Growing demand to deepen understanding of the human brain has accelerated efforts to identify the structure of biological neural networks from neuronal activities. This paper presents a fully digital, tile-able neural array integrated circuit(IC) that, to our knowledge, is the first hardware platform for network reconstruction—inferring synaptic connectivity directly from spike-train data generated by a biological (ground-truth) network. Designed with the overarching goal of emulation of biological networks, the architecture employs repeatable digital neuron-module tiles to ensure the scalability, flexibility and verifiability. Two chip-level run-time interfaces are integrated: a writable spike-forcing path for injecting biological spike pulses into selected IC neurons for synchronized co-firing, and a dedicated monitoring path for streaming spike events, synaptic weights, and membrane potentials. Scalability is further enabled by single-timer Δt capture and a piecewise-linear STDP Δw generator shared per neuron, avoiding complex LUTs/multipliers while preserving biological plausibility. The platform is realized in silicon and tested with an FPGA-based setup. Using only spike activity, the system reconstructs synaptic connectivity with high fidelity across diverse ground-truth networks ranging from simple 2-layer topologies to biologically derived C.elegans head network, as well as networks with bimodal and trimodal weight distributions. Accuracy was comprehensively quantified from multiple perspectives for both connectivity and spike-train similarity, confirming faithful replication. These results demonstrate that the proposed platform can recover the synaptic structure from spikes alone and provide a practical tool for predicting network learning responses under varied stimuli, advancing biological neural network research and real-time neuromorphic experimentation.

One of the most challenging aspects of operating a large-scale web search engine is to accurately evaluate and monitor the search engine's result quality regardless of search types. From a business perspective, in the face of such challenges, it is important to establish a universal search quality metric that can be easily understood by the entire organisation. In this paper, we introduce a model-based quality metric using Explainable Boosting Machine as the classifier and online user behaviour signals as features to predict search quality. The proposed metric takes into account a variety of search types and has good interpretability. To examine the performance of the metric, we constructed a large dataset of user behaviour on search engine results pages (SERPs) with SERP quality ratings from professional annotators. We compared the performance of the model in our metric to those of other black-box machine learning models on the dataset. We also share a few experiences within our company for the org-wide adoption of this metric relevant to metric design.

Although the high performance of the convolutional neural networks (CNNs) for brain-computer interface (BCI) tasks based on raw electroencephalography (EEG) signals, the explanation of the prediction result remains challenging owing to their complex structure and numerous parameters. We propose a novel framework for explaining CNNs for EEG-based BCI tasks by using the perturbation-based influence scores. The method supports the interpretation of CNN classification for EEG signals at both the example-level and the feature-level. The experiments on the BCIC III-IVa dataset demonstrate that the proposed method is effective for not only the interpretation of the predictive models, but also for the improvement of the classification accuracy.

Query relaxation is one of the crucial components for approximate query answering. Query relaxation has extensively been investigated in terms of categorical data; few studies, however, have been effectively established for both numerical and categorical data. In this article, we develop a query relaxation method by exploiting hierarchical quantified data abstraction, and a novel method is proposed to quantify the semantic distances between the categorical data so that the query conditions for categorical data are effectively relaxed. We additionally introduce query relaxation algorithms to modify the approximate queries into ordinary queries, which are followed by a series of examples to represent the modification process. Our method outperformed the conventional approaches for the various combinations of complex queries with respect to the cost model and the number of child nodes.