Event-driven simulation of spiking neurons with stochastic dynamics

We present a new technique, based on a proposed event-based strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions among neurons occur by means of events that are well localized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here, we extend the event-driven simulation strategy to the case in which the dynamics of the state variables in the inter-event intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single-model neurons under random current injection. The new simulation strategy allows the study of networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics

Organic matter governs N and P balance in Danube Delta lakes

The transformation of dissolved inorganic nitrogen (DIN) and soluble reactive phosphorous (SRP), and the release of dissolved organic and particulate N and P, were analyzed in two lake complexes (Uzlina-Isac and Puiu-Rosu-Rosulet) of the Danube Delta wetland during flood conditions in May and at low water level in September 2006. The Uzlina-Isac complex was hydrologically tightly-connected with the Danube River and was flushed with river-borne nutrients and organic matter. These lakes acted as effective transformers for nutrients and produced large amounts of fresh biomass, that promoted the excretion of dissolved organic N and P during active growth. Biomass breakdown created particulate matter (<0.45μm), which was widely liberated during low flow in the fall. The Puiu-Rosu-Rosulet complex was characterized by a more distant position to the Danube and proximity to the Black Sea, and received dominantly transformed organic compounds from the flow-through water and vast vegetation cover. Due to reduced nutrient input, the internal production of organic biomass also was reduced in these more remote lakes. Total N and P export from the lake nearest to the shelf was governed by dominantly dissolved organic and particulate compounds (mean 58 and 82%, respectively). Overall, this survey found that these highly productive wetlands efficiently transform nutrients into a large pool of dissolved organic and particulate N and P. Hence, wetland lakes may behave widely as net sources of organic N and P to downstream waters and coastal marine system

Neural field model for measuring and reproducing time intervals

The continuous real-time motor interaction with our environment requires the capacity to measure and produce time intervals in a highly flexible manner. Recent neurophysiological evidence suggests that the neural computational principles supporting this capacity may be understood from a dynamical systems perspective: Inputs and initial conditions determine how a recurrent neural network evolves from a “resting state” to a state triggering the action. Here we test this hypothesis in a time measurement and time reproduction experiment using a model of a robust neural integrator based on the theoretical framework of dynamic neural fields. During measurement, the temporal accumulation of input leads to the evolution of a self-stabilized bump whose amplitude reflects elapsed time. During production, the stored information is used to reproduce on a trial-by-trial basis the time interval either by adjusting input strength or initial condition of the integrator. We discuss the impact of the results on our goal to endow autonomous robots with a human-like temporal cognition capacity for natural human-robot interactions.The work received financial support from FCT through the PhD fellowship PD/BD/128183/2016, the project ”Neurofield” (POCI-01-0145-FEDER-031393) and the research centre CMAT within the project UID/MAT/00013/2013

Ready ... Go: Amplitude of the fMRI Signal Encodes Expectation of Cue Arrival Time

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals

Time Markers and Temporal Illusions

Peer reviewe

A biologically plausible model of time-scale invariant interval timing

The temporal durations between events often exert a strong influence over behavior. The details of this influence have been extensively characterized in behavioral experiments in different animal species. A remarkable feature of the data collected in these experiments is that they are often time-scale invariant. This means that response measurements obtained under intervals of different durations coincide when plotted as functions of relative time. Here we describe a biologically plausible model of an interval timing device and show that it is consistent with time-scale invariant behavior over a substantial range of interval durations. The model consists of a set of bistable units that switch from one state to the other at random times. We first use an abstract formulation of the model to derive exact expressions for some key quantities and to demonstrate time-scale invariance for any range of interval durations. We then show how the model could be implemented in the nervous system through a generic and biologically plausible mechanism. In particular, we show that any system that can display noise-driven transitions from one stable state to another can be used to implement the timing device. Our work demonstrates that a biologically plausible model can qualitatively account for a large body of data and thus provides a link between the biology and behavior of interval timing

Oribatid communities and heavy metal bioaccumulation in selected species associated with lichens in a heavily contaminated habitat

The study examines oribatid communities and heavy metal bioaccumulation in selected species associated with different microhabitats of a post-smelting dump, i.e. three lichen species of Cladonia with various growth forms and the slag substrate. The abundance of oribatids collected from the substrate was significantly lower than observed in lichen thalli. The morphology and chemical properties of lichens, and to some extent varying concentrations of heavy metals in thalli, are probably responsible for significant differences in oribatid communities inhabiting different Cladonia species. Some oribatids demonstrate the ability to accumulate zinc and cadmium with unusual efficiency, whereas lead is the most effectively regulated element by all species. A positive correlation was found between Zn content in all studied oribatids and their microhabitats. Oribatids exploring different food resources, i.e. fungivorous and non-fungivorous grazers, show considerable differences in bioconcentrations of certain elements

Between but not within species variation in the distribution of fitness effects

New mutations provide the raw material for evolution and adaptation. The distribution of fitness effects (DFE) describes the spectrum of effects of new mutations that can occur along a genome, and is therefore of vital interest in evolutionary biology. Recent work has uncovered striking similarities in the DFE between closely related species, prompting us to ask whether there is variation in the DFE among populations of the same species, or among species with different degrees of divergence, i.e., whether there is variation in the DFE at different levels of evolution. Using exome capture data from six tree species sampled across Europe we characterised the DFE for multiple species, and for each species, multiple populations, and investigated the factors potentially influencing the DFE, such as demography, population divergence and genetic background. We find statistical support for there being variation in the DFE at the species level, even among relatively closely related species. However, we find very little difference at the population level, suggesting that differences in the DFE are primarily driven by deep features of species biology, and that evolutionarily recent events, such as demographic changes and local adaptation, have little impact

Changes of the 2-stroke aerosol in the exhaust sampling system

Nanoparticle emissions of two 2-stroke scooters were investigated along the exhaust- and CVS-system (Constant Volume Sampling) with closed and with open line (cone). Due to their technology, the scooters produce different kind of aerosol (state of oxidation & SOF-content) and in addition to that they were operated with and without oxidation catalyst. The scooters represent a modern technology with direct injection TSDI* ) (two stroke direct injection) and with carburettor. The tests were performed at two constant speeds of the vehicles (20 km/h & 40 km/h) according to the measuring procedures, which were established in the previous research in the Swiss Scooter Network. The nanoparticulate emissions were measured by means of SMPS (CPC) and NanoMet (abbreviations see at the end of this paper). The most important results are: - the changes of the PSD's of the aerosol along the exhaust and CVS-system are connected to the average gas temperature and PC-concentration, which result after the different dilution steps and cooling down in the connecting pipe, - in the "open" variant of exhaust gas extraction there is a dilution step with unfiltered ambient air directly after tailpipe. This causes a stop of agglomeration, reduction of diffusion loses and increased background NPconcentration. There is also lower post oxidation of CO & HC. In some cases spontaneous condensates due to the temperature drop are supposed, - with the "closed" variant there is a stronger reduction of SMPS PC's along the gas way, than with the open variant. This is to explain with the higher temperatures and concentrations in the closed system, which enable more intense thermophoresis - and diffusion losses, - the NP-concentrations measured with "open" variant are always higher, - most intense oxidation is observed with Peugeot Carb: due to the SAS, rich tuning and a relatively high temperature level there are oxidation effects already without catalyst (temp. approx. 350 centigrade). With catalyst the temperature is in the range of 400centigrade and the oxidation is so intense, that the particles are nearly eliminated. The type of sampling: "open", or "closed" as well as the sampling position in the exhaust installation have significant influence on the measured nanoparticles emission results