34 research outputs found
Sensitivity to the cutoff value in the quadratic adaptive integrate-and-fire model
The quadratic adaptive integrate-and-fire model (Izhikecih 2003, 2007) is
recognized as very interesting for its computational efficiency and its ability
to reproduce many behaviors observed in cortical neurons. For this reason it is
currently widely used, in particular for large scale simulations of neural
networks. This model emulates the dynamics of the membrane potential of a
neuron together with an adaptation variable. The subthreshold dynamics is
governed by a two-parameter differential equation, and a spike is emitted when
the membrane potential variable reaches a given cutoff value. Subsequently the
membrane potential is reset, and the adaptation variable is added a fixed value
called the spike-triggered adaptation parameter. We show in this note that when
the system does not converge to an equilibrium point, both variables of the
subthreshold dynamical system blow up in finite time whatever the parameters of
the dynamics. The cutoff is therefore essential for the model to be well
defined and simulated. The divergence of the adaptation variable makes the
system very sensitive to the cutoff: changing this parameter dramatically
changes the spike patterns produced. Furthermore from a computational
viewpoint, the fact that the adaptation variable blows up and the very sharp
slope it has when the spike is emitted implies that the time step of the
numerical simulation needs to be very small (or adaptive) in order to catch an
accurate value of the adaptation at the time of the spike. It is not the case
for the similar quartic (Touboul 2008) and exponential (Brette and Gerstner
2005) models whose adaptation variable does not blow up in finite time, and
which are therefore very robust to changes in the cutoff value
Beyond blow-up in excitatory integrate and fire neuronal networks: refractory period and spontaneous activity
International audienceThe Network Noisy Leaky Integrate and Fire equation is among the simplest model allowing for a self-consistent description of neural networks and gives a rule to determine the probability to find a neuron at the potential . However, its mathematical structure is still poorly understood and, concerning its solutions, very few results are available. In the midst of them, a recent result shows blow-up in finite time for fully excitatory networks. The intuitive explanation is that each firing neuron induces a discharge of the others; thus increases the activity and consequently the discharge rate of the full network. In order to better understand the details of the phenomena and show that the equation is more complex and fruitful than expected, we analyze further the model. We extend the finite time bow-up result to the case when neurons, after firing, enter a refractory state for a given period of time. We also show that spontaneous activity may occur when, additionally, randomness is included on the firing potential in regimes where blow-up occurs for a fixed value of
One-Dimensional Population Density Approaches to Recurrently Coupled Networks of Neurons with Noise
Mean-field systems have been previously derived for networks of coupled,
two-dimensional, integrate-and-fire neurons such as the Izhikevich, adapting
exponential (AdEx) and quartic integrate and fire (QIF), among others.
Unfortunately, the mean-field systems have a degree of frequency error and the
networks analyzed often do not include noise when there is adaptation. Here, we
derive a one-dimensional partial differential equation (PDE) approximation for
the marginal voltage density under a first order moment closure for coupled
networks of integrate-and-fire neurons with white noise inputs. The PDE has
substantially less frequency error than the mean-field system, and provides a
great deal more information, at the cost of analytical tractability. The
convergence properties of the mean-field system in the low noise limit are
elucidated. A novel method for the analysis of the stability of the
asynchronous tonic firing solution is also presented and implemented. Unlike
previous attempts at stability analysis with these network types, information
about the marginal densities of the adaptation variables is used. This method
can in principle be applied to other systems with nonlinear partial
differential equations.Comment: 26 Pages, 6 Figure
Dynamics and bifurcations of the adaptive exponential integrate-and-fire model
Recently, several two-dimensional spiking neuron models have been introduced, with the aim of reproducing the diversity of electrophysiological features displayed by real neurons while keeping a simple model, for simulation and analysis purposes. Among these models, the adaptive integrate-and-fire model is physiologically relevant in that its parameters can be easily related to physiological quantities. The interaction of the differential equations with the reset results in a rich and complex dynamical structure. We relate the subthreshold features of the model to the dynamical properties of the differential system and the spike patterns to the properties of a Poincaré map defined by the sequence of spikes. We find a complex bifurcation structure which has a direct interpretation in terms of spike trains. For some parameter values, spike patterns are chaotic
A Markovian event-based framework for stochastic spiking neural networks
In spiking neural networks, the information is conveyed by the spike times,
that depend on the intrinsic dynamics of each neuron, the input they receive
and on the connections between neurons. In this article we study the Markovian
nature of the sequence of spike times in stochastic neural networks, and in
particular the ability to deduce from a spike train the next spike time, and
therefore produce a description of the network activity only based on the spike
times regardless of the membrane potential process.
To study this question in a rigorous manner, we introduce and study an
event-based description of networks of noisy integrate-and-fire neurons, i.e.
that is based on the computation of the spike times. We show that the firing
times of the neurons in the networks constitute a Markov chain, whose
transition probability is related to the probability distribution of the
interspike interval of the neurons in the network. In the cases where the
Markovian model can be developed, the transition probability is explicitly
derived in such classical cases of neural networks as the linear
integrate-and-fire neuron models with excitatory and inhibitory interactions,
for different types of synapses, possibly featuring noisy synaptic integration,
transmission delays and absolute and relative refractory period. This covers
most of the cases that have been investigated in the event-based description of
spiking deterministic neural networks