Financial time series prediction using spiking neural networks

In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al

Mathematical properties of neuronal TD-rules and differential Hebbian learning: a comparison

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transfered to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the δ-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications

Effects of Sorafenib on Intra-Tumoral Interstitial Fluid Pressure and Circulating Biomarkers in Patients with Refractory Sarcomas (NCI Protocol 6948)

Purpose: Jain Sorafenib is a multi-targeted tyrosine kinase inhibitor with therapeutic efficacy in several malignancies. Sorafenib may exert its anti-neoplastic effect in part by altering vascular permeability and reducing intra-tumoral interstitial hypertension. As correlative science with a phase II study in patients with advanced soft-tissue sarcomas (STS), we evaluated the impact of this agent on intra-tumor interstitial fluid pressure (IFP), serum circulating biomarkers, and vascular density. Patients and Methods: Patients with advanced STS with measurable disease and at least one superficial lesion amenable to biopsy received sorafenib 400 mg twice daily. Intratumoral IFP and plasma and circulating cell biomarkers were measured before and after 1–2 months of sorafenib administration. Results were analyzed in the context of the primary clinical endpoint of time-to-progression (TTP). Results: In 15 patients accrued, the median TTP was 45 days (range 14–228). Intra-tumoral IFP measurements obtained in 6 patients at baseline showed a direct correlation with tumor size. Two patients with stable disease at two months had post-sorafenib IFP evaluations and demonstrated a decline in IFP and vascular density. Sorafenib significantly increased plasma VEGF, PlGF, and SDF1$\alpha$ and decreased sVEGFR-2 levels. Increased plasma SDF1$\alpha$ and decreased sVEGFR-2 levels on day 28 correlated with disease progression. Conclusions: Pretreatment intra-tumoral IFP correlated with tumor size and decreased in two evaluable patients with SD on sorafenib. Sorafenib also induced changes in circulating biomarkers consistent with expected VEGF pathway blockade, despite the lack of more striking clinical activity in this small series

Bidirectional Coupling between Astrocytes and Neurons Mediates Learning and Dynamic Coordination in the Brain: A Multiple Modeling Approach

In recent years research suggests that astrocyte networks, in addition to nutrient and waste processing functions, regulate both structural and synaptic plasticity. To understand the biological mechanisms that underpin such plasticity requires the development of cell level models that capture the mutual interaction between astrocytes and neurons. This paper presents a detailed model of bidirectional signaling between astrocytes and neurons (the astrocyte-neuron model or AN model) which yields new insights into the computational role of astrocyte-neuronal coupling. From a set of modeling studies we demonstrate two significant findings. Firstly, that spatial signaling via astrocytes can relay a “learning signal” to remote synaptic sites. Results show that slow inward currents cause synchronized postsynaptic activity in remote neurons and subsequently allow Spike-Timing-Dependent Plasticity based learning to occur at the associated synapses. Secondly, that bidirectional communication between neurons and astrocytes underpins dynamic coordination between neuron clusters. Although our composite AN model is presently applied to simplified neural structures and limited to coordination between localized neurons, the principle (which embodies structural, functional and dynamic complexity), and the modeling strategy may be extended to coordination among remote neuron clusters

The Accuracy of Survival Time Prediction for Patients with Glioma Is Improved by Measuring Mitotic Spindle Checkpoint Gene Expression

Identification of gene expression changes that improve prediction of survival time across all glioma grades would be clinically useful. Four Affymetrix GeneChip datasets from the literature, containing data from 771 glioma samples representing all WHO grades and eight normal brain samples, were used in an ANOVA model to screen for transcript changes that correlated with grade. Observations were confirmed and extended using qPCR assays on RNA derived from 38 additional glioma samples and eight normal samples for which survival data were available. RNA levels of eight major mitotic spindle assembly checkpoint (SAC) genes (BUB1, BUB1B, BUB3, CENPE, MAD1L1, MAD2L1, CDC20, TTK) significantly correlated with glioma grade and six also significantly correlated with survival time. In particular, the level of BUB1B expression was highly correlated with survival time (p<0.0001), and significantly outperformed all other measured parameters, including two standards; WHO grade and MIB-1 (Ki-67) labeling index. Measurement of the expression levels of a small set of SAC genes may complement histological grade and other clinical parameters for predicting survival time

Effective transvascular delivery of nanoparticles across the blood-brain tumor barrier into malignant glioma cells

<p>Abstract</p> <p>Background</p> <p>Effective transvascular delivery of nanoparticle-based chemotherapeutics across the blood-brain tumor barrier of malignant gliomas remains a challenge. This is due to our limited understanding of nanoparticle properties in relation to the physiologic size of pores within the blood-brain tumor barrier. Polyamidoamine dendrimers are particularly small multigenerational nanoparticles with uniform sizes within each generation. Dendrimer sizes increase by only 1 to 2 nm with each successive generation. Using functionalized polyamidoamine dendrimer generations 1 through 8, we investigated how nanoparticle size influences particle accumulation within malignant glioma cells.</p> <p>Methods</p> <p>Magnetic resonance and fluorescence imaging probes were conjugated to the dendrimer terminal amines. Functionalized dendrimers were administered intravenously to rodents with orthotopically grown malignant gliomas. Transvascular transport and accumulation of the nanoparticles in brain tumor tissue was measured <it>in vivo </it>with dynamic contrast-enhanced magnetic resonance imaging. Localization of the nanoparticles within glioma cells was confirmed <it>ex vivo </it>with fluorescence imaging.</p> <p>Results</p> <p>We found that the intravenously administered functionalized dendrimers less than approximately 11.7 to 11.9 nm in diameter were able to traverse pores of the blood-brain tumor barrier of RG-2 malignant gliomas, while larger ones could not. Of the permeable functionalized dendrimer generations, those that possessed long blood half-lives could accumulate within glioma cells.</p> <p>Conclusion</p> <p>The therapeutically relevant upper limit of blood-brain tumor barrier pore size is approximately 11.7 to 11.9 nm. Therefore, effective transvascular drug delivery into malignant glioma cells can be accomplished by using nanoparticles that are smaller than 11.7 to 11.9 nm in diameter and possess long blood half-lives.</p

Respectful leadership:Reducing performance challenges posed by leader role incongruence and gender dissimilarity

We investigate how respectful leadership can help overcome the challenges for follower performance that female leaders face when working (especially with male) followers. First, based on role congruity theory, we illustrate the biases faced by female leaders. Second, based on research on gender (dis-)similarity, we propose that these biases should be particularly pronounced when working with a male follower. Finally, we propose that respectful leadership is most conducive to performance in female leader–male follower dyads compared with all other gender configurations. A multi-source field study (N = 214) provides partial support for our hypothesis. While our hypothesized effect was confirmed, respectful leadership seems to be generally effective for female leaders irrespective of follower gender, thus lending greater support in this context to the arguments of role congruity rather than gender dissimilarity

Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron. Using a simple cascade model, we demonstrate that triggering somatic firing by a relatively small dendritic branch requires the amplification of local events by dendritic spiking and synaptic plasticity. The moderately branching dendritic tree of granule cells seems optimal for this computation since larger dendritic trees favor local plasticity by isolating dendritic compartments, while reliable detection of individual dendritic spikes in the soma requires a low branch number. Finally, we demonstrate that these parallel dendritic computations could contribute to the generation of multiple independent place fields of hippocampal granule cells