Synchronization of noisy systems by stochastic signals

Synchronization of gene expression across eukaryotic communities through chemical rhythms

The synchronization is a recurring phenomenon in neuroscience, ecology, human sciences, and biology. However, controlling synchronization in complex eukaryotic consortia on extended spatial-temporal scales remains a major challenge. Here, to address this issue we construct a minimal synthetic system that directly converts chemical signals into a coherent gene expression synchronized among eukaryotic communities through rate-dependent hysteresis. Guided by chemical rhythms, isolated colonies of yeast Saccharomyces cerevisiae oscillate in near-perfect synchrony despite the absence of intercellular coupling or intrinsic oscillations. Increased speed of chemical rhythms and incorporation of feedback in the system architecture can tune synchronization and precision of the cell responses in a growing cell collectives. This synchronization mechanism remain robust under stress in the two-strain consortia composed of toxin-sensitive and toxin-producing strains. The sensitive cells can maintain the spatial-temporal synchronization for extended periods under the rhythmic toxin dosages produced by killer cells. Our study provides a simple molecular framework for generating global coordination of eukaryotic gene expression through dynamic environment.

Controlling the phase locking of stochastic magnetic bits for ultra-low power computation

When fabricating magnetic memories, one of the main challenges is to maintain the bit stability while downscaling. Indeed, for magnetic volumes of a few thousand nm(3), the energy barrier between magnetic configurations becomes comparable to the thermal energy at room temperature. Then, switches of the magnetization spontaneously occur. These volatile, superparamagnetic nanomagnets are generally considered useless. But what if we could use them as low power computational building blocks? Remarkably, they can oscillate without the need of any external dc drive, and despite their stochastic nature, they can beat in unison with an external periodic signal. Here we show that the phase locking of superparamagnetic tunnel junctions can be induced and suppressed by electrical noise injection. We develop a comprehensive model giving the conditions for synchronization, and predict that it can be achieved with a total energy cost lower than 10(-13) J. Our results open the path to ultra-low power computation based on the controlled synchronization of oscillators.

Direct connections assist neurons to detect correlation in small amplitude noises

We address a question on the effect of common stochastic inputs on the correlation of the spike trains of two neurons when they are coupled through direct connections. We show that the change in the correlation of small amplitude stochastic inputs can be better detected when the neurons are connected by direct excitatory couplings. Depending on whether intrinsic firing rate of the neurons is identical or slightly different, symmetric or asymmetric connections can increase the sensitivity of the system to the input correlation by changing the mean slope of the correlation transfer function over a given range of input correlation. In either case, there is also an optimum value for synaptic strength which maximizes the sensitivity of the system to the changes in input correlation.

Synchronization of stochastic oscillators in biochemical systems

We investigate the synchronization of stochastic oscillations in biochemical models by calculating the complex coherence function within the linear noise approximation. The method is illustrated on a simple example and then applied to study the synchronization of chemical concentrations in social amoeba. The degree to which variation of rate constants in different cells and the volume of the cells affects synchronization of the oscillations is explored and the phase lag calculated. In all cases the analytical results are shown to be in good agreement with those obtained through numerical simulations.

Patterned Brain Stimulation, What a Framework with Rhythmic and Noisy Components Might Tell Us about Recovery Maximization

Brain stimulation is having remarkable impact on clinical neurology. Brain stimulation can modulate neuronal activity in functionally segregated circumscribed regions of the human brain. Polarity, frequency, and noise specific stimulation can induce specific manipulations on neural activity. In contrast to neocortical stimulation, deep-brain stimulation has become a tool that can dramatically improve the impact clinicians can possibly have on movement disorders. In contrast, neocortical brain stimulation is proving to be remarkably susceptible to intrinsic brain-states. Although evidence is accumulating that brain stimulation can facilitate recovery processes in patients with cerebral stroke, the high variability of results impedes successful clinical implementation. Interestingly, recent data in healthy subjects suggests that brain-state dependent patterned stimulation might help resolve some of the intrinsic variability found in previous studies. In parallel, other studies suggest that noisy “stochastic resonance” (SR)-like processes are a non-negligible component in non-invasive brain stimulation studies. The hypothesis developed in this manuscript is that stimulation patterning with noisy and oscillatory components will help patients recover from stroke related deficits more reliably. To address this hypothesis we focus on two factors common to both neural computation (intrinsic variables) as well as brain stimulation (extrinsic variables): noise and oscillation. We review diverse theoretical and experimental evidence that demonstrates that subject-function specific brain-states are associated with specific oscillatory activity patterns. These states are transient and can be maintained by noisy processes. The resulting control procedures can resemble homeostatic or SR processes. In this context we try to extend awareness for inter-individual differences and the use of individualized stimulation in the recovery maximization of stroke patients.

Temperature-dependent stochastic dynamics of the Huber-Braun neuron model

The response of a four-dimensional mammalian cold receptor model to different implementations of noise is studied across a wide temperature range. It is observed that for noisy activation kinetics, the parameter range decomposes into two regions in which the system reacts qualitatively completely different to small perturbations through noise, and these regions are separated by a homoclinic bifurcation. Noise implemented as an additional current yields a substantially different system response at low temperature values, while the response at high temperatures is comparable to activation-kinetic noise. We elucidate how this phenomenon can be understood in terms of state space dynamics and gives quantitative results on the statistics of interspike interval distributions across the relevant parameter range.

Higher-order statistics of a bistable system driven by dichotomous colored noise

Exact expressions for first- and higher-order residence time statistics, count statistics, and spectral properties of a bistable system driven by dichotomous colored noise are put forward and discussed. The employed method is based on a discrete kinetic scheme and is valid for a wide range of parameter values of the colored noise. This permits a detailed analysis of the effects of noise correlations for arbitrary correlation times. It is found that at a characteristic correlation time of the dichotomous noise, the residence time sequence becomes Poissonian; in particular, all correlations between residence times disappear. We also find that correlations become strongest for a finite strength of the driving force. The analytical results can also be used to infer the underlying driving parameters in the case of noise with long-range temporal correlations.

Array enhancement of stochastic synchronization and signal-to-noise ratio gain in the nonlinear regime of signal transmission

The nonlinear transformation of an external noisy signal by an array of noninteracting elements with internal noise is considered. The array simulates a neuronal system that processes spike trains. It is shown that increasing the number of array elements entails significant extending of the stochastic synchronization region and improvement of the signal-to-noise ratio (SNR). The effects are demonstrated for arrays of triggers, overdamped bistable oscillators, and Fitzhugh-Nagumo systems. The interrelation between SNR improvement and the efficiency of information processing is discussed.

Synchronization of stochastic bistable systems by biperiodic signals

We study the nonlinear response of a noisy bistable system to a biperiodic signal through experiments with an electronic circuit (Schmitt trigger). The signal we use is a biharmonic one, i.e., a superposition of low and high frequency harmonic components. It is shown that the mean switching frequency (MSF) of the system can be locked at both low and high frequencies. Moreover, the phenomenon of MSF locking at the lower frequency can be induced and enhanced by the higher frequency excitation. Thus high frequency bias can control synchronization at the low frequency.

Correlations in the sequence of residence times

Sequences of residence times (RTs) associated with the escape from metastable states are observed in many fields. Here we study analytically and numerically the correlations among RTs for a bistable stochastic system driven by dichotomous noise. Our theory predicts an oscillatory behavior of the correlations with respect to the lag between RTs. Correlations vanish at all lags if the switching rate matches the hopping rate of the unperturbed system. It is also shown that RT correlations may reveal features of the driving which are not present in the single-RT statistics.

Phase velocity and phase diffusion in periodically driven discrete-state systems

We develop a theory to calculate the effective phase diffusion coefficient and the mean phase velocity in periodically driven stochastic models with two discrete states. This theory is applied to a dichotomically driven Markovian two-state system. Explicit expressions for the mean phase velocity, the effective phase diffusion coefficient, and the Pe clet number are analytically calculated. The latter indicates as a measure of phase-coherence forced synchronization of the stochastic system with respect to the periodic driving and exhibits a "bona fide" resonance. In a second step, the theory is applied to a non-Markovian two-state system modeling excitable systems. The results prove again stochastic synchronization to the periodic driving and are in good agreement with simulations of a stochastic FitzHugh-Nagumo system.

State-dependent effects of Na channel noise on neuronal burst generation

We explore the effects of stochastic sodium (Na) channel activation on the variability and dynamics of spiking and bursting in a model neuron. The complete model segregates Hodgin-Huxley-type currents into two compartments, and undergoes applied current-dependent bifurcations between regimes of periodic bursting, chaotic bursting, and tonic spiking. Noise is added to simulate variable, finite sizes of the population of Na channels in the fast spiking compartment. During tonic firing, Na channel noise causes variability in interspike intervals (ISIs). The variance, as well as the sensitivity to noise, depend on the model's biophysical complexity. They are smallest in an isolated spiking compartment; increase significantly upon coupling to a passive compartment; and increase again when the second compartment also includes slow-acting currents. In this full model, sufficient noise can convert tonic firing into bursting. During bursting, the actions of Na channel noise are state-dependent. The higher the noise level, the greater the jitter in spike timing within bursts. The noise makes the burst durations of periodic regimes variable, while decreasing burst length duration and variance in a chaotic regime. Na channel noise blurs the sharp transitions of spike time and burst length seen at the bifurcations of the noise-free model. Close to such a bifurcation, the burst behaviors of previously periodic and chaotic regimes become essentially indistinguishable. We discuss biophysical mechanisms, dynamical interpretations and physiological implications. We suggest that noise associated with finite populations of Na channels could evoke very different effects on the intrinsic variability of spiking and bursting discharges, depending on a biological neuron's complexity and applied current-dependent state. We find that simulated channel noise in the model neuron qualitatively replicates the observed variability in burst length and interburst interval in an isolated biological bursting neuron.

Stochastic resonance and synchronization in the crayfish caudal photoreceptor

Stochastic resonance is the process by which noise added to a weak external stimulus can enhance encoding efficiency in the sensory periphery and thence in the central nervous system. Stochastic synchronization is the process by which noisy phase synchronization of two periodic (or aperiodic) signals can occur. Together with a brief review of both concepts, we illustrate their applications to the encoding of weak external hydrodynamic signals in the mechanosensory system of the crayfish.

Is there chaos in the brain? II. Experimental evidence and related models

The search for chaotic patterns has occupied numerous investigators in neuroscience, as in many other fields of science. Their results and main conclusions are reviewed in the light of the most recent criteria that need to be satisfied since the first descriptions of the surrogate strategy. The methods used in each of these studies have almost invariably combined the analysis of experimental data with simulations using formal models, often based on modified Huxley and Hodgkin equations and/or of the Hindmarsh and Rose models of bursting neurons. Due to technical limitations, the results of these simulations have prevailed over experimental ones in studies on the nonlinear properties of large cortical networks and higher brain functions. Yet, and although a convincing proof of chaos (as defined mathematically) has only been obtained at the level of axons, of single and coupled cells, convergent results can be interpreted as compatible with the notion that signals in the brain are distributed according to chaotic patterns at all levels of its various forms of hierarchy. This chronological account of the main landmarks of nonlinear neurosciences follows an earlier publication [Faure, Korn, C. R. Acad. Sci. Paris, Ser. III 324 (2001) 773-793] that was focused on the basic concepts of nonlinear dynamics and methods of investigations which allow chaotic processes to be distinguished from stochastic ones and on the rationale for envisioning their control using external perturbations. Here we present the data and main arguments that support the existence of chaos at all levels from the simplest to the most complex forms of organization of the nervous system. We first provide a short mathematical description of the models of excitable cells and of the different modes of firing of bursting neurons (Section 1). The deterministic behavior reported in giant axons (principally squid), in pacemaker cells, in isolated or in paired neurons of Invertebrates acting as coupled oscillators is then described (Section 2). We also consider chaotic processes exhibited by coupled Vertebrate neurons and of several components of Central Pattern Generators (Section 3). It is then shown that as indicated by studies of synaptic noise, deterministic patterns of firing in presynaptic interneurons are reliably transmitted, to their postsynaptic targets, via probabilistic synapses (Section 4). This raises the more general issue of chaos as a possible neuronal code and of the emerging concept of stochastic resonance Considerations on cortical dynamics and of EEGs are divided in two parts. The first concerns the early attempts by several pioneer authors to demonstrate chaos in experimental material such as the olfactory system or in human recordings during various forms of epilepsies, and the belief in 'dynamical diseases' (Section 5). The second part explores the more recent period during which surrogate-testing, definition of unstable periodic orbits and period-doubling bifurcations have been used to establish more firmly the nonlinear features of retinal and cortical activities and to define predictors of epileptic seizures (Section 6). Finally studies of multidimensional systems have founded radical hypothesis on the role of neuronal attractors in information processing, perception and memory and two elaborate models of the internal states of the brain (i.e. 'winnerless competition' and 'chaotic itinerancy'). Their modifications during cognitive functions are given special attention due to their functional and adaptive capabilities (Section 7) and despite the difficulties that still exist in the practical use of topological profiles in a state space to identify the physical underlying correlates. The reality of 'neurochaos' and its relations with information theory are discussed in the conclusion (Section 8) where are also emphasized the similarities between the theory of chaos and that of dynamical systems. Both theories strongly challenge computationalism and suggest that new models are needed to describe how the external world is represented in the brain.

Behavioral stochastic resonance within the human brain

We provide the first evidence that stochastic resonance within the human brain can enhance behavioral responses to weak sensory inputs. We asked subjects to adjust handgrip force to a slowly changing, subthreshold gray level signal presented to their right eye. Behavioral responses were optimized by presenting randomly changing gray levels separately to the left eye. The results indicate that observed behavioral stochastic resonance was mediated by neural activity within the human brain where the information from both eyes converges.

Frequency and phase locking of noise-sustained oscillations in coupled excitable systems: array-enhanced resonances

We study the interplay among noise, weak driving signal and coupling in excitable FitzHugh-Nagumo neurons. Due to coupling, noise-sustained oscillations become locked to the signal as functions of both signal frequency and noise intensity. Higher order m:n locking tongues and various array-enhanced resonance features are demonstrated. This resonance and locking behavior due to a time scale matching between noise-sustained oscillations and the signal is fundamentally different from stochastic resonance in usual noisy threshold elements.

Frequency and phase synchronization in stochastic systems

The phenomenon of frequency and phase synchronization in stochastic systems requires a revision of concepts originally phrased in the context of purely deterministic systems. Various definitions of an instantaneous phase are presented and compared with each other with special attention paid to their robustness with respect to noise. We review the results of an analytic approach describing noise-induced phase synchronization in a thermal two-state system. In this context exact expressions for the mean frequency and the phase diffusivity are obtained that together determine the average length of locking episodes. A recently proposed method to quantify frequency synchronization in noisy potential systems is presented and exemplified by applying it to the periodically driven noisy harmonic oscillator. Since this method is based on a threshold crossing rate pioneered by Rice the related phase velocity is termed the Rice frequency. Finally, we discuss the relation between the phenomenon of stochastic resonance and noise-enhanced phase coherence by applying the developed concepts to the periodically driven bistable Kramers oscillator.

Stochastic phase synchronization in the crayfish mechanoreceptor/photoreceptor system

The two light-sensitive neurons in the crayfish's abdominal sixth ganglion ("caudal photoreceptors," or CPRs), are both primary light sensors and secondary neurons in a mechanosensory pathway. Pei et al. (1996) demonstrated that light enhances the transduction of weak, periodic hydrodynamic stimuli (measured as an increase in the signal-to-noise ratio at the stimulus frequency in the power spectrum of the recorded neural spikes). This has been interpreted as a stochastic resonance effect, in which added light increases the noise intensity of the input to the photoreceptor (possibly through fluctuations in membrane potential), leading to an enhancement of the signal-to-noise ratio (SNR). Here, we discuss the recent demonstration (Bahar et al., 2002) of the correlation between a stochastic-resonance-like effect and an increase in stochastic phase synchronization between the neural response and a periodic mechanical stimulus. We also discuss a novel effect (Bahar et al., 2002) in which light increases the SNR of the second higher harmonic of a periodic input signal, effectively rectifying the input signal. This "second harmonic effect" can also be interpreted in terms of stochastic phase synchronization (Bahar et al., 2002). We review other recent results on the role of stochastic phase synchronization in mediating sensory responses in the crayfish nervous system.

The community of the self

Good health, which reflects the harmonious integration of molecules, cells, tissues and organs, is dynamically stable: when displaced by disease, compensation and correction are common, even without medical care. Physiology and computational biology now suggest that healthy dynamic stability arises through the combination of specific feedback mechanisms and spontaneous properties of interconnected networks. Today's physicians are already testing to 'see if the network is right'; tomorrow's physicians may well use therapies to 'make the network right'.

Phase synchronization and stochastic resonance effects in the crayfish caudal photoreceptor

We study the nonlinear response of the crayfish caudal photoreceptor to periodic mechanical stimuli in terms of stochastic synchronization. The amplitude and frequency of the mechanical stimuli and the light level are used as control parameters. The system shows multiple locking regions as the stimulus frequency is varied. We find that the synchronization index increases as the signal-to-noise ratio (SNR) of the periodic drive, in response to increasing light levels; this effect exhibits features similar to stochastic resonance. We demonstrate a nonlinear rectification effect in which the SNR of the second harmonic of the input stimulus increases as the light level is raised, and show that the corresponding synchronization index increases as the SNR of the second harmonic.

Oscillatory systems driven by noise: frequency and phase synchronization

The phenomenon of effective phase synchronization in stochastic oscillatory systems can be quantified by an average frequency and a phase diffusion coefficient. A different approach to compute the noise-averaged frequency is put forward. The method is based on a threshold crossing rate pioneered by Rice. After the introduction of the Rice frequency for noisy systems we compare this quantifier with those obtained in the context of other phase concepts, such as the natural and the Hilbert phase, respectively. It is demonstrated that the average Rice frequency <omega>R typically supersedes the Hilbert frequency <omega>H, i.e. <omega>R > or = <omega>H. We investigate next the Rice frequency for the harmonic and the damped, bistable Kramers oscillator, both without and with external periodic driving. Exact and approximative analytic results are corroborated by numerical simulation results. Our results complement and extend previous findings for the case of noise-driven inertial systems.

Noise-induced phase synchronization enhanced by dichotomic noise

We study the nonlinear response of a stochastic bistable system driven by both a weak periodic signal and a dichotomic noise in terms of stochastic phase synchronization. We show that the effect of noise-induced phase synchronization can be significantly enhanced by the addition of a dichotomic noise.

Synchronization of hyperexcitable systems with phase-repulsive coupling

We study two-dimensional arrays of FitzHugh-Nagumo elements with nearest-neighbor coupling from the viewpoint of synchronization. The elements are diffusively coupled. By varying the diffusion coefficient from positive to negative values, interesting synchronization patterns are observed. The results of the simulations resemble the intracellular oscillation patterns observed in cultured human epileptic astrocytes. Three measures are proposed to determine the degree of synchronization (or coupling) in both the simulated and the experimental system.

Do neocortical pyramidal neurons display stochastic resonance?

Neocortical pyramidal neurons in vivo are subject to an intense synaptic background activity that has a significant impact on various electrophysiological properties and dendritic integration. Using detailed biophysical models of a morphologically reconstructed neocortical pyramidal neuron, in which synaptic background activity was simulated according to recent measurements in cat parietal cortex in vivo, we show that the responsiveness of the cell to additional periodic subthreshold stimuli can be significantly enhanced through mechanisms similar to stochastic resonance. We compare several paradigms leading to stochastic resonance-like behavior, such as varying the strength or the correlation in the background activity. A new type of resonance-like behavior was obtained when the correlation was varied, in which case the responsiveness is sensitive to the statistics rather than the strength of the noise. We suggest that this type of resonance may be relevant to information processing in the cerebral cortex.

Influence of low intensity noise on assemblies of diffusively coupled chaotic cells

The effect of time-correlated and white Gaussian noises of low intensity on one-dimensional arrays consisting of diffusively coupled chaotic cells is analyzed. An improvement or worsening of the synchronization between cells of the array driven by low-intensity colored noise is observed for a resonant interval of time correlation values. A comparison between colored and white noise and additive and multiplicative contribution has been carried out investigating the nonlinear cooperative effects of noise strength, correlation time, and coupling strength to control spatiotemporal chaos in coupled arrays of chaotic cells. The possibility to distinguish highly correlated areas of a diffusively coupled network of cells by using low-intensity time correlated noise is discussed. (c) 2001 American Institute of Physics.

Noise-assisted transmission of binary information: theory and experiment

We study the response of a bistable vertical cavity surface emitting laser to an aperiodic binary signal, by adding a variable amount of noise. The resulting behavior is an example of aperiodic stochastic resonance, and in this work we give a detailed comparison between analytical and numerical results and accurate experimental measurements. We characterize the phenomenon by using different appropriate indicators, which also allow us to quantify the binary information transmission. We show that the quality of the transmission is enhanced by a suitable amount of noise, and we give a physical picture of the phenomenon.

Correlation detection and resonance in neural systems with distributed noise sources

We investigated the resonance behavior in model neurons receiving a large number of random synaptic inputs, whose distributed nature permits one to introduce correlations between them and investigate its effect on cellular responsiveness. A change in the strength of this background led to enhanced responsiveness, consistent with stochastic resonance. Altering the correlation revealed a type of resonance behavior in which the neuron is sensitive to statistical properties rather than the strength of the noise. Remarkably, the neuron could detect weak correlations among the distributed inputs within millisecond time scales.

Multifractal characterization of stochastic resonance

We use a multifractal formalism to study the effect of stochastic resonance in a noisy bistable system driven by various input signals. To characterize the response of a stochastic bistable system we introduce a new measure based on the calculation of a singularity spectrum for a return time sequence. We use wavelet transform modulus maxima method for the singularity spectrum computations. It is shown that the degree of multifractality defined as a width of singularity spectrum can be successfully used as a measure of complexity both in the case of periodic and aperiodic (stochastic or chaotic) input signals. We show that in the case of periodic driving force, singularity spectrum can change its structure qualitatively becoming monofractal in the regime of stochastic synchronization. This fact allows us to consider the degree of multifractality as a new measure of stochastic synchronization also. Moreover, our calculations have shown that the effect of stochastic resonance can be catched by this measure even from a very short return time sequence. We use also the proposed approach to characterize the noise-enhanced dynamics of a coupled stochastic neurons model.

Behavioral stochastic resonance: how a noisy army betrays its outpost

Juvenile paddlefish prey upon single zooplankton by detecting a weak electric signature resulting from its feeding and swimming motions. Moreover, it has recently been shown that paddlefish make use of stochastic resonance near the threshold for prey detection: a process termed behavioral stochastic resonance. But this process depends upon an external source of electric noise. A swarm of plankton, for example, Daphnia, can provide this noise. Assuming that juvenile paddlefish attack single Daphnia as outliers in the vicinity of the swarm, making use of noise from the swarm, we calculate the spatial distribution of the average phase locking period for the subthreshold signals acting at the paddlefish rostrum. Numeric evaluation of analytic formulas supports the notion of a noise-induced widening of the capture area quantitatively.

Stochastic resonance enhanced by dichotomic noise in a bistable system

We study linear responses of a stochastic bistable system driven by dichotomic noise to a weak periodic signal. We show that the effect of stochastic resonance can be greatly enhanced in comparison with the conventional case when dichotomic forcing is absent, that is, both the signal-to-noise ratio and the spectral power amplification reach much greater values than in the standard stochastic resonance setup.

A unifying definition of synchronization for dynamical systems

We propose a unifying definition for synchronization between stationary finite dimensional deterministic dynamical systems. By example, we show that the synchronization phenomena discussed in the dynamical systems literature fits within the framework of this definition, and discuss problems with previous definitions of synchronization. We conclude with a discussion of possible extensions of the definition to infinite dimensional systems described by partial differential equations and/or systems where noise is present. (c) 2000 American Institute of Physics.

Coherence resonance and noise-induced synchronization in globally coupled Hodgkin-Huxley neurons

The coherence resonance (CR) of globally coupled Hodgkin-Huxley neurons is studied. When the neurons are set in the subthreshold regime near the firing threshold, the additive noise induces limit cycles. The coherence of the system is optimized by the noise. The coupling of the network can enhance CR in two different ways. In particular, when the coupling is strong enough, the synchronization of the system is induced and optimized by the noise. This synchronization leads to a high and wide plateau in the local CR curve. A bell-shaped curve is found for the peak height of power spectra of the spike train, being significantly different from a monotonic behavior for the single neuron. The local-noise-induced limit cycle can evolve to a refined spatiotemporal order through the dynamical optimization among the autonomous oscillation of an individual neuron, the coupling of the network, and the local noise.

Analytical approach to the stochastic FitzHugh-Nagumo system and coherence resonance

We consider the FitzHugh-Nagumo system under the influence of white Gaussian noise in the excitable regime. We present an analytical approximation in the limit of fast activator time scale. Marginal probability densities of a reduced system and dynamical quantities such as the pulse rate are found and the mean interspike interval and its relative standard deviation are investigated. The latter quantities allow a quantitative description of the phenomenon of coherence resonance, as comparisons with simulations show.