Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (VLSI) circuits. In this article, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultrahigh energy efficiency can be achieved. We also propose several techniques to improve the performance on recognition tasks and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST and Fashion-MNIST datasets. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.

Spiking neural networks (SNNs) are expected to be energy efficient when implemented on dedicated hardware. However, fully exploiting SNN’s characteristics such as event-driven communications challenges on circuit designers and manufacturers. In this paper, inspired by the recent success of an artificial neural network (ANN) based system, known as charge-domain computing (CDC), we propose a novel framework for SNNs called “RC-Spike.” As CDC, RC-Spike uses a two-phase system: input spikes are received in the accumulation phase, and a neuron produces a spike in the spike generation phase. In RC-Spike, synaptic currents are accumulated with resistively coupled synapses, with which circuit implementation can be simplified compared with CDC circuits. Because of this resistive coupling effect, a neuron in RC-Spike does not compute an exact dot product. However, RC-Spike can be successfully trained in the framework of SNNs, and we show that the learning performance of RC-Spike is as high as ANNs on the MNIST and Fashion-MNIST datasets.

Dense measurements of visual-field, which is necessary to detect glaucoma, is known as very costly and labor intensive. Recently, measurement of retinal-thickness can be less costly than measurement of visual-field. Thus, it is sincerely desired that the retinal-thickness could be transformed into visual-sensitivity data somehow. In this paper, we propose two novel methods to estimate the sensitivity of the visual-field with SITA-Standard mode 10-2 resolution using retinal-thickness data measured with optical coherence tomography (OCT). The first method called Affine-Structured Non-negative Matrix Factorization (ASNMF) which is able to cope with both the estimation of visual-field and the discovery of deep glaucoma knowledge. While, the second is based on Convolutional Neural Networks (CNNs) which demonstrates very high estimation performance. These methods are kinds of multi-view learning methods because they utilize visual-field and retinal thickness data simultaneously. We experimentally tested the performance of our methods from several perspectives. We found that ASNMF worked better for relatively small data size while CNNs did for relatively large data size. In addition, some clinical knowledge are discovered via ASNMF. To the best of our knowledge, this is the first paper to address the dense estimation of the visual-field based on the retinal-thickness data.

In this paper, we propose a novel change detection method for temporal networks. In usual change detection algorithms, change scores are generated from an observed time series. When this change score reaches a threshold, an alert is raised to declare the change. Our method aggregates these change scores and alerts based on network centralities. Many types of changes in a network can be discovered from changes to the network structure. Thus, nodes and links should be monitored in order to recognize changes. However, it is difficult to focus on the appropriate nodes and links when there is little information regarding the dataset. Network centrality such as PageRank measures the importance of nodes in a network based on certain criteria. Therefore, it is natural to apply network centralities in order to improve the accuracy of change detection methods. Our analysis reveals how and when network centrality works well in terms of change detection. Based on this understanding, we propose an aggregating algorithm that emphasizes the appropriate network centralities. Our evaluation of the proposed aggregation algorithm showed highly accurate predictions for an artificial dataset and two real datasets. Our method contributes to extending the field of change detection in temporal networks by utilizing network centralities.

In this paper, we propose a method to cluster the spacial patterns of the visual field in glaucoma patients to analyze the progression patterns of glaucoma. The degree of progression in the visual field of glaucoma patients can be divided into several regions by straight line boundaries, we call this specific structure Direct Product Structure in this paper. Since we can observe the direct product structure in the visual fields, we propose a bottom-up hierarchical clustering method to embed this structure into the clustering structure. In our method, according to the minimum description length (MDL) principle, we select the best cluster division so that the total code length required for encoding the data as well as the clustering structure is minimum. We can thereby select the clusters that are robust to the noise in the position of the direct product structure for clustering. We demonstrate the effectiveness of our method using an artificial dataset and a real glaucoma dataset. Our proposed method performed better than existing methods for both datasets. For the real glaucoma dataset in particular, our method discovered the characteristic progressive patterns of glaucoma as specific features of clusters. These patterns agree with clinical knowledge. Furthermore, we show that our clusters can be applied to improve the accuracy of predicting glaucoma progression. Thus, our clusters contain rich information of glaucoma, and hence can contribute to further development in glaucoma research.

We consider the prediction of glaucomatous visual field loss based on patient datasets. It is critically important to predict how rapidly the disease is progressing in an individual patient. However, the number of measurements for each patient is so small that a reliable predictor cannot be constructed from the data of a single patient alone. In this paper, we propose a novel multi-task learning approach to this issue. Patient data consist of three features: patient ID, 74-dimensional visual loss values, and inspection time. We reduce the prediction problem into one of matrix completion for these features. Specifically, by assuming heterogeneity in the three features, we introduce similarity measures that reflect the unique statistical nature of the respective features to solve a specific type of matrix decomposition problem. For example, we employ Gaussian kernels as a similarity measure for visual field loss and a linear regression-type relation for the time feature. We empirically demonstrate that our proposed method works significantly better than the existing methods.