A modelling study of beta-amyloid induced change in hippocampal theta rhythm

Many dementia cases, such as Alzheimer’s disease (AD), are characterized by an increase in low frequency field potential oscillations. However, a definitive understanding of the effects of the beta-Amyloid peptide, which is a main marker of AD, on the low frequency theta rhythm (4-7Hz) is still unavailable. In this work, we investigate the neural mechanisms associated with beta-Amyloid toxicity using a conductance-based neuronal network model of the hippocampus CA1 region. We simulate the effects of beta-Amyloid on the A-type fast inactivating K+ channel by modulating the maximum conductance of the current in pyramidal cells, denoted by gA. Our simulation results demonstrate that as gA decreases (through A[beta]
blockage), the theta band power first increases then decreases. Thus there exists a value of gA that maximizes the theta band power. The neuronal and network mechanism underlying the change in theta rhythm is systematically analyzed. We show that the increase in theta power is due to the improved synchronization of pyramidal neurons, and the theta decrease is induced by the faster depolarisation of pyramidal neurons

Dynamically Reconfigurable Online Self-organising Fuzzy Neural Network with Variable Number of Inputs for Smart Home Application

A self-organising fuzzy-neural network (SOFNN) adapts its structure based on variations of the input data. Conventionally in such self-organising networks, the number of inputs providing the data is fixed. In this paper, we consider the situation where the number of inputs to a network changes dynamically during its online operation. We extend our existing work on a SOFNN such that the SOFNN can self-organise its structure based not only on its input data, but also according to the changes in the number of its inputs. We apply the approach to a smart home application, where there are certain situations when some of the existing events may be removed or new events emerge, and illustrate that our approach enhances cognitive reasoning in a dynamic smart home environment. In this case, the network identifies the removed and/or added events from the received information over time, and reconfigures its structure dynamically. We present results for different combinations of training and testing phases of the dynamic reconfigurable SOFNN using a set of realistic synthesized data. The results show the potential of the proposed method

Simulation of all-optical demultiplexing utilizing two-photon absorption in semiconductor devices for high-speed OTDM networks

The performance of a two-photon absorption (TPA) based demultiplexer in an OTDM communication system is modeled. The demultiplexer is evaluated by comparing the electrical BER of the demultiplexed and detected channel to the optical BER of the signal before the demultiplexer. An error-free demultiplexing of a 250 Gbit/s signal (25 × 10 Gbit/s channels) is shown, using a 30:1 control-to-signal peak power ratio, with a TPA device with a bandwidth of 20 GHz should be possible. The device that is fabricated for TPA is a GaAs/AlAs PIN microcavity photodetector grown on a GaAs substrate

Generation of wavelength tunable optical pulses with SMSR exceeding 50 dB by self-seeding a gain-switched source containing two FP lasers

In this letter, we show the generation of shorter pulses (∼20 ps) that exhibit side mode suppression ratios (SMSR's) greater than 50 dB and wider tuning range (48.91 nm). Our technique is based on the self-seeding of a gain-switched source containing two FP lasers

Assessing retino-geniculo-cortical connectivities in Alzheimer's Disease with a neural mass model

Longitudinal studies have shown that increase ofmean frequency within the theta band may be considered as an early symptom of progression into Alzheimer’s Disease (AD). Also, slowing of mean frequency within the alpha band has long since been known to be a defi nitive marker in AD. This work is aimed at developing a better understanding of alterations in neuronal connectivity underlying Electroencephalogram (EEG) changes in AD. Specif cally, connectivity changes in the dorsolateral geniculo-cortical pathway are studied using a neural mass computational model. Connectivity parameters in the model are informed by the most recent experimental data on mammalian Lateral Geniculate Nucleus (dorsal). A slowing of the mean power spectra of the model output is observed with increase in both excitatory and inhibitory parameters in the intra-thalamic and thalamocortical pathways and a decrease of sensory pathway synaptic connectivity. The biological plausibility of the results suggest potential of further model extension in AD research

Convolutional AutoEncoders for Anomaly Detection in Semiconductor Manufacturing

Semiconductor manufacturing, characterised by its complex processes, demands efficient anomaly detection (AD) systems for quality assurance. This study extends from previous work utilising unsupervised Convolutional AutoEncoders for AD in Semiconductor batch manufacturing by applying the technique to a novel dataset supplied by a local Semiconductor Manufacturer. Our method uses an approach that employs 1-dimensional Convolutional Autoencoders (1d-CAE) to improve AD performance and interpretability through the numerical decomposition of reconstruction errors. Identifying anomalies this way allows engineering resources to explain anomalies more effectively than traditional methods. We validate our approach with experiments, demonstrating its performance in accurately detecting anomalies while providing insights into the nature of these irregularities. The experiments also demonstrate the impact of training setup on detection capability, outlining an efficient framework for determining an optimal hyperparameter set-up in an industrial dataset. The proposed unsupervised learning approach with AE reconstruction error improves model explainability, which is expected to be beneficial for deployment in semiconductor manufacturing, where interpretable and trustworthy results are critical for solution adoption by process engineering teams

A Spiking Neural Network Model of the Medial Superior Olive Using Spike Timing Dependent Plasticity for Sound Localization

Sound localization can be defined as the ability to identify the position of an input sound source and is considered a powerful aspect of mammalian perception. For low frequency sounds, i.e., in the range 270 Hz–1.5 KHz, the mammalian auditory pathway achieves this by extracting the Interaural Time Difference between sound signals being received by the left and right ear. This processing is performed in a region of the brain known as the Medial Superior Olive (MSO). This paper presents a Spiking Neural Network (SNN) based model of the MSO. The network model is trained using the Spike Timing Dependent Plasticity learning rule using experimentally observed Head Related Transfer Function data in an adult domestic cat. The results presented demonstrate how the proposed SNN model is able to perform sound localization with an accuracy of 91.82% when an error tolerance of ±10° is used. For angular resolutions down to 2.5°, it will be demonstrated how software based simulations of the model incur significant computation times. The paper thus also addresses preliminary implementation on a Field Programmable Gate Array based hardware platform to accelerate system performance

Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead

Fault tolerance and adaptive capabilities are challenges for modern networks-on-chip (NoC) due to the increase in physical defects in advanced manufacturing processes. Two novel adaptive routing algorithms, namely coarse and fine-grained (FG) look-ahead algorithms, are proposed in this paper to enhance 2-D mesh/torus NoC system fault-tolerant capabilities. These strategies use fault flag codes from neighboring nodes to obtain the status or conditions of real-time traffic in an NoC region, then calculate the path weights and choose the route to forward packets. This approach enables the router to minimize congestion for the adjacent connected channels and also to bypass a path with faulty channels by looking ahead at distant neighboring router paths. The novelty of the proposed routing algorithms is the weighted path selection strategies, which make near-optimal routing decisions to maintain the NoC system performance under high fault rates. Results show that the proposed routing algorithms can achieve performance improvement compared to other state of the art works under various traffic loads and high fault rates. The routing algorithm with FG look-ahead capability achieves a higher throughput compared with the coarse-grained approach under complex fault patterns. The hardware area/power overheads of both routing approaches are relatively low which does not prohibit scalability for large-scale NoC implementations