Improved integrate-and-fire model for rsa.

A simple operational model of heart rate variability is described, accounting in particular for the respiratory sinus arrhythmia, and is fitted to some interbeat interval sequences recorded from normal subjects at rest. The model performance is evaluated using a test based on the nonlinear prediction approach. Moreover, a short comparative account of two similar models described in the literature is given.

The synchronization properties of a network of inhibitory interneurons depend on the biophysical model.

The synchronization properties of a pair of coupled fast spiking interneurons are studied by using the theory of weakly coupled oscillators. Four different biophysical models of the single fast spiking interneuron are used and the corresponding results are compared. It is shown that for a pair of identical coupled cells, the synchronization properties are model-dependent. In particular, the firing coherence of the network is strongly affected by the reversal potential, the kinetics of the inhibitory postsynaptic current and the electrical coupling; the activation properties of the sodium and potassium currents play a significant role too.

Calcium signalling in astrocytes and modulation of neural activity.

Starting from the experimental data on ATP evoked calcium responses in astrocytes, a biophysical model describing these phenomena was built. The simulations showed, in agreement with the experimental findings, that the intracellular calcium fluxes mediated by the P2X and P2Y purinoreceptors are responsible for the biphasic ATP evoked calcium response in astrocytes. Then, the modulation effects on the neural dynamics arising from the release of glutamate from astrocyte are also investigated. By using a minimal network model describing a neuron coupled to the astrocyte, we demonstrated that the calcium extrusion rate through the astrocyte membrane is critically involved in the generation of different firing patterns of the neuron.

Stochastic resonance in two simple compare-and-fire models.

Two neural models are analysed and shown to exhibit the stochastic resonance effect. Namely, they respond to an underthreshold sinusoidal signal with an output signal whose signal-to-noise ratio (SNR) firstly increases then decreases as the intensity of noise affecting the system increases. The resonance curves are determined, analytically for the first and simplest model and by a synthetic method for the second one, and the respective resonant behaviours are illustrated and interpreted.

Signal processing properties of fast spiking interneurons.

Fast spiking interneurons receive excitatory synaptic inputs from pyramidal cells and a relevant problem is to understand how these cells readout this information. Here this topic is investigated theoretically by using a biophysical modeling of a pair of coupled fast spiking interneuron models. The model predicts, in agreement with the experimental findings, that these cells are capable of transmitting pre-synaptic signals with high temporal precision and transferring high frequency inputs while preserving their relative timing. Moreover, it is shown that a pair of fast spiking interneurons, coupled through both inhibitory and electrical synapses, behaves as a coincidence detector. Lastly, to understand the mechanisms underlying these phenomena, a theoretical analysis is carried out by using a simpler modeling approach.

Stochastic resonance in the LIF models with input or threshold noise.

In this paper, two stochastic versions of the LIF neural model are considered: one with the noise signal applied to the firing threshold, the other having it added to the input current. Then, adopting a discontinuous stepwise noise whose innovations are uncorrelated and gaussian distributed, the behaviours of the two models pertaining to the stochastic resonance (SR) are analysed and compared. Furthermore, it is shown that introducing a suitable time correlation into the noise signal brings us from the first model to the second one.

Stochastic resonance in a sinusoidally forced LIF model with noisy threshold.

In this report, the LIF neural model driven by underthreshold sinusoidal signals but with a gaussian-distributed noise on the threshold, is approximated by suitably defining an instantaneous firing (or escape) rate, which depends only on the momentary value of the voltage variable. This allows us to obtain, by analytically solving the relevant equations, the main statistical functions describing the "firing activity"; namely, the probability density function of firing phases and that of interspike intervals. From these functions two quantities can be derived, whose dependence on the noise intensity allows the Stochastic Resonance (SR) to be demonstrated. Besides the "regular" SR, the analysed system was found to produce, either for low frequencies and large amplitudes of modulation or for high modulation frequencies, resonance curves displaying two peaks. This bimodal feature of the resonance curves is accounted for on the basis of phase locked firing patterns.

Synchronization in a network of fast-spiking interneurons.

Experimental results revealed that in neocortex inhibitory fast-spiking (FS) interneurons interact also by electrical synapses (gap-junctions). They receive sensory information from thalamus and transfer it to principal cells by feedforward inhibition. Moreover, their synchronous discharge enhances their inhibitory control of pyramidal neurons. By using a biophysical model of FS interneurons the synchronization properties of a network of two synaptically coupled units are investigated. In the case they interact only by inhibitory synapses, well defined regions exist in the parameters space described by the strength and duration of the synaptic current, where synchronous regimes occur. Then an empirical protocol is proposed to determine approximately the borders of the synchronization manifold (SM). When electrical synapses are included, the region of synchronous discharge of the two interneurons becomes larger. In both cases, the coherent states are characterized by discharge frequencies in the gamma range. Lastly, the effects of heterogeneity, either obtained by using different stimulation currents or unidirectional inhibitory coupling, are studied.