729 research outputs found

    Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes

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    This research is a survey to determine the career chosen of form four student in commerce streams. The important aspect of the career chosen has been divided into three, first is information about career, type of career and factor that most influence students in choosing a career. The study was conducted at Sekolah Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was chosen by using non-random sampling purpose method as respondent. All information was gather by using questionnaire. Data collected has been analyzed in form of frequency, percentage and mean. Results are performed in table and graph. The finding show that information about career have been improved in students career chosen and mass media is the main factor influencing students in choosing their career

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

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    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

    A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP

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    Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multineuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, that enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, that allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multidimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor inhibitory cognitive task. The test exhibits good classification performance, indicating the adequateness of our approach. In addition to its effectiveness, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc

    A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons

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    Spiking neurons are becoming increasingly popular owing to their biological plausibility and promising computational properties. Unlike traditional rate-based neural models, spiking neurons encode information in the temporal patterns of the transmitted spike trains, which makes them more suitable for processing spatiotemporal information. One of the fundamental computations of spiking neurons is to transform streams of input spike trains into precisely timed firing activity. However, the existing learning methods, used to realize such computation, often result in relatively low accuracy performance and poor robustness to noise. In order to address these limitations, we propose a novel highly effective and robust membrane potential-driven supervised learning (MemPo-Learn) method, which enables the trained neurons to generate desired spike trains with higher precision, higher efficiency, and better noise robustness than the current state-of-the-art spiking neuron learning methods. While the traditional spike-driven learning methods use an error function based on the difference between the actual and desired output spike trains, the proposed MemPo-Learn method employs an error function based on the difference between the output neuron membrane potential and its firing threshold. The efficiency of the proposed learning method is further improved through the introduction of an adaptive strategy, called skip scan training strategy, that selectively identifies the time steps when to apply weight adjustment. The proposed strategy enables the MemPo-Learn method to effectively and efficiently learn the desired output spike train even when much smaller time steps are used. In addition, the learning rule of MemPo-Learn is improved further to help mitigate the impact of the input noise on the timing accuracy and reliability of the neuron firing dynamics. The proposed learning method is thoroughly evaluated on synthetic data and is further demonstrated on real-world classification tasks. Experimental results show that the proposed method can achieve high learning accuracy with a significant improvement in learning time and better robustness to different types of noise
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