Reverberating activity in a neural network with distributed signal transmission delays

It is known that an identical delay in all transmission lines can destabilize macroscopic stationarity of a neural network, causing oscillation or chaos. We analyze the collective dynamics of a network whose intra-transmission delays are distributed in time. Here, a neuron is modeled as a discrete-time threshold element that responds in an all-or-nothing manner to a linear sum of signals that arrive after delays assigned to individual transmission lines. Even though transmission delays are distributed in time, a whole network exhibits a single collective oscillation with a period close to the average transmission delay. The collective oscillation can not only be a simple alternation of the consecutive firing and resting, but also nontrivially sequenced series of firing and resting, reverberating in a certain period of time. Moreover, the system dynamics can be made quasiperiodic or chaotic by changing the distribution of delays.Comment: 8pages, 9figure

本震直後からの余震活動のリアルタイム短期予測と中期予測

要旨あり地震予測と統計モデル研究詳

Training Generative Question-Answering on Synthetic Data Obtained from an Instruct-tuned Model

This paper presents a simple and cost-effective method for synthesizing data to train question-answering systems. For training, fine-tuning GPT models is a common practice in resource-rich languages like English, however, it becomes challenging for non-English languages due to the scarcity of sufficient question-answer (QA) pairs. Existing approaches use question and answer generators trained on human-authored QA pairs, which involves substantial human expenses. In contrast, we use an instruct-tuned model to generate QA pairs in a zero-shot or few-shot manner. We conduct experiments to compare various strategies for obtaining QA pairs from the instruct-tuned model. The results demonstrate that a model trained on our proposed synthetic data achieves comparable performance to a model trained on manually curated datasets, without incurring human costs.Comment: PACLIC 2023 short paper, 4 pages (6 pages including references), 4 figure

Immunohistochemical Demonstration of Membrane-bound Prostaglandin E2 Synthase-1 in Papillary Thyroid Carcinoma

Microsomal prostaglandin E2 synthase-1 (mPGES-1) is an inducible enzyme that catalyzes the conversion of prostaglandin (PG) H2 to PGE2 in downstream of cyclooxygenase-2 (COX-2). Recent studies have obtained in vitro evidence that PGE2 participates in carcinogenesis, angiogenesis, and induction of matrix metalloproteinase-9 (MMP-9), which plays a crucial role in cancer invasion. However, implications for mPGES-1 in thyroid carcinomas remain to be determined. To address this issue, we performed an immunohistochemical analysis for mPGES-1, COX-2 and MMP-9 in 20 papillary thyroid carcinoma (PTC) patients. mPGES-1 immunoreactivity was localized in the cytoplasm of carcinoma cells in 19 cases, with an intensity that tended to be distinct at the interface between the tumor and the surrounding non-neoplastic tissue. Staining was more intense in regions with papillary arrangement, while it was less intense in regions with trabecular or solid arrangement. In many cases, immunohistochemical localization of COX-2 and MMP-9 resemble that of mPGES-1. Taken together, our results suggest the involvement of mPGES-1 in proliferation and differentiation of PTC as well as local invasion of PTC

Deciphering Elapsed Time and Predicting Action Timing from Neuronal Population Signals

The proper timing of actions is necessary for the survival of animals, whether in hunting prey or escaping predators. Researchers in the field of neuroscience have begun to explore neuronal signals correlated to behavioral interval timing. Here, we attempt to decode the lapse of time from neuronal population signals recorded from the frontal cortex of monkeys performing a multiple-interval timing task. We designed a Bayesian algorithm that deciphers temporal information hidden in noisy signals dispersed within the activity of individual neurons recorded from monkeys trained to determine the passage of time before initiating an action. With this decoder, we succeeded in estimating the elapsed time with a precision of approximately 1 s throughout the relevant behavioral period from firing rates of 25 neurons in the pre-supplementary motor area. Further, an extended algorithm makes it possible to determine the total length of the time-interval required to wait in each trial. This enables observers to predict the moment at which the subject will take action from the neuronal activity in the brain. A separate population analysis reveals that the neuronal ensemble represents the lapse of time in a manner scaled relative to the scheduled interval, rather than representing it as the real physical time

Can distributed delays perfectly stabilize dynamical networks?

Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integro-differential equation that describes the collective dynamics of a neural network with distributed signal delays. With the gamma distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.Comment: 4pages 5figure

Observation of micropores in hard-carbon using Xe-129 NMR porosimetry

The existence of micropores and the change of surface structure in pitch-based hard-carbon in xenon atmosphere were demonstrated using Xe-129 NMR. For high-pressure (4.0 MPa) Xe-129 NMR measurements, the hard-carbon samples in Xe gas showed three peaks at 27, 34 and 210 ppm. The last was attributed to the xenon in micropores (<1 nm) in hard-carbon particles. The NMR spectrum of a sample evacuated at 773 K and exposed to 0.1 MPa Xe gas at 773 K for 24 h showed two peaks at 29 and 128 ppm, which were attributed, respectively, to the xenon atoms adsorbed in the large pores (probably mesopores) and micropores of hard-carbon. With increasing annealing time in Xe gas at 773 K, both peaks shifted and merged into one peak at 50 ppm. The diffusion of adsorbed xenon atoms is very slow, probably because the transfer of molecules or atoms among micropores in hard-carbon does not occur readily. Many micropores are isolated from the outer surface. For that reason, xenon atoms are thought to be adsorbed only by micropores near the surface, which are easily accessible from the surrounding space.</p