Neural networks have attracted much interest in the last two decades for their potential to realistically describe brain functions, but so far they have failed to provide models that can be simulated in a reasonable time on computers; rather they have been limited to toy models. Quantum computing is a possible candidate for improving the computational efficiency of neural networks. In this framework of quantum computing, the Qubit neuron model, proposed by Matsui and Nishimura, has shown a high efficiency in solving problems such as data compression. Simulations have shown that the Qubit model solves learning problems with significantly improved efficiency as compared to the classical model. In this paper, we confirm our previous results in further detail and investigate what contributes to the efficiency of our model through 4-bit and 6-bit parity check problems, which are known as basic benchmark tests. Our simulations suggest that the improved performance is due to the use of superposition of neural states and the use of probability interpretation in the observation of the output states of the model.
Electroencephalography (EEG) has long been studied as a potential diagnostic method for Alzheimer's disease (AD). The pathological progression of AD leads to cortical disconnection. These disconnections may manifest as functional connectivity alterations, measured by the degree of synchronization between different brain regions, and alterations in complex behaviors produced by the interaction among widespread brain regions. Recently, machine learning methods, such as clustering algorithms and classification methods, have been adopted to detect disease-related changes in functional connectivity and classify the features of these changes. Although complexity of EEG signals can also reflect AD-related changes, few machine learning studies have focused on the changes in complexity. Therefore, in this study, we compared the ability of EEG signals to detect characteristics of AD using different machine learning approaches one focused on functional connectivity and the other focused on signal complexity. We examined functional connectivity, estimated by phase lag index (PLI) in EEG signals in healthy older participants [healthy control (HC)] and patients with AD. We estimated signal complexity using multi-scale entropy. Utilizing a support vector machine, we compared the identification accuracy of AD based on functional connectivity at each frequency band and complexity component. Additionally, we evaluated the relationship between synchronization and complexity. The identification accuracy of functional connectivity of the alpha, beta, and gamma bands was significantly high (AUC 1.0), and the identification accuracy of complexity was sufficiently high (AUC 0.81). Moreover, the relationship between functional connectivity and complexity exhibited various temporal-scale-andregional-specific dependency in both HC participants and patients with AD. In conclusion, the combination of functional connectivity and complexity might reflect complex pathological process of AD. Applying a combination of both machine learning methods
Associative memory networks based on quaternionic Hopfield neural network are investigated in this paper. These networks are composed of quaternionic neurons, and input, output, threshold, and connection weights are represented in quaternions, which is a class of hypercomplex number systems. The energy function of the network and the Hebbian rule for embedding patterns are introduced. The stable states and their basins are explored for the networks with three neurons and four neurons. It is clarified that there exist at most 16 stable states, called multiplet components, as the degenerated stored patterns, and each of these states has its basin in the quaternionic networks.
1. Insulin resistance has been highlighted as a common causal factor for hypertension, hyperlipidaemia, diabetes mellitus and obesity, all of which are recognized to occur simultaneously, and a distinct clinical entity is defined as 'multiple risk factor syndrome'. 2. Recently, a new class of antidiabetic agents, thiazolidinediones (TZD) has been developed and has been shown to improve insulin resistance by binding and activating a nuclear receptor, peroxisome proliferator-activated receptor (PPAR) gamma. 3. cDNA of rat PPAR gamma 1 and gamma 2 were cloned and gene regulation of PPAR gamma in rat mature adipocytes was examined. Hydrogen peroxide, an oxygen radical, which is recognized to be the common intracellular signal for multiple risk factors, potently down-regulated PPAR gamma mRNA expression in rat mature adipocytes. 4. Tumour necrosis factor (TNF)-alpha, which is considered to play a role in obesity-induced non-insulin-dependent diabetes mellitus and to augment oxidative stress, also suppressed PPAR gamma expression. 5. Thiazolidinediones dose-dependently recovered TNF-alpha-induced down-regulation of PPAR gamma mRNA expression. 6. The modulation of PPAR gamma expression by TZD can be one mechanism for the improvement of insulin resistance by TZD. 7. Vascular tone and remodelling are controlled by several vasoactive autocrine/paracrine factors produced by endothelial cells in response to several vascular injury stimuli, including hypertension. The PPAR gamma gene transcript was detected in cultured endothelial cells. 8. The administration of TZD stimulated the endothelial secretion of type-C natriuretic peptide, which is one of the natriuretic peptide family and is demonstrated by us to act as a novel endothelium-derived relaxing peptide. 9. Concomitantly, TZD significantly suppressed the secretion of endothelin, a potent endothelium-derived vasoconstricting peptide. 10. Thiazolidinediones can affect vascular tone and growth by modulating the production of endothelium-derived vasoactive substances to influence occurrence and progression of hypertension and atherosclerosis.
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