Fisher information for spike-based population decoding

We evaluate the Fisher information of a population of model neurons that receive dynamical input and interact via spikes. With spatially independent threshold noise, the spike-based Fisher information that summarizes the information carried by individual spike timings has a particularly simple analytical form. We calculate the loss of information caused by abandoning spike timing and study the effect of synaptic connections on the Fisher information. For a simple spatiotemporal input, we derive the optimal recurrent connectivity that has a local excitation and global inhibition structure. The optimal synaptic connections depend on the spatial or temporal feature of the input that the system is designed to code.     

Respiratory modulation of heart beat-to-beat interval in diabetic patients

We have analyzed simultaneous recordings of respiration and heartbeat intervals in diabetic patients and control subjects. Our main findings are that in diabetic patients the heart beat-to-beat interval variability and cardiorespiratory crosscorrelation are decreased, the autocorrelation time of the interval series is increased, and the phase relation of the respiration with the heartbeat interval oscillations is often reversed in comparison with the control subjects. We have been able to reproduce the data using a biophysical model in which the time dependent input signal to the sinoatrial node was constituted of quasiperiodic and aperiodic components. The quasiperiodic input was obtained from the recording of the respiratory signal and the aperiodic input was obtained from selected realizations of correlated noise. Our study indicates that both input components to the sinoatrial node are modified in diabetic patients.     

Novel class of neural stochastic resonance and error-free information transfer

We investigate a novel class of neural stochastic resonance (SR) exhibiting error-free information transfer. Unlike conventional neural SR, where the decrease of a system's response with too much noise is associated with an increase in the baseline firing rate, here the bell-shaped SR behavior of the input-output cross correlation emerges versus increasing input noise in spite of no significant increase of the baseline firing rate. The neuron thus acts as an error-free detector for weak signals. An integrate-and-fire model with short-term synaptic depression convincingly validates our experimental findings for SR in the human tactile blink reflex.     

Noise-induced synchronization and coherence resonance of a Hodgkin-Huxley model of thermally sensitive neurons

We study nontrivial effects of noise on synchronization and coherence of a chaotic Hodgkin-Huxley model of thermally sensitive neurons. We demonstrate that identical neurons which are not coupled but subjected to a common fluctuating input (Gaussian noise) can achieve complete synchronization when the noise amplitude is larger than a threshold. For nonidentical neurons, noise can induce phase synchronization. Noise enhances synchronization of weakly coupled neurons. We also find that noise enhances the coherence of the spike trains. A saddle point embedded in the chaotic attractor is responsible for these nontrivial noise-induced effects. Relevance of our results to biological information processing is discussed.     

Noise-induced synchronization in bidirectionally coupled type-I neurons

We present here some studies on noise-induced order and synchronous firing in a system of bidirectionally coupled generic type-I neurons. We find that transitions from unsynchronized to completely synchronized states occur beyond a critical value of noise strength that has a clear functional dependence on neuronal coupling strength and input values. For an inhibitory-excitatory (IE) synaptic coupling, the approach to a partially synchronized state is shown to vary qualitatively depending on whether the input is less or more than a critical value. We find that introduction of noise can cause a delay in the bifurcation of the firing pattern of the excitatory neuron for IE coupling.     

Propagation of spiking regularity and double coherence resonance in feedforward networks

We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.     

Inverse stochastic resonance induced by synaptic background activity with unreliable synapses

Inverse stochastic resonance (ISR) is a recently pronounced phenomenon that is the minimum occurrence in mean firing rate of a rhythmically firing neuron as noise level varies. Here, by using a realistic modeling approach for the noise, we investigate the ISR with concrete biophysical mechanisms. It is shown that mean firing rate of a single neuron subjected to synaptic bombardment exhibits a minimum as the spike transmission probability varies. We also demonstrate that the occurrence of ISR strongly depends on the synaptic input regime, where it is most prominent in the balanced state of excitatory and inhibitory inputs.     

Effects of synaptic noise and filtering on the frequency response of spiking neurons

Noise can have a significant impact on the response dynamics of a nonlinear system. For neurons, the primary source of noise comes from background synaptic input activity. If this is approximated as white noise, the amplitude of the modulation of the firing rate in response to an input current oscillating at frequency omega decreases as 1/square root[omega] and lags the input by 45 degrees in phase. However, if filtering due to realistic synaptic dynamics is included, the firing rate is modulated by a finite amount even in the limit omega-->infinity and the phase lag is eliminated. Thus, through its effect on noise inputs, realistic synaptic dynamics can ensure unlagged neuronal responses to high-frequency inputs.     

Transmission of noise coded versus additive signals through a neuronal ensemble

Neuronal populations receive signals through temporally inhomogeneous spike trains which can be approximated by an input consisting of a time dependent mean value (additive signal) and noise with a time dependent intensity (noise coded signal). We compare the linear response of an ensemble of model neurons to these signals. Our analytical solution for the mean activity demonstrates the high efficiency of the transmission of a noise coded signal in a broad frequency band. For both kinds of signal we show that the transmission by the ensemble reveals stochastic resonance as well as a nonmonotonous dependence on the driving frequency.     

On the noise of high-transimpedance amplifiers for long-wavelength pulse OTDRs

Noise measurements on high-transimpedance amplifiers suitable for long-wavelength OTDRs give results higher than is predicted by normal noise models. Consequently, we have developed two useful techniques to measure independently the noise contribution of the JFET and the feedback resistor to the overall amplifier noise. p ]Our results show that the noise of the JFET is in accordance with an accurate theoretical model for such a device. In contrast, the noise from the feedback resistor is much higher than is predicted from the normal resistance-capacitance model for such a component. This increase results from the distributed nature of high-ohmic resistors. Our results indicate that both choice of resistor manufacturer and individual selection of a resistor from a specific manufacturer are warranted. By selecting a low-noise resistor we demonstrate a 500-M transimpedance amplifier with an input equivalent noise current of 13.8 pA. In comparison, the same amplifier with a noisy resistor had an input equivalent noise current of 23 pA. p ]We use our results to show that a reasonable value of the input equivalent noise current of a low-noise photodiode-amplifier combination is 20 pA.     

Neurodynamical amplification of perceptual signals via system-size resonance

We study the effect of finite-size synaptic noise on the response to time-varying stimuli of a detailed neurodynamical model of perceptual-decision making. Specifically, the reaction to a periodic stimulus is analyzed, while controlling the noise level through the number of neurons in the perception module. We find an enhanced response to the dynamical stimulus for intermediate noise, i.e. for an optimal system size. We thus propose a possible functional role of system-size resonance in a complex cognitive task.     

Multiple filamentation induced by input-beam ellipticity

We provide what is to our knowledge the first experimental evidence that multiple filamentation (MF) of ultra-short pulses can be induced by input beam ellipticity. Unlike noise-induced MF, which results in complete beam breakup, the MF pattern induced by small input beam ellipticity appears as a result of nucleation of annular rings surrounding the central filament. Moreover, our experiments show that input beam ellipticity can dominate the effect of noise (transverse modulational instability), giving rise to predictable and highly reproducible MF patterns. The results are explained with a theoretical model and simulations.     

Noise reduction efforts for the ALS infrared beamlines

The quality of infrared microscopy and spectroscopy data collected at synchrotron based sources is strongly dependent on signal-to-noise. We have successfully identified and suppressed several noise sources affecting beamlines 1.4.2, 1.4.3, and 1.4.4 at the advanced light source (ALS), resulting in a significant increase in the quality of FTIR spectra obtained. In this paper, we present our methods of noise source analysis, the negative effect of noise on the infrared beam quality, and the techniques used to reduce the noise. These include reducing the phase noise in the storage ring radio-frequency (RF) system, installing an active mirror feedback system, analyzing and changing physical mounts to better isolate portions of the beamline optics from low-frequency environmental noise, and modifying the input signals to the main ALS RF system. We also discuss the relationship between electron beam energy oscillations at a point of dispersion and infrared beamline noise.     

Optimal control of neuronal activity

We investigate the optimal control of neuronal spiking activity for neurons receiving a class of random synaptic inputs, characterized by a positive parameter alpha. Optimal control signals and optimal variances are found exactly for the diffusion process approximating an integrate and fire model. When synaptic inputs are "sub-Poisson" (alpha<0.5), we find that the optimal synaptic input is a delta function (corresponding to bang-bang control) and the optimal signal is not unique. Poisson synaptic input is the critical case: The control signal is unique, but the control signal is still a delta function. For "supra-Poisson" (alpha>0.5) inputs, the optimal control is smooth and unique. The optimal variance obtained in the current paper sets the lowest possible bound in controlling the stochasticity of neuronal activity. We also discuss how to implement the optimal control signal for certain model neurons.     

Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network

Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feedforward network composed of Hodgkin--Huxley (HH) neurons with sparse connectivity. When white noise is afferent to the input layer, neuronal firing becomes progressively more synchronous in successive layers and synchrony is well developed in deeper layers owing to the feedforward connections between neighboring layers. The synchrony ensures the successful propagation of rate signals through the network when the synaptic conductance is weak. As the synaptic time constant τ_syn varies, coherence resonance is observed in the network activity due to the intrinsic property of HH neurons. This makes the output firing rate single-peaked as a function of τ_syn, suggesting that the signal propagation can be modulated by the synaptic time constant. These results are consistent with experimental results and advance our understanding of how information is processed in feedforward networks.     

Spontaneous dynamics and response properties of a Hodgkin-Huxley-type neuron model driven by harmonic synaptic noise

We study statistical properties, response dynamics, and information transmission in a Hodgkin-Huxley–type neuron system, modeling peripheral electroreceptors in paddlefish. In addition to sodium and potassium currents, the neuron model includes fast calcium and slow afterhyperpolarization (AHP) potassium currents. The synaptic transmission from sensory epithelium is modeled by a Poission process with a rate modulated by narrow-band noise, mimicking stochastic epithelial oscillations observed experimentally. We study how the interplay of parameters of AHP current and synaptic noise affects the statistics of spontaneous dynamics and response properties of the system. In particular, we confirm predictions made earlier with perfect integrate and fire and phase neuron models that epithelial oscillations enhance stimulus–response coherence and thus information transmission in electroreceptor system. In addition, we consider a strong stimulus regime and show that coherent epithelial oscillations may reduce variability of electroreceptor responses to time-varying stimuli.     

Marked signal improvement by stochastic resonance for aperiodic signals in the double-well system

On the basis of our mixed-signal simulations we report significant stochastic resonance induced input-output signal improvement in the double-well system for aperiodic input types. We used a pulse train with randomised pulse locations and a band-limited noise with low cut-off frequency as input signals, and applied a cross-spectral measure to quantify their noise content. We also supplemented our examinations with simulations in the Schmitt trigger to show that the signal improvement we obtained is not a result of a potential filtering effect due to the limited response time of the double-well dynamics.     

非高斯噪声激励下含周期信号FitzHugh-Nagumo系统的响应特征

研究了非高斯噪声激励下含周期信号的FHN模型的动力学行为. 通过计算神经元的平均响应时间、观察神经元的共振活化和噪声增强稳定现象,分析了非高斯噪声对神经元动力学行为的影响. 发现通过改变非高斯噪声的相关时间可以有效地改变共振活化和噪声增强稳定现象. 观察到在强相关噪声下不同强度的非高斯噪声抑制了神经元的噪声增强稳定现象而共振活化现象几乎不变,也就是非高斯噪声有效地增强了神经响应的效率. 观察了平均响应时间与非高斯噪声参数q之间的关系,当q为一个有限的小于1的值时,平均响应时间取得最小值. 最后表明在一定条件下,非高斯噪声出现重尺度现象,即非高斯噪声产生的效果可以由高斯白噪声来估计. 关键词： FHN神经系统 非高斯噪声 平均响应时间 共振活化现象     

Random networks of spiking neurons: instability in the Xenopus tadpole moto-neural pattern

A large network of integrate-and-fire neurons is studied analytically when the synaptic weights are independently randomly distributed according to a Gaussian distribution with arbitrary mean and variance. The relevant order parameters are identified, and it is shown that such network is statistically equivalent to an ensemble of independent integrate-and-fire neurons with each input signal given by the sum of a self-interaction deterministic term and a Gaussian colored noise. The model is able to reproduce the quasisynchronous oscillations, and the dropout of their frequency, of the central nervous system neurons of the swimming Xenopus tadpole. Predictions from the model are proposed for future experiments.     

Cross-correlation of heartbeat and respiration rhythms

The cross-correlation function between respiration and heart beat interval series shows that during metronomized breathing¹ the heart beat follows the respiration more closely than during spontaneous breathing. We reproduced the heart beat interval modulations during metronomized breathing using a biophysical model of the sinoatrial node excited by an input signal formed by the recorded respiration. In the case of spontaneous breathing, a good agreement with the experimental data was obtained only by using an input signal formed by the sum of the recorded respiration and a realization of correlated noise.