A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks

There is a biological evidence to prove information is coded through precise timing of spikes in the brain. However, training a population of spiking neurons in a multilayer network to fire at multiple precise times remains a challenging task. Delay learning and the effect of a delay on weight learning in a spiking neural network (SNN) have not been investigated thoroughly. This paper proposes a novel biologically plausible supervised learning algorithm for learning precisely timed multiple spikes in a multilayer SNNs. Based on the spike-timing-dependent plasticity learning rule, the proposed learning method trains an SNN through the synergy between weight and delay learning. The weights of the hidden and output neurons are adjusted in parallel. The proposed learning method captures the contribution of synaptic delays to the learning of synaptic weights. Interaction between different layers of the network is realized through biofeedback signals sent by the output neurons. The trained SNN is used for the classification of spatiotemporal input patterns. The proposed learning method also trains the spiking network not to fire spikes at undesired times which contribute to misclassification. Experimental evaluation on benchmark data sets from the UCI machine learning repository shows that the proposed method has comparable results with classical rate-based methods such as deep belief network and the autoencoder models. Moreover, the proposed method can achieve higher classification accuracies than single layer and a similar multilayer SNN

Network on chip architecture for multi-agent systems in FPGA

A system of interacting agents is, by definition, very demanding in terms of computational resources. Although multi-agent systems have been used to solve complex problems in many areas, it is usually very difficult to perform large-scale simulations in their targeted serial computing platforms. Reconfigurable hardware, in particular Field Programmable Gate Arrays (FPGA) devices, have been successfully used in High Performance Computing applications due to their inherent flexibility, data parallelism and algorithm acceleration capabilities. Indeed, reconfigurable hardware seems to be the next logical step in the agency paradigm, but only a few attempts have been successful in implementing multi-agent systems in these platforms. This paper discusses the problem of inter-agent communications in Field Programmable Gate Arrays. It proposes a Network-on-Chip in a hierarchical star topology to enable agents’ transactions through message broadcasting using the Open Core Protocol, as an interface between hardware modules. A customizable router microarchitecture is described and a multi-agent system is created to simulate and analyse message exchanges in a generic heavy traffic load agent-based application. Experiments have shown a throughput of 1.6Gbps per port at 100 MHz without packet loss and seamless scalability characteristics

SpikeTemp: an enhanced rank-order-based learning approach for spiking neural networks with adaptive structure

This paper presents an enhanced rank - order based learning algorithm, called SpikeTemp, for Spiking Neural Networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: an encoding layer which temporally encodes real valued features into spatio-temporal spike patterns, and an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and thus reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark datasets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms and the results demonstrate the ability of SpikeTemp to classify different datasets after just one presentation of the training samples with comparable classification performance

Detecting wash trade in financial market using digraphs and dynamic programming

Wash trade refers to the illegal activities of traders who utilise carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, wash trade can be extremely damaging to the proper functioning and integrity of capital markets. Existing work focuses on collusive clique detections based on certain assumptions of trading behaviours. Effective approaches for analysing and detecting wash trade in a real-life market have yet to be developed. This paper analyses and conceptualises the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of the Knapsack problem, which can be solved by dynamic programming approaches. The proposed approach is evaluated on seven stock datasets from NASDAQ and the London Stock Exchange. Experimental results show that the proposed approach can effectively detect all primary wash trade scenarios across the selected datasets

Forecasting price movements using technical indicators: investigating the impact of varying input window length

The creation of a predictive system that correctly forecasts future changes of a stock price is crucial for investment management and algorithmic trading. The use of technical analysis for financial forecasting has been successfully employed by many researchers. Input window length is a time frame parameter required to be set when calculating many technical indicators. This study explores how the performance of the predictive system depends on a combination of a forecast horizon and an input window length for forecasting variable horizons. Technical indicators are used as input features for machine learning algorithms to forecast future directions of stock price movements. The dataset consists of ten years daily price time series for fifty stocks. The highest prediction performance is observed when the input window length is approximately equal to the forecast horizon. This novel pattern is studied using multiple performance metrics: prediction accuracy, winning rate, return per trade and Sharpe ratio

Effect of nitrogen fertilizer on growth, flowering, fruiting and nodulation of three varieties of common bean in the arid region of aïn naga (Biskra, Algeria)

Field experiments were conducted to investigate the response of common bean (Phaseolus vulgaris L.) to nitrogen fertilizer. The main factor included the fertilizer (Granular Urea 46% N), was made up of two levels: no fertilization (0 kg/plot: control) and fertilization (0.4 kg/plot), while the secondary factor was the variety (three varieties of common bean: Djedida, Nelson and Jalila). The experimental design was a randomized complete block design with split plot arrangement and replicated four times. The effect of fertilization was evaluated during two development stages (flowering and fruiting) for each variety and this on the total dry biomass, the length of the aerial and root parts, as well as on the number of secondary roots. On the other hand, the combined effect of fertilization and variety was studied on some yield parameters per plant, such as the number of flowers, pods, seeds and the harvest index. In addition, the number of nodules at the end of seed maturation was evaluated. The results indicated that the nitrogen fertilizer application significantly reduced the root length, the number of nodules and secondary roots in most of the common bean varieties. However, fertilizer application significantly increased dry matter in both flowering and fruiting stages, for the three studied varieties. Pod number per plant and seed yield was increased by the application of N fertilizer, depending on varieties and the parameters being measured. Djedida and Jalila varieties gave the best yield and can therefore be recommended to farmers. According to our results, the percentage of improvement by N fertilization on one parameter or another does not exceed an average of 20%, compared to the control. Indeed, the effect of fertilization is positive and seems to increase the harvest index by 18% in Djedida and by 20% in Jalila, compared to non-fertilized plants. Unfertilized plants of the variety Nelson showed the highest ability to nodulate

Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

—The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched. However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories