Masking efficiency as a function of stimulus onset asynchrony for spatial-frequency detection and identification

Detection and identification thresholds for grating targets were measured in the presence of a compound mask grating as a function of the stimulus onset asynchrony (SOA). The detection and identification SOA functions are both reversed U-shaped but they are not parallel. The detection-to-identification ratio is itself a reversed U-shaped function of SOA, even for stimuli two octaves apart, with a peak between +20 and +60 ms SOA (backward masking). It is argued that these results support the hypothesis according to which detection and identification are serial processes.     

Effect of periodic stimulus on a neuronal diffusion model with signal-dependent noise

To relate the noise intensity with a periodically modulated input signal in a single neuron stochastic model we introduce a diffusion model with both time modulated drift and diffusion coefficient. Such a model is the continuous version of a Stein model with time oscillating frequencies for the Poisson processes describing the inputs impinging on the neuron. We focus here on some aspects of the resonance phenomenon for such a model. We compare the corresponding interspike interval distribution with the analogous distribution for a model sharing the same parameter values, but with constant noise intensity. Examples with two different levels for this noise intensity are discussed. The enhancement of the height of the peaks in the interspike interval distribution appearing at the modulation period, the improvement of the phase locking behavior and an enlargement of the noise ranges where a resonance like behavior arises are the main features observed in the considered cases.     

Human sleep and circadian rhythms: a simple model based on two coupled oscillators

We propose a model of the human circadian system. The sleep-wake and body temperature rhythms are assumed to be driven by a pair of coupled nonlinear oscillators described by phase variables alone. The novel aspect of the model is that its equations may be solved analytically. Computer simulations are used to test the model against sleep-wake data pooled from 15 studies of subjects living for weeks in unscheduled, time-free environments. On these tests the model performs about as well as the existing models, although its mathematical structure is far simpler.Supported by NIGMS Grant No. 5-R01-GM-30719-03     

AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model

Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to traditional antimicrobial drugs. In this study, we developed an improved AMP classification model, called AMP-BERT. We propose a deep learning model with a fine-tuned bidirectional encoder representations from transformers (BERT) architecture designed to extract structural/functional information from input peptides and identify each input as AMP or non-AMP. We compared the performance of our proposed model and other machine/deep learning-based methods. Our model, AMP-BERT, yielded the best prediction results among all models evaluated with our curated external dataset. In addition, we utilized the attention mechanism in BERT to implement an interpretable feature analysis and determine the specific residues in known AMPs that contribute to peptide structure and antimicrobial function. The results show that AMP-BERT can capture the structural properties of peptides for model learning, enabling the prediction of AMPs or non-AMPs from input sequences. AMP-BERT is expected to contribute to the identification of candidate AMPs for functional validation and drug development. The code and dataset for the fine-tuning of AMP-BERT is publicly available at https://github.com/GIST-CSBL/AMP-BERT .     

Six types of multistability in a neuronal model based on slow calcium current

Background

Multistability of oscillatory and silent regimes is a ubiquitous phenomenon exhibited by excitable systems such as neurons and cardiac cells. Multistability can play functional roles in short-term memory and maintaining posture. It seems to pose an evolutionary advantage for neurons which are part of multifunctional Central Pattern Generators to possess multistability. The mechanisms supporting multistability of bursting regimes are not well understood or classified.

Methodology/Principal Findings

Our study is focused on determining the bio-physical mechanisms underlying different types of co-existence of the oscillatory and silent regimes observed in a neuronal model. We develop a low-dimensional model typifying the dynamics of a single leech heart interneuron. We carry out a bifurcation analysis of the model and show that it possesses six different types of multistability of dynamical regimes. These types are the co-existence of 1) bursting and silence, 2) tonic spiking and silence, 3) tonic spiking and subthreshold oscillations, 4) bursting and subthreshold oscillations, 5) bursting, subthreshold oscillations and silence, and 6) bursting and tonic spiking. These first five types of multistability occur due to the presence of a separating regime that is either a saddle periodic orbit or a saddle equilibrium. We found that the parameter range wherein multistability is observed is limited by the parameter values at which the separating regimes emerge and terminate.

Conclusions

We developed a neuronal model which exhibits a rich variety of different types of multistability. We described a novel mechanism supporting the bistability of bursting and silence. This neuronal model provides a unique opportunity to study the dynamics of networks with neurons possessing different types of multistability.     

The aging brain: protein phosphorylation as a target of changes in neuronal function.

There is evidence that senescence affects neurotransmission at different levels. In particular, this review summarizes the studies on age-dependent modifications in protein phosphorylation, which represents the final pathway in the action of transmitters and hormones at neuronal level. Cyclic AMP-dependent protein kinase and protein kinase C have been reported to be modified during aging in various cerebral areas; the changes may involve either enzyme activity or substrate availability. These findings can be related to the alterations in neurotransmitter function and synaptic efficiency observed in the senescent brain. The activity of the other types of protein kinases (tyrosine-, cGMP-, calcium/calmodulin-dependent) during aging needs to be explored. An emerging point is the role of protein phosphorylation in the transfer of membrane signals to the nucleus, for the activation or disactivation of specific genes responsible for long-term neuronal events. Along this view, alterations in protein kinase pathway during senescence would ultimately affect gene expression, resulting in long term modifications of cell function. The reviewed literature opens the perspective of restoring some of the deficits associated with senescence by modulating protein phosphorylation pathway.     

Excitability of septo-hippocampal axon terminals: changes as a function of the level of neuronal activity

The septo-hippocampal terminals were electrically stimulated at the level of the gyrus dentatus in urethane anesthetized rats and antidromic responses were recorded in the medial septum. The excitability of the terminals was assessed by the threshold of terminal antidromic activation. An increase in the discharge frequency of the septal neurons following a microiontophoretic application of glutamate to their soma induced an increase in the antidromic activation threshold, i.e. a decrease in excitability of the terminals. An application of GABA which inhibited septal neuronal activity, induced a decrease in the antidromic activation threshold, i.e. an increase in the terminal excitability of septo-hippocampal neurons. These results are discussed in the light of the presynaptic autoreceptor hypothesis.     

Noise effects on robust synchronization of a small pacemaker neuronal ensemble via nonlinear controller: electronic circuit design

In this paper, we report on the synchronization of a pacemaker neuronal ensemble constituted of an AB neuron electrically coupled to two PD neurons. By the virtue of this electrical coupling, they can fire synchronous bursts of action potential. An external master neuron is used to induce to the whole system the desired dynamics, via a nonlinear controller. Such controller is obtained by a combination of sliding mode and feedback control. The proposed controller is able to offset uncertainties in the synchronized systems. We show how noise affects the synchronization of the pacemaker neuronal ensemble, and briefly discuss its potential benefits in our synchronization scheme. An extended Hindmarsh–Rose neuronal model is used to represent a single cell dynamic of the network. Numerical simulations and Pspice implementation of the synchronization scheme are presented. We found that, the proposed controller reduces the stochastic resonance of the network when its gain increases.     

An ecosystem model of the phytoplankton competition in the East China Sea, as based on field experiments

An ecological dynamic model for the simulation of two pelagic phytoplankton groups is developed in this article. Model parameters were adjusted and validated based on the light-limited field culture experiments and the mesocosm experiments in the East China Sea (ECS). The calculation comparisons from the proposed model, along with field experiment observations, show that the model simulate the datasets very well, qualitatively and quantitatively. The parameters’ sensitivity analysis indicates that the competition between the diatoms and dinoflagellates is most sensitive to the photosynthetic process, followed by the exudation process of the phytoplankton, while the autolysis and respiration processes of phytoplankton and the grazing and exudation processes of zooplankton can also influence this competition to some extent. The sensitive parameters include: the photosynthetic optimal specific rate; the optimal irradiance and optimal temperature for phytoplankton growth; and the half-saturation constant for limiting nutrients, etc. Results of the sensitivity analysis also indicate that light, temperature and limiting nutrients are the controlling environmental factors for the competition between the diatoms and dinoflagellates in the ECS. In order to explore the effects of light and nutrients on the phytoplankton competition, simulations were carried out with varying light and nutrient conditions. Model simulations suggest that the diatoms favor higher irradiance, lower DIN/PO₄–P ratios, higher SiO₄–Si/DIN ratios and higher nutrient concentrations, as compared to the dinoflagellates. These results support the speculation that the increase in the DIN/PO₄−P ratio and the decrease in the SiO₄–Si/DIN ratio in the ECS may be responsible for the composition change in the functional Harmful Algal Bloom (HAB) groups from the diatom to the dinoflagellate communities over the last two decades. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Handling editor: L. Naselli-Flores     

Astrocyte- neuron interaction as a mechanism responsible for generation of neural synchrony: a study based on modeling and experiments

Neural synchronization is considered as an important mechanism for information processing. In addition, based on recent neurophysiologic findings, it is believed that astrocytes regulate the synaptic transmission of neuronal networks. Therefore, the present study focused on determining the functional contribution of astrocytes in neuronal synchrony using both computer simulations and extracellular field potential recordings. For computer simulations, as a first step, a minimal network model is constructed by connecting two Morris-Lecar neuronal models. In this minimal model, astrocyte-neuron interactions are considered in a functional-based procedure. Next, the minimal network is extended and a biologically plausible neuronal population model is developed which considers functional outcome of astrocyte-neuron interactions too. The employed structure is based on the physiological and anatomical network properties of the hippocampal CA1 area. Utilizing these two different levels of modeling, it is demonstrated that astrocytes are able to change the threshold value of transition from synchronous to asynchronous behavior among neurons. In this way, variations in the interaction between astrocytes and neurons lead to the emergence of synchronous/asynchronous patterns in neural responses. Furthermore, population spikes are recorded from CA1 pyramidal neurons in rat hippocampal slices to validate the modeling results. It demonstrates that astrocytes play a primary role in neuronal firing synchronicity and synaptic coordination. These results may offer a new insight into understanding the mechanism by which astrocytes contribute to stabilizing neural activities.     

The effect of dendritic voltage-gated conductances on the neuronal impedance: a quantitative model

Neuronal impedance characterizes the magnitude and timing of the subthreshold response of a neuron to oscillatory input at a given frequency. It is known to be influenced by both the morphology of the neuron and the presence of voltage-gated conductances in the cell membrane. Most existing theoretical accounts of neuronal impedance considered the effects of voltage-gated conductances but neglected the spatial extent of the cell, while others examined spatially extended dendrites with a passive or spatially uniform quasi-active membrane. We derived an explicit mathematical expression for the somatic input impedance of a model neuron consisting of a somatic compartment coupled to an infinite dendritic cable which contained voltage-gated conductances, in the more general case of non-uniform dendritic membrane potential. The validity and generality of this model was verified through computer simulations of various model neurons. The analytical model was then applied to the analysis of experimental data from real CA1 pyramidal neurons. The model confirmed that the biophysical properties and predominantly dendritic localization of the hyperpolarization-activated cation current I (h) were important determinants of the impedance profile, but also predicted a significant contribution from a depolarization-activated fast inward current. Our calculations also implicated the interaction of I (h) with amplifying currents as the main factor governing the shape of the impedance-frequency profile in two types of hippocampal interneuron. Our results provide not only a theoretical advance in our understanding of the frequency-dependent behavior of nerve cells, but also a practical tool for the identification of candidate mechanisms that determine neuronal response properties.     

Different programming modes of human saccadic eye movements as a function of stimulus eccentricity: Indications of a functional subdivision of the visual field

1. Voluntary saccadic eye movements were made toward flashes of light on the horizontal meridian, whose duration and distance from the point of fixation were varied; eye movements were measured using d.c.-electrooculography.—2. Targets within 10°–15° eccentricity are usually reached by one saccadic eye movement. When the eyes turn toward targets of more than 10°–15° eccentricity, the first saccadic eye movement falls short of the target by an angle usually not exceeding 10°. The presence of the image of the target off the fovea (visual error signal) subsequent to such an undershoot elicits, after a short interval, corrective saccades (usually one) which place the image of the target on the fovea. In the absence of a visual error signal, the probability of occurrence of corrective saccades is low, but it increases with greater target eccentricities. These observations suggest that there are different, eccentricity-dependent modes of programming saccadic eye movements.—3. Saccadic eye movements appear to be programmed in retinal coordinates. This conclusion is based on the observations that, irrespective of the initial position of the eyes in the orbit, a) there are different programming modes for eye movements to targets within and beyond 10°–15° from the fixation point, and b_ the maximum velocity of saccadic eye movements is always reached at 25° to 30° target eccentricity. —4. Distributions of latency and intersaccadic interval (ISI) are frequently multimodal, with a separation between modes of 30 to 40 msec. These observations suggest that saccadic eye movements are produced by mechanisms which, at a frequency of 30 Hz, process visual information. —5. Corrective saccades may occur after extremely short intervals (30 to 60 msec) regardless of whether or not a visual error signal is present; the eyes may not even come to a complete stop during these very short intersaccadic intervals. It is suggested that these corrective saccades are triggered by errors in the programming of the initial saccadic eye movements, and not by a visual error signal. —6. The exitence of different, eccentricity-dependent programming modes of saccadic eye movements, is further supported by anatomical, physiological, psychophysical, and neuropathological observations that suggest a dissociation of visual functions dependent on retinal eccentricity. Saccadic eye movements to targets more eccentric than 10°–15° appear to be executed by a mechanism involving the superior colliculus (perhaps independent of the visual cortex), whereas saccadic eye movements to less eccentric targets appear to depend on a mechanism involving the geniculo-cortical pathway (perhaps in collaboration with the superior colliculus).     

Discovery of novel neuronal voltage-dependent calcium channel blockers based on emopamil left hand as a bioactive template

A series of novel neuronal voltage-dependent calcium channel (VDCC) blockers, with inhibitory activity at low micromolar and moderate solubility in water, was discovered by constructing and screening a focused library based on emopamil (1) left hand (ELH) as a bioactive template.     

Functional approach based on morphology as a model of phytoplankton variability in a subtropical floodplain lake: a long-term study

Long-term phytoplankton dynamics in a floodplain lake, between periods of limnophase and potamophase was studied using the morphology-based functional classification (MBFG). The work was carried out to test the hypothesis that the temporal distribution of MBFGs is influenced by the hydrosedimentological regime of the Paraná River, and that these differences can be registered by analyzing the dominant MBFGs in the two periods. Samples were taken in an isolated floodplain lake on the Upper Paraná River floodplain, Brazil, from 2000 to 2012, and water level, water temperature, conductivity, pH, dissolved-oxygen, euphotic zone, maximum depth, and nutrients were used to explain the distribution of MBFGs. 478 taxa were identified and distributed in seven MBFGs (I–VII). MBFG V (flagellates algae) and VI (diatoms) showed 100% frequency of occurrence. MBFG II was associated exclusively with the limnophase; MBFG IV and VII were associated with limnophase periods with higher pH and dissolved-oxygen content; and MBFG I, III, V, and VI were associated with limnophase and potamophase, mainly associated with transparency, nutrients, and conductivity. The MBFG approach represented the trends of each group in terms of its occurrence and biovolume, according to the hydrosedimentological regime, providing broad-scale information on changes in the phytoplankton.