Self-organizing homotopy network

In this paper, we propose a conceptual learning algorithm called the 'self-organizing homotopy (SOH)' together with an implementation thereof. As in the case of the SOM, our SOH organizes a homotopy in a self-organizing manner by giving a set of data episodes. Thus it is an extension of the SOM, moving from a 'map' to a 'homotopy'. From a geometrical viewpoint, the SOH represents a set of (i.e. multiple) data distributions by a fiber bundle, whereas the SOM represents a single data distribution by a manifold. One of the solutions to the SOH is SOM², in which every reference vector unit of the conventional SOM is itself replaced by an SOM. Consequently SOM² has the ability to represent a fiber bundle, i.e. a product manifold, by using a product space of SOM x SOM. It is expected that SOHs will play important roles in the fields of pattern recognition, adaptive functions, context understanding, and others, in which nonlinear manifolds and the homotopy play crucial roles

Modular Network SOM and Self-Organizing Homotopy Network as a Foundation for Brain-like Intelligence

In this paper, two generalizations of the SOM are introduced. The first of these extends the SOM to deal with more generalized classes of objects besides the vector dataset. This generalization is realized by employing modular networks instead of reference vector units and is thus called a modular network SOM (mnSOM). The second generalization involves the extension of the SOM from "map" to "homotopy", allowing the SOM to deal with a set of data distributions rather than a set of data vectors. The resulting architecture is called SOM^n, where each reference unit represents a tensor of rank n. These generalizations are expected to provide good platforms on which to build brain-like intelligence

Task Segmentation in a Mobile Robot by mnSOM and Hierarchical Clustering

Our previous studies assigned labels to mnSOM modules based on the assumption that winner modules corresponding to subsequences in the same class share the same label. We propose segmentation using hierarchical clustering based on the resulting mnSOM. Since it does not need the above unrealistic assumption, it gains practical importance at the sacrifice of the deterioration of the segmentation performance by 1.2%. We compare the performance of task segmentation for two kinds of module architecture in mnSOM. The result is that module architecture with sensory-motor signals as target outputs has superior performance to that with only sensory signals as target outputs

Asymmetric Temporal Properties in the Receptive Field of Retinal Transient Amacrine Cells

The speed of signal conduction is a factor determining the temporal properties of individual neurons and neuronal networks. We observed very different conduction velocities within the receptive field of fast-type On-Off transient amacrine cells in carp retina cells, which are tightly coupled to each other via gap junctions. The fastest speeds were found in the dorsal area of the receptive fields, on average five times faster than those detected within the ventral area. The asymmetry was similar in the On- and Off-part of the responses, thus being independent of the pathway, pointing to the existence of a functional mechanism within the recorded cells themselves. Nonetheless, the spatial decay of the graded-voltage photoresponse within the receptive field was found to be symmetrical, with the amplitude center of the receptive field being displaced to the faster side from the minimum-latency location. A sample of the orientation of varicosity-laden polyaxons in neurobiotin-injected cells supported the model, revealing that ∼75% of these processes were directed dorsally from the origin cells. Based on these results, we modeled the velocity asymmetry and the displacement of amplitude center by adding a contribution of an asymmetric polyaxonal inhibition to the network. Due to the asymmetry in the conduction velocity, the time delay of a light response is proposed to depend on the origin of the photostimulus movement, a potentially important mechanism underlying direction selectivity within the inner retina

Simultaneous Visualization of Documents, Words and Topics by Tensor Self-Organizing Map and Non-negative Matrix Factorization

The purpose of this work is to develop a simultaneous visualization method of documents, words, and topics. The task of the proposed method is to map a set of documents to a pair of low-dimensional latent spaces corresponding to documents and words, by which the relations between them are visualized. In addition, the method also decomposes the mapping as the sum of topics, so that the topic distributions are visualized on the latent spaces. To achieve the task, we combined the tensor self-organizing map and the non-negative matrix factorization. We applied the method to NeurIPS data set, and the result shows that the method enables us to understand the tripartite relation between document, words and topics easily.2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems (SCIS-ISIS), December 5-8, 2020, Hachijo island, Tokyo, Japan（オンライン開催に変更

Task Segmentation in a Mobile Robot by mnSOM and Hierarchical Clustering

Our previous studies assigned labels to mnSOM modules based on the assumption that winner modules corresponding to subsequences in the same class share the same label. We propose segmentation using hierarchical clustering based on the resulting mnSOM. Since it does not need the above unrealistic assumption, it gains practical importance at the sacrifice of the deterioration of the segmentation performance by 1.2%. We compare the performance of task segmentation for two kinds of module architecture in mnSOM. The result is that module architecture with sensory-motor signals as target outputs has superior performance to that with only sensory signals as target outputs

Sacubitril/valsartan attenuates atrial conduction disturbance and electrophysiological heterogeneity with ameliorating fibrosis in mice

BackgroundSacubitril/valsartan (SacVal) has been shown to improve the prognosis of heart failure; however, whether SacVal reduces the occurrence of atrial fibrillation (AF) in heart failure has not yet been elucidated. In this study, we aimed to determine whether SacVal is effective in reducing the occurrence of AF in heart failure and identify the underlying mechanism of its electrophysiological effect in mice.MethodsAdult male mice underwent transverse aortic constriction, followed by SacVal, valsartan, or vehicle treatment for two weeks. Electrophysiological study (EPS) and optical mapping were performed to assess the susceptibility to AF and the atrial conduction properties, and fibrosis was investigated using heart tissue and isolated cardiac fibroblasts (CFs).ResultsEPS analysis revealed that AF was significantly less inducible in SacVal-treated mice than in vehicle-treated mice. Optical mapping of the atrium showed that SacVal-treated and valsartan-treated mice restored the prolonged action potential duration (APD); however, only SacVal-treated mice showed the restoration of decreased conduction velocity (CV) compared to vehicle-treated mice. In addition, the electrophysiological distribution analysis demonstrated that heterogeneous electrophysiological properties were rate-dependent and increased heterogeneity was closely related to the susceptibility to AF. SacVal attenuated the increased heterogeneity of CV at short pacing cycle length in atria, whereas Val could not. Histological and molecular evaluation showed that SacVal exerted the anti-fibrotic effect on the atria. An in vitro study of CFs treated with natriuretic peptides and LBQ657, the metabolite and active form of sacubitril, revealed that C-type natriuretic peptide (CNP) combined with LBQ657 had an additional anti-fibrotic effect on CFs.ConclusionsOur results demonstrated that SacVal can improve the conduction disturbance and heterogeneity through the attenuation of fibrosis in murine atria and reduce the susceptibility of AF in heart failure with pressure overload, which might be attributed to the enhanced function of CNP

Multilevel–Multigroup Analysis Using a Hierarchical Tensor SOM Network

This paper describes a method of multilevel–multigroup analysis based on a nonlinear multiway dimensionality reduction. To analyze a set of groups in terms of the probabilistic distribution of their constituent member data, the proposed method uses a hierarchical pair of tensor self-organizing maps (TSOMs), one for the member analysis and the other for the group analysis. This architecture enables more flexible analysis than ordinary parametric multilevel analysis, as it retains a high level of translatability supported by strong visualization. Furthermore, this architecture provides a consistent and seamless computation method for multilevel–multigroup analysis by integrating two different levels into a hierarchical tensor SOM network. The proposed method is applied to a dataset of football teams in a university league, and successfully visualizes the types of players that constitute each team as well as the differences or similarities between the teams.23rd International Conference on Neural Information Processing, ICONIP 2016, October 16–21, 2016, Kyoto, Japa

The potential role of Arhgef33 RhoGEF in foveal development in the zebra finch retina

The fovea is a pit formed in the center of the retina that enables high-acuity vision in certain vertebrate species. While formation of the fovea fascinates many researchers, the molecular mechanisms underlying foveal development are poorly understood. In the current study, we histologically investigated foveal development in zebra finch (Taeniopygia guttata) and found that foveal pit formation begins just before post-hatch day 14 (P14). We next performed RNA-seq analysis to compare gene expression profiles between the central (foveal and parafoveal) and peripheral retina in zebra finch at P14. We found that the Arhgef33 expression is enriched in the middle layer of the inner nuclear layer at the parafovea, suggesting that Arhgef33 is dominantly expressed in Müller glial cells in the developing parafovea. We then performed a pull-down assay using Rhotekin-RBD and observed GEF activity of Arhgef33 against RhoA. We found that overexpression of Arhgef33 in HEK293 cells induces cell contraction and that Arhgef33 expression inhibits neurite extension in Neuro 2A cells, which is partially recovered by a Rho-kinase (ROCK) inhibitor. Taken together, we used zebra finch as a model animal to investigate foveal development and identified Arhgef33 as a candidate protein possibly involved in foveal development through modulating RhoA activity