A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future

Benzothiazolylphenol–Substituted Ketoester is a Useful Fluorescent Probe for Detection of the Mitochondrion in Sea Urchin Sperm

One of the ketoesters derived from benzothiazolylphenol-substituted dioxetane,benzothiazolylphenol-substituted ketoester (TPKE), demonstrates fluorescence in a 0.1 MNaOH 1). In this study, the fluorescent staining of a living cell with TPKE was demonstratedby fluorescence microscopy. When sperm from two species of sea urchins—Pseudocentrotusdepressus and Anthocidaris crassispina—were used as biological materials, TPKE showed afluorescent signal in the midpiece that was composed of a single mitochondrion. The ratioof fluorescent signal intensity to background noise (S/N) was high in the sperm stained with1.0–5.0 μg/ml TPKE in normal artificial seawater (pH 8.0). The S/N ratio decreased inacidic seawater (pH 6.0); acidic conditions repress respiratory activity in sea urchin sperm.Moreover, in the presence of the respiratory chain inhibitor antimycin A and the uncouplercarbonyl cyanide p--trifluoromethoxyphenyl-hydrazone, the sperm showed faint or nofluorescence in normal artificial seawater (pH 8.0). Sea urchin sperm stained with TPKEafter fixation showed faint or no fluorescence. These results suggest that TPKE is apotential fluorescent probe of living sea urchin sperm mitochondria with high respiratoryactivities

A lightweight data-driven spiking neuronal network model of Drosophila olfactory nervous system with dedicated hardware support

Data-driven spiking neuronal network (SNN) models enable in-silico analysis of the nervous system at the cellular and synaptic level. Therefore, they are a key tool for elucidating the information processing principles of the brain. While extensive research has focused on developing data-driven SNN models for mammalian brains, their complexity poses challenges in achieving precision. Network topology often relies on statistical inference, and the functions of specific brain regions and supporting neuronal activities remain unclear. Additionally, these models demand huge computing facilities and their simulation speed is considerably slower than real-time. Here, we propose a lightweight data-driven SNN model that strikes a balance between simplicity and reproducibility. The model is built using a qualitative modeling approach that can reproduce key dynamics of neuronal activity. We target the Drosophila olfactory nervous system, extracting its network topology from connectome data. The model was successfully implemented on a small entry-level field-programmable gate array and simulated the activity of a network in real-time. In addition, the model reproduced olfactory associative learning, the primary function of the olfactory system, and characteristic spiking activities of different neuron types. In sum, this paper propose a method for building data-driven SNN models from biological data. Our approach reproduces the function and neuronal activities of the nervous system and is lightweight, acceleratable with dedicated hardware, making it scalable to large-scale networks. Therefore, our approach is expected to play an important role in elucidating the brain's information processing at the cellular and synaptic level through an analysis-by-construction approach. In addition, it may be applicable to edge artificial intelligence systems in the future

Examination of the Most Suitable Preparation Method for Pollen Observation by Scanning Electron Microscope

To examine the most suitable method to prepare pollen for scanning electronmicroscope observation, several fixation methods and dehydration (or drying) methods wereapplied to Arabidopsis and lily pollen obtained from flowers before and after flowering.Light and confocal laser microscope observations revealed that, in both plants, pollenobtained before flowering was swollen and wet, while that obtained after flowering wasshrunken and dry. For scanning electron microscopy, pollen was unfixed or fixed with 50%(eventually 100%) acetone, FAA (5% formalin with 50% ethanol and 5% acetic acid) or 6%glutaraldehyde solutions. It was then dried naturally in air or artificially by either criticalpoint-dry or freeze-dry machines, respectively. The results indicated that, for scanningelectron microscope observation of dry pollen, the most suitable treatment is natural dryingwithout fixation. On the other hand, to observe wet pollen, the combination of FAA orglutaraldehyde fixation with artificial drying using either machines is preferable, andfixation with acetone is unsuitable.テクニカルノー

Separated Transcriptomes of Male Gametophyte and Tapetum in Rice: Validity of a Laser Microdissection (LM) Microarray

In flowering plants, the male gametophyte, the pollen, develops in the anther. Complex patterns of gene expression in both the gametophytic and sporophytic tissues of the anther regulate this process. The gene expression profiles of the microspore/pollen and the sporophytic tapetum are of particular interest. In this study, a microarray technique combined with laser microdissection (44K LM-microarray) was developed and used to characterize separately the transcriptomes of the microspore/pollen and tapetum in rice. Expression profiles of 11 known tapetum specific-genes were consistent with previous reports. Based on their spatial and temporal expression patterns, 140 genes which had been previously defined as anther specific were further classified as male gametophyte specific (71 genes, 51%), tapetum-specific (seven genes, 5%) or expressed in both male gametophyte and tapetum (62 genes, 44%). These results indicate that the 44K LM-microarray is a reliable tool to analyze the gene expression profiles of two important cell types in the anther, the microspore/pollen and tapetum

Analysis of Chromosome Dynamics during Meiosis I of Arabidopsis thaliana Pollen Mother Cells by Fluorescent In Situ Hybridization(FISH)

Since insertion mutagenesis methods, which enabled us to identify the mutagenized genes routinely, were developed for plants, Arabidopsis thaliana has been playing a central role in plant meiosis research. Though several techniques to analyze meiotic chromosome behavior have been introduced into Arabidopsis research since Ross et al. reported the method to observe male meiotic chromosomes of this plant through light microscope in 1996 (Chromosome Res. 4-507-516), intimate analysis of the chromosome behavior has not been accomplished. Taking advantage of the recent development of new nucleotides labeled with fluorescent dyes, we investigated chromosome behavior during male meiosis by multicolor FISH. Telomeres found around nucleoli in premeiotic interphase cells dispersed after entering meiosis, then clustered in a bouquet-like configuration. Statistically, telomeres of homologous chromosomes paired earlier than centromeres, but when respective chromosomes were examined, the telomeres were not always quick to pair. At early prophase I, possibly at around the zygotene stage, the signals from telomeres reduced to less than ten. This reduction suggests that the paired telomeres of homologous chromosomes temporally associate with other telomeres to look for their real partners. When homologous chromosomes separated at anaphase I, telomeres were always last to segregate. This suggested that there was unknown interaction between the telomeres of homologs, connecting them until anaphase I started