With this delayed stochastic
transition this is one of the simplest dynamical models under the influence of
``noise'' and ``delay''. We demonstrate numerically and analytically that we
can observe resonant phenomena between the oscillatory behavior due to noise
and that due to delay.Comment: 4 pages, 5 figures, submitted to Phys.Rev.Lett Expanded and Added
Reference
Stochastic Resonance in Nonpotential Systems
We propose a method to analytically show the possibility for the appearance
of a maximum in the signal-to-noise ratio in nonpotential systems. We apply our
results to the FitzHugh-Nagumo model under a periodic external forcing, showing
that the model exhibits stochastic resonance. The procedure that we follow is
based on the reduction to a one-dimensional dynamics in the adiabatic limit,
and in the topology of the phase space of the systems under study. Its
application to other nonpotential systems is also discussed.Comment: Submitted to Phys. Rev.
A Tool to Recover Scalar Time-Delay Systems from Experimental Time Series
We propose a method that is able to analyze chaotic time series, gained from
exp erimental data. The method allows to identify scalar time-delay systems. If
the dynamics of the system under investigation is governed by a scalar
time-delay differential equation of the form ,
the delay time and the functi on can be recovered. There are no
restrictions to the dimensionality of the chaotic attractor. The method turns
out to be insensitive to noise. We successfully apply the method to various
time series taken from a computer experiment and two different electronic
oscillators
Noise-induced dynamics in bistable systems with delay
Noise-induced dynamics of a prototypical bistable system with delayed
feedback is studied theoretically and numerically. For small noise and
magnitude of the feedback, the problem is reduced to the analysis of the
two-state model with transition rates depending on the earlier state of the
system. In this two-state approximation, we found analytical formulae for the
autocorrelation function, the power spectrum, and the linear response to a
periodic perturbation. They show very good agreement with direct numerical
simulations of the original Langevin equation. The power spectrum has a
pronounced peak at the frequency corresponding to the inverse delay time, whose
amplitude has a maximum at a certain noise level, thus demonstrating coherence
resonance. The linear response to the external periodic force also has maxima
at the frequencies corresponding to the inverse delay time and its harmonics.Comment: 4 pages, 4 figures, submitted to Physical Review Letter
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