Ghost resonance in a pool of heterogeneous neurons

We numerically study the subharmonic response of a heterogeneous pool of neurons to a pair of independent inputs. The neurons are stimulated with periodic pulse trains of frequencies f(1)=2 Hz and f(2)=3 Hz, and with inharmonic pulses whose frequencies f(1) and f(2) are equally shifted an amount Delta f. When both inputs are subthreshold, we find that the neurons respond at a frequency equal to f(2)-f(1) in the harmonic situation (Delta f=0), that increases linearly with Delta f in the inharmonic case. Thus the neurons detect a frequency not present in the input; this effect is termed "ghost resonance". When one of the inputs is slightly suprathreshold the ghost resonance persists, but responses related with the frequency of the suprathreshold input also emerge. This behavior must be taken into account in experimental studies of signal integration and coincidence detection by neuronal pools.     

Phantom reflexes: muscle contractions at a frequency not physically present in the input stimuli

In the motor system, the periodic stimulation of one Ia-afferent input produces reflex muscle contractions at the input frequency. However, we observed that when two Ia monosynaptic reflex-afferent inputs are involved the periodic muscle contractions may occur at a frequency physically not present in the afferent inputs even when these inputs are sub-threshold. How can the muscles respond with such phantom reflex contractions at a frequency physically absent in the sub-threshold Ia-afferent input stimuli? Here we provide an explanation for this phenomenon in the cat spinal cord, that we termed “ghost motor response”. We recorded monosynaptic reflexes in the L7 ventral root, intracellular potentials in the motoneurons, and the associated muscular contractions elicited by stimulation of the lateral and medial gastrocnemius nerves. By stimulating with periodic pulses of sub-threshold intensities and distinct frequencies of 2 and 3 Hz the lateral and medial gastrocnemius nerves, respectively, we observed monosynaptic responses and phantom reflex muscle contractions occurring at the fundamental frequency (1 Hz), which was absent in the input stimuli. Thus we observed a reflex ghost motor response at a frequency not physically present in the inputs. We additionally studied the inharmonic case for sub-threshold stimuli and observed muscular contractions occurring at much lower frequencies, which were also conspicuously absent in the inputs. This is the first experimental evidence of a phantom reflex response in the nervous system. The observed behavior was modeled by numerical simulations of a pool of neurons subjected to two different input pulses.     

Firing-rate models capture essential response dynamics of LGN relay cells

Firing-rate models provide a practical tool for studying signal processing in the early visual system, permitting more thorough mathematical analysis than spike-based models. We show here that essential response properties of relay cells in the lateral geniculate nucleus (LGN) can be captured by surprisingly simple firing-rate models consisting of a low-pass filter and a nonlinear activation function. The starting point for our analysis are two spiking neuron models based on experimental data: a spike-response model fitted to data from macaque (Carandini et al. J. Vis., 20(14), 1–2011, 2007), and a model with conductance-based synapses and afterhyperpolarizing currents fitted to data from cat (Casti et al. J. Comput. Neurosci., 24(2), 235–252, 2008). We obtained the nonlinear activation function by stimulating the model neurons with stationary stochastic spike trains, while we characterized the linear filter by fitting a low-pass filter to responses to sinusoidally modulated stochastic spike trains. To account for the non-Poisson nature of retinal spike trains, we performed all analyses with spike trains with higher-order gamma statistics in addition to Poissonian spike trains. Interestingly, the properties of the low-pass filter depend only on the average input rate, but not on the modulation depth of sinusoidally modulated input. Thus, the response properties of our model are fully specified by just three parameters (low-frequency gain, cutoff frequency, and delay) for a given mean input rate and input regularity. This simple firing-rate model reproduces the response of spiking neurons to a step in input rate very well for Poissonian as well as for non-Poissonian input. We also found that the cutoff frequencies, and thus the filter time constants, of the rate-based model are unrelated to the membrane time constants of the underlying spiking models, in agreement with similar observations for simpler models.     

Testing of information condensation in a model reverberating spiking neural network

Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.     

Modelling the isometric force response to multiple pulse stimuli in locust skeletal muscle

An improved model of locust skeletal muscle will inform on the general behaviour of invertebrate and mammalian muscle with the eventual aim of improving biomedical models of human muscles, embracing prosthetic construction and muscle therapy. In this article, the isometric response of the locust hind leg extensor muscle to input pulse trains is investigated. Experimental data was collected by stimulating the muscle directly and measuring the force at the tibia. The responses to constant frequency stimulus trains of various frequencies and number of pulses were decomposed into the response to each individual stimulus. Each individual pulse response was then fitted to a model, it being assumed that the response to each pulse could be approximated as an impulse response and was linear, no assumption were made about the model order. When the interpulse frequency (IPF) was low and the number of pulses in the train small, a second-order model provided a good fit to each pulse. For moderate IPF or for long pulse trains a linear third-order model provided a better fit to the response to each pulse. The fit using a second-order model deteriorated with increasing IPF. When the input comprised higher IPFs with a large number of pulses the assumptions that the response was linear could not be confirmed. A generalised model is also presented. This model is second-order, and contains two nonlinear terms. The model is able to capture the force response to a range of inputs. This includes cases where the input comprised of higher frequency pulse trains and the assumption of quasi-linear behaviour could not be confirmed.     

A proposed mechanism for multiplication of neural signals

The probability of the joint occurrence of two statistically independent events is the product of the probabilities of the individual events. This fact is used to show that a neuron which detects coincident arrivals of spikes from two input neurons can function as a multiplier, i.e. its average output spike frequency is proportional to the product of the average input spike frequencies. The theoretical analysis is checked in two ways: (a) Computer simulations confirm the derived expressions for the output frequency and show that increasing the jitter in the input spike trains improves the operation of the multiplier by making the output spike train more regular (b) Experimentally recorded spike trains are used to demonstrate that the type and amount of jitter present in real spike trains is adequate for satisfactory operation of the proposed scheme for multiplication. The operating characteristics of the proposed multiplier make it an attractive candidate for the multiplicative mechanism that is involved in the optomotor response of insects.     

The dependence of impulse propagation speed on firing frequency, dispersion, for the Hodgkin-Huxley model.

Propagation speed of an impulse is influenced by previous activity. A pulse following its predecessor too closely may travel more slowly than a solitary pulse. In contrast, for some range of interspike intervals, a pulse may travel faster than normal because of a possible superexcitable phase of its predecessor's wake. Thus, in general, pulse speeds and interspike intervals will not remain constant during propagation. We consider these issues for the Hodgkin-Huxley cable equations. First, the relation between speed and frequency or interspike interval, the dispersion relation, is computed for particular solutions, steadily propagating periodic wave trains. For each frequency, omega, below some maximum frequency, omega max, we find two such solutions, one fast and one slow. The latter are likely unstable as a computational example illustrates. The solitary pulse is obtained in the limit as omega tends to zero. At high frequency, speed drops significantly below the solitary pulse speed; for 6.3 degrees C, the drop at omega max is greater than 60%. For an intermediate range of frequencies, supernormal speeds are found and these are correlated with oscillatory swings in sub- and superexcitability in the return to rest of an impulse. Qualitative consequences of the dispersion relation are illustrated with several different computed pulse train responses of the full cable equations for repetitively applied current pulses. Moreover, changes in pulse speed and interspike interval during propagation are predicted quantitatively by a simple kinematic approximation which applies the dispersion relation, instantaneously, to individual pulses. One example shows how interspike time intervals can be distorted during propagation from a ratio of 2:1 at input to 6:5 at a distance of 6.5 cm.     

Mode dynamics of interacting neural populations by bi-orthogonal spectral decomposition

A system of coupled bistable Hopf oscillators with an external periodic input source was used to model the ability of interacting neural populations to synchronize and desynchronize in response to variations of the input signal. We propose that, in biological systems, the settings of internal and external coupling strengths will affect the behaviour of the system to a greater degree than the input frequency. While input frequency and coupling strength were varied, the spatio-temporal dynamics of the network was examined by the bi-orthogonal decomposition technique. Within this method, effects of variation of input frequency and coupling strength were analyzed in terms of global, spatial and temporal mode entropy and energy, using the spatio-temporal data of the system. We observed a discontinuous evolution of spatio-temporal patterns depending sensitively on both the input frequency and the internal and external coupling strengths of the network. Received: 10 June 1998 / Accepted in revised form: 9 August 1999     

A method of nonlinear analysis in the frequency domain.

A method is developed for the analysis of nonlinear biological systems based on an input temporal signal that consists of a sum of a large number of sinusoids. Nonlinear properties of the system are manifest by responses at harmonics and intermodulation frequencies of the input frequencies. The frequency kernels derived from these nonlinear responses are similar to the Fourier transforms of the Wiener kernels. Guidelines for the choice of useful input frequency sets, and examples satisfying these guidelines, are given. A practical algorithm for varying the relative phases of the input sinusoids to separate high-order interactions is presented. The utility of this technique is demonstrated with data obtained from a cat retinal ganglion cell of the Y type. For a high spatial frequency grafting, the entire response is contained in the even-order nonlinear components. Even at low contrast, fourth-order components are detectable. This suggests the presence of an essential nonlinearity in the functional pathway of the Y cell, with its singularity at zero contrast.     

Factors Influencing the Estimates of Correlation between Motor Unit Activities in Humans

Background

Alpha motoneurons receive common synaptic inputs from spinal and supraspinal pathways. As a result, a certain degree of correlation can be observed between motoneuron spike trains during voluntary contractions. This has been studied by using correlation measures in the time and frequency domains. These measures are interpreted as reflecting different types of connectivity in the spinal networks, although the relation between the degree of correlation of the output motoneuron spike trains and of their synaptic inputs is unclear.

Methodology/Principal Findings

In this study, we analyze theoretically this relation and we complete this analysis by simulations and experimental data on the abductor digiti minimi muscle. The results demonstrate that correlation measures between motoneuron output spike trains are inherently influenced by the discharge rate and that this influence cannot be compensated by normalization. Because of the influence of discharge rate, frequency domain measures of correlation (coherence) do not identify the full frequency content of the common input signal when computed from pairs of motoneurons. Rather, an increase in sampling rate is needed by using cumulative spike trains of several motoneurons. Moreover, the application of averaging filters to the spike trains influences the magnitude of the estimated correlation levels calculated in the time, but not in the frequency domain (coherence).

Conclusions

It is concluded that the analysis of coherence in different frequency bands between cumulative spike trains of a sufficient number of motoneurons provides information on the spectrum of the common synaptic input. Nonetheless, the absolute values of coherent peaks cannot be compared across conditions with different cumulative discharge rates.     

Synchronizing a multicellular system by external input: an artificial control strategy

MOTIVATION: Although there are significant advances on elucidating the collective behaviors on biological organisms in recent years, the essential mechanisms by which the collective rhythms arise remain to be fully understood, and further how to synchronize multicellular networks by artificial control strategy has not yet been well explored. RESULTS: A control strategy is developed to synchronize gene regulatory networks in a multicellular system when spontaneous synchronization cannot be achieved. We first construct an impulsive control system to model the process of periodically injecting coupling substances with constant or random impulsive control amounts into the common extracellular medium, and further study its effects on the dynamics of individual cells. We derive the threshold of synchronization induced by the periodic substance input. Therefore, we can synchronize the multicellular network to a specific collective behavior by changing the frequency and amplitude of the periodic stimuli. Moreover, a two-stage scheme is proposed to facilitate the synchronization in this paper. We show that the presence of the external input may also initiate different dynamics. The multicellular network of coupled repressilators is used to show the effectiveness of the proposed method. The results not only provide a perspective to understand the interactions between external stimuli and intrinsic physiological rhythms, but also may lead to development of realistic artificial control strategy and medical therapy. AVAILABILITY: CONTACT: aihara@sat.t.u-tokyo.ac.jp.     

Calcium Input Frequency, Duration and Amplitude Differentially Modulate the Relative Activation of Calcineurin and CaMKII

NMDA receptor dependent long-term potentiation (LTP) and long-term depression (LTD) are two prominent forms of synaptic plasticity, both of which are triggered by post-synaptic calcium elevation. To understand how calcium selectively stimulates two opposing processes, we developed a detailed computational model and performed simulations with different calcium input frequencies, amplitudes, and durations. We show that with a total amount of calcium ions kept constant, high frequencies of calcium pulses stimulate calmodulin more efficiently. Calcium input activates both calcineurin and Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) at all frequencies, but increased frequencies shift the relative activation from calcineurin to CaMKII. Irrespective of amplitude and duration of the inputs, the total amount of calcium ions injected adjusts the sensitivity of the system to calcium input frequencies. At a given frequency, the quantity of CaMKII activated is proportional to the total amount of calcium. Thus, an input of a small amount of calcium at high frequencies can induce the same activation of CaMKII as a larger amount, at lower frequencies. Finally, the extent of activation of CaMKII signals with high calcium frequency is further controlled by other factors, including the availability of calmodulin, and by the potency of phosphatase inhibitors.     

Activity of the forewing stretch receptor in immature and mature adult locusts

Maturation of the flight system of Locusta migratoria occurs during the first two weeks following imaginal ecdysis. One aspect of maturation is an increase in the wingbeat frequency from about 13 Hz to about 23 Hz. We investigated physiological and anatomical mechanisms that may contribute to this process. The difference between the frequencies of the central flight rhythms of immature and mature deafferented preparations was not as great as that between the wingbeat frequencies of immature and mature intact animals. Results from static and dynamic wing elevation showed that the intensity of the forewing stretch receptor response to a given stimulus increased during maturation. The diameter of the main stretch receptor axon was larger and the conduction velocity of signals conveyed along the forewing stretch receptor and the dorsal longitudinal motoneuron was faster in mature than in immature animals. We conclude that during maturation of the flight system the forewing stretch receptor responds to wing elevation with a higher frequency signal that reaches the central circuitry faster. These findings are discussed in the context of a model that describes the influence of stretch receptor input on wingbeat frequency along with other potential mechanisms involved in flight maturation.Abbreviations fDLMn forewing dorsal longitudinal motoneuron - fSR forewing stretch receptor - SR stretch receptor     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

The spike trains generated by a neuron model are studied by the methods of nonlinear time series analysis. The results show that the spike trains are chaotic. To investigate effect of noise on transmission of chaotic spike trains, this chaotic spike trains are used as a discrete subthreshold input signal to the integrate-and-fire neuronal model and the FitzHugh-Nagumo(FHN) neuronal model working in noisy environment. The mutual information between the input spike trains and the output spike trains is calculated, the result shows that the transformation of information encoded by the chaotic spike trains is optimized by some level of noise, and stochastic resonance(SR) measured by mutual information is a property available for neurons to transmit chaotic spike trains.     

STOCHASTIC RESONANCE MEASURED BY THEMUTUAL INFORMATION IN THE NEURONSTHAT TRANSMIT CHAOTIC SPIKE TRAINS

１ＩｎｔｒｏｄｕｃｔｉｏｎＩｔｉｓｗｅｌｌｋｎｏｗｎｔｈａｔｎｅｒｖｅｃｅｌｌｓｗｏｒｋｉｎｎｏｉｓｙｅｎｖｉｒｏｎｍｅｎｔ,ａｎｄｎｏｉｓｅｓｏｕｒｃｅｓｒａｎｇｉｎｇｆｒｏｍｉｎｔｅｒｎａｌｔｈｅｒｍａｌｎｏｉｓｅｔｏｅｘｔｅｒｎａｌｐｅｒｔｕｒｂａｔｉｏｎ．Ｏｎｅｐｕｚｚｌｉｎｇｐｒｏｂｌｅｍｉｓｈｏｗｄｏｎｅｒｖｅｃｅｌｌｓａｃｃｏｍｍｏｄａｔｅｎｏｉｓｅｉｎｃｏｄｉｎｇａｎｄｔｒａｎｓｆｏｒｍｉｎｇｉｎｆｏｒｍａｔｉｏｎ,ｒｅｃｅｎｔｒｅｓｅａｒｃｈｓｈｏｗｓｔｈａｔｎｏｉｓｅｍａｙｐ…     

Informed switching strongly decreases the prevalence of antibiotic resistance in hospital wards

Antibiotic resistant nosocomial infections are an important cause of mortality and morbidity in hospitals. Antibiotic cycling has been proposed to contain this spread by a coordinated use of different antibiotics. Theoretical work, however, suggests that often the random deployment of drugs ("mixing") might be the better strategy. We use an epidemiological model for a single hospital ward in order to assess the performance of cycling strategies which take into account the frequency of antibiotic resistance in the hospital ward. We assume that information on resistance frequencies stems from microbiological tests, which are performed in order to optimize individual therapy. Thus the strategy proposed here represents an optimization at population-level, which comes as a free byproduct of optimizing treatment at the individual level. We find that in most cases such an informed switching strategy outperforms both periodic cycling and mixing, despite the fact that information on the frequency of resistance is derived only from a small sub-population of patients. Furthermore we show that the success of this strategy is essentially a stochastic phenomenon taking advantage of the small population sizes in hospital wards. We find that the performance of an informed switching strategy can be improved substantially if information on resistance tests is integrated over a period of one to two weeks. Finally we argue that our findings are robust against a (moderate) preexistence of doubly resistant strains and against transmission via environmental reservoirs. Overall, our results suggest that switching between different antibiotics might be a valuable strategy in small patient populations, if the switching strategies take the frequencies of resistance alleles into account.     

A model for energy flow in the inner ear of the bullfrog (Rana catesbeiana)

We present a quantitative mathematical model that represents the main features of the bullfrog inner ear. Calculated responses based on this model predict the observed frequency separation between the amphibian papilla and basilar papilla responses. The origin of this separation can be traced to the effect of the contact membranes on the impedance of the respective paths. Additionally, we calculated the input impedance of the periotic canal and showed that at low frequencies it acts as a bypass for most of the energy entering the ear, shunting it away from the amphibian-basilar papilla complex. As this shunting decreases with increasing frequency, we propose that the periotic canal functions as a protection mechanism to prevent overload of the amphibian papilla and basilar papilla during ventilation and for quasi-static pressure equalization. Our model explains the main features of the empirical data obtained from direct measurement of the amphibian papilla and basilar papilla contact membranes reported in an accompanying paper (this issue). Accepted: 9 March 2000     

生物电阻抗特征参数测量数据采集系统的研究

为了实现生物电阻抗参数成像,我们建立了一个快速、精确的生物电阻抗特性参数测量的数据采集系统,采取多频率组合激励并扫基频的方法,在多个激励频率不同测量阻抗信息,同时采用了一种新的高精度解调方法;正交序列数字解调,并对阻抗模型参数的估算方法进行了改进。结果表明,系统可以在1．6kHz-380kHz的频带范围内以小于100Hz的跨度编程选择工作频率,最多时可同时在四种频率下测量阻抗信息,系统的噪声水平在－80dB左右。在每周期采样64点的情况下,解调算法可将信噪比提高5．6倍以上。     

Automated determination of parameters describing power spectra of micrograph images in electron microscopy

The current theory of image formation in electron microscopy has been semi-quantitatively successful in describing data. The theory involves parameters due to the transfer function of the microscope (defocus, spherical aberration constant, and amplitude constant ratio) as well as parameters used to describe the background and attenuation of the signal. We present empirical evidence that at least one of the features of this model has not been well characterized. Namely the spectrum of the noise background is not accurately described by a Gaussian and associated "B-factor;" this becomes apparent when one studies high-quality far-from focus data. In order to have both our analysis and conclusions free from any innate bias, we have approached the questions by developing an automated fitting algorithm. The most important features of this routine, not currently found in the literature, are (i). a process for determining the cutoff for those frequencies below which observations and the currently adopted model are not in accord, (ii). a method for determining the resolution at which no more signal is expected to exist, and (iii). a parameter-with units of spatial frequency-that characterizes which frequencies mainly contribute to the signal. Whereas no general relation is seen to exist between either of these two quantities and the defocus, a simple empirical relationship approximately relates all three.