Spiking Dynamics during Perceptual Grouping in the Laminar Circuits of Visual Cortex

Grouping of collinear boundary contours is a fundamental process during visual perception. Illusory contour completion vividly illustrates how stable perceptual boundaries interpolate between pairs of contour inducers, but do not extrapolate from a single inducer. Neural models have simulated how perceptual grouping occurs in laminar visual cortical circuits. These models predicted the existence of grouping cells that obey a bipole property whereby grouping can occur inwardly between pairs or greater numbers of similarly oriented and co-axial inducers, but not outwardly from individual inducers. These models have not, however, incorporated spiking dynamics. Perceptual grouping is a challenge for spiking cells because its properties of collinear facilitation and analog sensitivity to inducer configurations occur despite irregularities in spike timing across all the interacting cells. Other models have demonstrated spiking dynamics in laminar neocortical circuits, but not how perceptual grouping occurs. The current model begins to unify these two modeling streams by implementing a laminar cortical network of spiking cells whose intracellular temporal dynamics interact with recurrent intercellular spiking interactions to quantitatively simulate data from neurophysiological experiments about perceptual grouping, the structure of non-classical visual receptive fields, and gamma oscillations.CELEST, an NSF Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001); Defense Advanced Research Project Agency (HR001-09-C-0011

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Methods and Apparatus for Autonomous Robotic Control

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements

Asymmetry of parietal interhemispheric connections in humans

Visuospatial abilities are preferentially mediated by the right hemisphere. Although this asymmetry of function is thought to be due to an unbalanced interaction between cerebral hemispheres, the underlying neurophysiological substrate is still largely unknown. Here, using a method of trifocal transcranial magnetic stimulation, we show that the right, but not left, human posterior parietal cortex exerts a strong inhibitory activity over the contralateral homologous area by a short-latency connection. We also clarify, using diffusion-tensor magnetic resonance imaging, that such an interaction is mediated by direct transcallosal projections located in the posterior corpus callosum. We argue that this anatomo-functional network may represent a possible neurophysiological basis for the ongoing functional asymmetry between parietal cortices, and that its damage could contribute to the clinical manifestations of neglect

Characteristics of patients with haematological and breast cancer (1996–2009) who died of heart failure-related causes after cancer therapy

Aims: To describe the characteristics and time to death of patients with breast or haematological cancer who died of heart failure (HF) after cancer therapy. Patients with an index admission for HF who died of HF-related causes (IAHF) and those with no index admission for HF who died of HF-related causes (NIAHF) were compared. Methods and results: We performed a linked data analysis of cancer registry, death registry, and hospital administration records (n = 15 987). Index HF admission must have occurred after cancer diagnosis. Of the 4894 patients who were deceased (30.6% of cohort), 734 died of HF-related causes (50.1% female) of which 279 (38.0%) had at least one IAHF (41.9% female) post-cancer diagnosis. Median age was 71 years [interquartile range (IQR) 62–78] for IAHF and 66 years (IQR 56–74) for NIAHF. There were fewer chemotherapy separations for IAHF patients (median = 4, IQR 2–9) compared with NIAHF patients (median = 6, IQR 2–12). Of the IAHF patients, 71% had died within 1 year of the index HF admission. There was no significant difference in HF-related mortality in IAHF patients compared with NIAHF (HR, 1.10, 95% CI, 0.94–1.29, P = 0.225). Conclusions: The profile of IAHF patients who died of HF-related causes after cancer treatment matched the current profile of HF in the general population (over half were aged ≥70 years). However, NIAHF were younger (62% were aged ≤69 years), female patients with breast cancer that died of HF-related causes before hospital admission for HF-related causes—a group that may have been undiagnosed or undertreated until death

A space-time generator for rainfall nowcasting: the PRAISEST model

International audienceThe paper introduces a new stochastic technique for forecasting rainfall in space-time domain: the PRAISEST Model (Prediction of Rainfall Amount Inside Storm Events: Space and Time). The model is based on the assumption that the rainfall height H accumulated on an interval ?t between the instants i?t and (i+1)?t and on a spatial cell of size ?x?y is correlated either with a variable Z, representing antecedent precipitation at the same point, either with a variable W, representing simultaneous rainfall at neighbour cells. The mathematical background is given by a joined probability density fH,W,Z (h,w,z) in which the variables have a mixed nature, that is a finite probability for null value and infinitesimal probabilities for the positive values. As study area, the Calabria region, in Southern Italy, has been selected. The region has been discretised by 10 km×10 km cell grid, according to the raingauge network density in this area. Storm events belonging to 1990?2004 period were analyzed to test performances of the PRAISEST model

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Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624)