A theoretical model of foam as a consolidating continuum is proposed. The general model is applied to foam in a gravity settler. It is predicted that liquid drainage from foam in a gravity settler begins with a slow drainage stage. Next, a stage with faster drainage occurs where the drainage ...

In 2008, oversulfated chondroitin sulfate (OSCS) was identified as the main contaminant in recalled heparin. Oversulfated chondroitin sulfate can be prepared from bovine (B), porcine (P), shark (Sh), or skate (S) origin and may produce changes in the antithrombotic, bleeding, and hemodynamic profile of heparins. This study examines the interactions ...

Mentoring in academic medicine has been shown to contribute to the success of junior faculty, resulting in increased productivity, career satisfaction, and opportunities for networking. Although traditional dyadic mentoring, involving one senior faculty member and one junior protégé, is the dominant model for mentoring in the academic environment, there is ...

INTRODUCTION: The anatomic seat of the human soul has been a controversial matter of discussion in the philosophical, theological and scientific fields throughout history. One of more known hypotheses on this subject was proposed by Descartes, for whom the soul would host in the pineal gland, a brain body with ...

Theta burst stimulation, a form of repetitive transcranial magnetic stimulation, can induce lasting changes in corticospinal excitability that are thought to involve long-term potentiation/depression (LTD/LTD)-like effects on cortical synapses. The pattern of delivery of TBS is crucial in determining the direction of change in synaptic efficiency. Previously we explained this ...

In the adult nervous system, chemical neurotransmission between neurons is essential for information processing. However, neurotransmission is also important for patterning circuits during development, but its precise roles have yet to be identified, and some remain highly debated. Here, we highlight viewpoints that have come to be widely accepted or ...

The brain is an amazing information processing system that allows organisms to adaptively monitor and control complex dynamic interactions with their environment across multiple spatial and temporal scales. Mathematical modeling and computer simulation techniques have become essential tools in understanding diverse aspects of neural processing ranging from sub-millisecond temporal coding ...

A minimalist model of magnocellular vasopressin neurones was developed to examine the hypothesis that their phasic behaviour is the product of intrinsic voltage- and activity-dependent intracellular mechanisms that create a bistable dynamical system. The model can closely match a range of phasic behaviours recorded in vasopressin cells in vivo, as ...

Short-term plasticity (STP) represents a key neuronal mechanism of information processing. In excitatory hippocampal synapses, STP serves as a high-pass filter optimized for the transmission of information-carrying place-field discharges. This STP filter enables synapses to perform a highly nonlinear, switch-like operation permitting the passage and amplification of signals with place-field-like ...

I review a class of hybrid models of neurons that combine continuous spike-generation mechanisms and a discontinuous 'after-spike' reset of state variables. Unlike Hodgkin-Huxley-type conductance-based models, the hybrid spiking models have a few parameters derived from the bifurcation theory; instead of matching neuronal electrophysiology, they match neuronal dynamics. I present ...

Reports an error in "Neurally constrained modeling of perceptual decision making" by Braden A. Purcell, Richard P. Heitz, Jeremiah Y. Cohen, Jeffrey D. Schall, Gordon D. Logan and Thomas J. Palmeri (Psychological Review, 2010[Oct], Vol 117[4], 1113-1143). Two grant numbers were incorrectly cited in the author note, and one correct ...

We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize ...

Recent bursts in the incidence of large wildfires worldwide have raised concerns about the influence climate change and humans might have on future fire activity. Comparatively little is known, however, about the relative importance of these factors in shaping global fire history. Here we use fire and climate modeling, combined ...

A novel method is presented for calculating the information channel capacity of spike trains. This method works by fitting a χ-distribution to the distribution of distances between responses to the same stimulus: the χ-distribution is the length distribution for a vector of Gaussian variables. The dimension of this vector defines ...

Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. ...

Stochastic models of synaptic plasticity propose that single synapses perform a directed random walk of fixed step sizes in synaptic strength, thereby embracing the view that the mechanisms of synaptic plasticity constitute a stochastic dynamical system. However, fluctuations in synaptic strength present a formidable challenge to such an approach. We ...

One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop ...

Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of ...

In the last decade there has been a great amount of research investigating the role of simulation in our ability to infer the underlying intentions of any observed action. The majority of studies have focussed on the role of mirror neurons and the network of cortical areas active during action ...

One of the two primary classes of models of grid cell spatial firing uses interference between oscillators at dynamically modulated frequencies. Generally, these models are presented in terms of idealized oscillators (modeled as sinusoids), which differ from biological oscillators in multiple important ways. Here we show that two more realistic, ...

High-frequency oscillations (HFOs) are an emerging biomarker for epileptic tissue. Yet the mechanism by which HFOs are produced is unknown, and their rarity makes them difficult to study. Our objective was to examine the occurrence of HFOs in relation to action potentials (APs) and the effect of microstimulation in the ...

The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable ...

Large data sets arising from neurophysiological experiments are frequently observed with repeating temporal patterns. Our ability to decode these patterns is dependent on the development of methods to assess whether the patterns are significant or occurring by chance. Given a hypothesized sequence within these data, we derive probability formulas to ...

The leaky integrate-and-fire neuronal model proposed in Stevens and Zador (1998), in which time constant and resting potential are postulated to be time dependent, is revisited within a stochastic framework in which the membrane potential is mathematically described as a gauss-diffusion process. The first-passage-time probability density, miming in such a ...

One approach for understanding the encoding of information by spike trains is to fit statistical models and then test their goodness of fit. The time-rescaling theorem provides a goodness-of-fit test consistent with the point process nature of spike trains. The interspike intervals (ISIs) are rescaled (as a function of the ...

Stochastic accumulator models account for response time in perceptual decision-making tasks by assuming that perceptual evidence accumulates to a threshold. The present investigation mapped the firing rate of frontal eye field (FEF) visual neurons onto perceptual evidence and the firing rate of FEF movement neurons onto evidence accumulation to test ...

When motor units are discharging tonically, transient excitatory synaptic inputs produce an increase in the probability of spike occurrence and also increase the instantaneous discharge rate. Several researchers have proposed that these induced changes in discharge rate and probability can be used to estimate the amplitude of the underlying excitatory ...

One standard interpretation of networks of cortical neurons is that they form dynamical attractors. Computations such as stimulus estimation are performed by mapping inputs to points on the networks' attractive manifolds. These points represent population codes for the stimulus values. However, this standard interpretation is hard to reconcile with the ...

Recently, the authors published a sequential decoding algorithm for motor brain-machine interfaces (BMIs) that infers movement directly from spike trains and produces a new kinematic output every time an observation of neural activity is present at its input. Such a methodology also needs a special instantaneous neuronal encoding model to ...

Neural activity is nonstationary and varies across time. Hidden Markov models (HMMs) have been used to track the state transition among quasi-stationary discrete neural states. Within this context, an independent Poisson model has been used for the output distribution of HMMs; hence, the model is incapable of tracking the change ...

Much of our understanding of the response of savanna systems to fire disturbance relies on observations derived from manipulative fire plot studies. Equivocal findings from both recent Australian and African savanna fire plot assessments have significant implications for informing conservation management and reliable estimation of biomass stocks and dynamics. Influential ...

The Drosophila larval neuromuscular junction (NMJ) shares many structural and functional similarities to synapses in other animals, including humans. These include the basic feature of synaptic transmission, as well as the molecular mechanisms regulating the synaptic vesicle cycle. Because of its large size, easy accessibility, and well-characterized genetics, the fly ...

The Drosophila larval neuromuscular junction (NMJ) shares many structural and functional similarities to synapses in other animals, including humans. These include the basic feature of synaptic transmission, as well as the molecular mechanisms regulating the synaptic vesicle cycle. Because of its large size, easy accessibility, and the well-characterized genetics, the ...

Fire scars are used widely to reconstruct historical fire regime parameters in forests around the world. Because fire scars provide incomplete records of past fire occurrence at discrete points in space, inferences must be made to reconstruct fire frequency and extent across landscapes using spatial networks of fire-scar samples. Assessing ...

The lack of a deeper understanding of how olfactory sensory neurons (OSNs) encode odors has hindered the progress in understanding the olfactory signal processing in higher brain centers. Here we employ methods of system identification to investigate the encoding of time-varying odor stimuli and their representation for further processing in ...

This work presents a neuroinformatic method for deriving mechanistic descriptions of fine-structured neural activity. This is a new development in the computer-assisted analysis of dynamics in conductance-based models, which is illustrated using single compartment models of an action potential. A sequence of abstract, qualitative motifs is inferred from this analysis, ...

Over the past decade fMRI researchers have developed increasingly sensitive techniques for analyzing the information represented in BOLD activity. The most popular of these techniques is linear classification, a simple technique for decoding information about experimental stimuli or tasks from patterns of activity across an array of voxels. A more ...

Point-process models have been shown to be useful in characterizing neural spiking activity as a function of extrinsic and intrinsic factors. Most point-process models of neural activity are parametric, as they are often efficiently computable. However, if the actual point process does not lie in the assumed parametric class of ...

Markov random field (MRF) and belief propagation have given birth to stereo vision algorithms with top performance. This article explores their biological plausibility. First, an MRF model guided by physiological and psychophysical facts was designed. Typically an MRF-based stereo vision algorithm employs a likelihood function that reflects the local similarity ...

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity ...

This paper presents a model of long-range cortical communication by means of a global neuronal workspace similar to that proposed by Dehaene and Naccache (2001). The model resembles that of Shanahan (2008), which was based on reverberating circuits of one-to-one connections, but uses a stochastic wiring regime in place of ...

To understand the functional connectivity of neural networks, it is important to develop simple and incisive descriptors of multineuronal firing patterns. Analysis at the pairwise level has proven to be a powerful approach in the retina, but it may not suffice to understand complex cortical networks. Here we address the ...

Conductance-based neuron models are frequently employed to study the dynamics of biological neural networks. For speed and ease of use, these models are often reduced in morphological complexity. Simplified dendritic branching structures may process inputs differently than full branching structures, however, and could thereby fail to reproduce important aspects of ...

To eradicate or effectively contain a biological invasion, all or most reproductive individuals of the invasion must be found and destroyed. To help find individual invading organisms, predictions of probable locations can be made with statistical models. We estimated spread dynamics based on time-series data and then used model-derived predictions ...

Analysis of correlated spike trains is a hot topic of research in computational neuroscience. A general model of probability distributions for spikes includes too many parameters to be of use in analyzing real data. Instead, we need a simple but powerful generative model for correlated spikes. We developed a class ...

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning ...

Recent advances in the analysis of neuronal activities suggest that the instantaneous activity patterns can be mostly explained by considering only first-order and pairwise interactions between recorded elements, i.e., action potentials or local field potentials (LFP), and do not require higher-than-pairwise-order interactions. If generally applicable, this pairwise approach greatly simplifies ...

Several stochastic models, with various degrees of complexity, have been proposed to model the neuronal activity from different parts of the human brain. In this article, we use a simple Ornstein-Uhlenbeck process (OUP) to model the spike activity recorded from the subthalamic nucleus of patients suffering from Parkinson's disease at ...

Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional ...

The degree of grass curing, i.e. the proportion of dehydrated dead grass per unit grassland area, is one of the most important parameters affecting grassland fire risk and fire behaviour. The objective of the present study was to develop a simple relationship between grass moisture and grass curing to use ...