The photoplethysmographic (PPG) signal is an important source of information for estimating heart rate (HR). However, the PPG signal could be strongly contaminated by the motion artifact of the ...subjects, making HR estimation a particularly difficult problem. In this paper, we propose PARHELIA, a PARticle filter-based algorithm for HEart rate estimation using photopLethysmographIc signAls. The proposed method employs a particle filter, and utilizes the simultaneously recorded acceleration signals from a wrist-type sensor, to keep track of multiple HR candidates. This achieves quick recovery from incorrect HR estimations under the strong influence of the MA. Experimental results for a dataset of 12 subjects recorded during fast running showed that the average absolute estimation error was 1.17 beats per minute (BPM) whereas that of the best-known conventional method, JOSS, is 1.28 BPM. Furthermore, the estimation time of PARHELIA is 20 times shorter than JOSS.
The extraction of the model parameters is as important as the development of compact model itself because simulation accuracy is fully determined by the accuracy of the parameters used. This article ...proposes an efficient model-parameter extraction method for compact models of power metal-oxide semiconductor field-effect transistors ( mosfet s). The proposed method employs automatic differentiation (AD), which is extensively used for training artificial neural networks. In the proposed AD-based parameter extraction, gradient of all the model parameters is analytically calculated by forming a graph that facilitates the backward propagation of errors. Based on the calculated gradient, computationally intensive numerical differentiation is eliminated and the model parameters are efficiently optimized. Experiments are conducted to fit current and capacitance characteristics of commercially available silicon carbide mosfet using power mosfet models having 13 model parameters. Results demonstrated that the proposed method could successfully derive the model parameters 3.50× faster than a conventional numerical-differentiation method while achieving the equal accuracy.
Cortical neurons form specific circuits, but the functional structure of this microarchitecture and its relation to behaviour are poorly understood. Two-photon calcium imaging can monitor activity of ...spatially defined neuronal ensembles in the mammalian cortex. Here we applied this technique to the motor cortex of mice performing a choice behaviour. Head-fixed mice were trained to lick in response to one of two odours, and to withhold licking for the other odour. Mice routinely showed significant learning within the first behavioural session and across sessions. Microstimulation and trans-synaptic tracing identified two non-overlapping candidate tongue motor cortical areas. Inactivating either area impaired voluntary licking. Imaging in layer 2/3 showed neurons with diverse response types in both areas. Activity in approximately half of the imaged neurons distinguished trial types associated with different actions. Many neurons showed modulation coinciding with or preceding the action, consistent with their involvement in motor control. Neurons with different response types were spatially intermingled. Nearby neurons (within approximately 150 mum) showed pronounced coincident activity. These temporal correlations increased with learning within and across behavioural sessions, specifically for neuron pairs with similar response types. We propose that correlated activity in specific ensembles of functionally related neurons is a signature of learning-related circuit plasticity. Our findings reveal a fine-scale and dynamic organization of the frontal cortex that probably underlies flexible behaviour.
Rheumatoid arthritis (RA) is an inflammatory disease with joint dysfunction following cartilage degradation. The level of lysophosphatidic acid (LPA) has been reported to be augmented in human ...synovial fluid from patients with RA. However, it remains to be elucidated whether LPA participates in cartilage destruction. In the present study, we have demonstrated that the production of promatrix metalloproteinases (proMMPs)-1 and -3 was augmented along with an increase of extracellular signal-regulated kinase (ERK)1/2 phosphorylation through LPA receptor 1 (LPAR1) in human synovial fibroblasts. These results suggest that LPA transcriptionally increases MMP production by the activation of an LPAR1/ERK1/2 signal pathway in human synovial fibroblasts. Thus, LPA is likely to be a pathological candidate for cartilage degradation in RA.
Humans are able to pay selective attention to music or speech in the presence of multiple sounds. It has been reported that in the speech domain, selective attention enhances the cross-correlation ...between the envelope of speech and electroencephalogram (EEG) while also affecting the spatial modulation of the alpha band. However, when multiple music pieces are performed at the same time, it is unclear how selective attention affects neural entrainment and spatial modulation. In this paper, we hypothesized that the entrainment to the attended music differs from that to the unattended music and that spatial modulation in the alpha band occurs in conjunction with attention. We conducted experiments in which we presented musical excerpts to 15 participants, each listening to two excerpts simultaneously but paying attention to one of the two. The results showed that the cross-correlation function between the EEG signal and the envelope of the unattended melody had a more prominent peak than that of the attended melody, contrary to the findings for speech. In addition, the spatial modulation in the alpha band was found with a data-driven approach called the common spatial pattern method. Classification of the EEG signal with a support vector machine identified attended melodies and achieved an accuracy of 100% for 11 of the 15 participants. These results suggest that selective attention to music suppresses entrainment to the melody and that spatial modulation of the alpha band occurs in conjunction with attention. To the best of our knowledge, this is the first report to detect attended music consisting of several types of music notes only with EEG.
Cortical maps, consisting of orderly arrangements of functional columns, are a hallmark of the organization of the cerebral cortex. However, the microorganization of cortical maps at the level of ...single neurons is not known, mainly because of the limitations of available mapping techniques. Here, we used bulk loading of Ca(2+) indicators combined with two-photon microscopy to image the activity of multiple single neurons in layer (L) 2/3 of the mouse barrel cortex in vivo. We developed methods that reliably detect single action potentials in approximately half of the imaged neurons in L2/3. This allowed us to measure the spiking probability following whisker deflection and thus map the whisker selectivity for multiple neurons with known spatial relationships. At the level of neuronal populations, the whisker map varied smoothly across the surface of the cortex, within and between the barrels. However, the whisker selectivity of individual neurons recorded simultaneously differed greatly, even for nearest neighbors. Trial-to-trial correlations between pairs of neurons were high over distances spanning multiple cortical columns. Our data suggest that the response properties of individual neurons are shaped by highly specific subcolumnar circuits and the momentary intrinsic state of the neocortex.
The effect of negative bias temperature instability (NBTI) varies significantly according to given workloads. Finding a feasible worst case workload is difficult due to logical correlation within the ...logic circuit under consideration. In this article, we propose an NBTI-aware subset simulation (SS) framework that efficiently and accurately finds the failure probability covering various input duty cycles determined by different workloads. In addition, the proposed method is incorporated with the NBTI mitigation technique to facilitate workload-aware mitigation. Through numerical experiments using benchmark circuits, the proposed method achieves up to 36 times speedup compared to a naive Monte Carlo method. The NBTI mitigation based on SS demonstrates <inline-formula> <tex-math notation="LaTeX">1.78\times </tex-math></inline-formula> better mitigation for multiple input duty cycles compared to the conventional method.
Nearby neurons, sharing the same locations within the mouse whisker map, can have dramatically distinct response properties. To understand the significance of this diversity, we studied the ...relationship between the responses of individual neurons and their projection targets in the mouse barrel cortex. Neurons projecting to primary motor cortex (MI) or secondary somatosensory area (SII) were labeled with red fluorescent protein (RFP) using retrograde viral infection. We used in vivo two-photon Ca(2+) imaging to map the responses of RFP-positive and neighboring L2/3 neurons to whisker deflections. Neurons projecting to MI displayed larger receptive fields compared with other neurons, including those projecting to SII. Our findings support the view that intermingled neurons in primary sensory areas send specific stimulus features to different parts of the brain.
Spiking neural networks (SNNs) have garnered significant attention due to their notable energy efficiency. However, conventional SNNs rely on spike firing frequency to encode information, ...necessitating a fixed sampling time and leaving room for further optimization. This study presents a novel approach to reduce sampling time and conserve energy by extracting early prediction results from the intermediate layer of the network and integrating them with the final layer's predictions in a Bayesian fashion. Experimental evaluations conducted on image classification tasks using MNIST, CIFAR-10, and CIFAR-100 datasets demonstrate the efficacy of our proposed method when applied to VGGNets and ResNets models. Results indicate a substantial energy reduction of 38.8% in VGGNets and 48.0% in ResNets, illustrating the potential for achieving significant efficiency gains in spiking neural networks. These findings contribute to the ongoing research in enhancing the performance of SNNs, facilitating their deployment in resource-constrained environments. Our code is available on GitHub: https://github.com/hanebarla/BayesianSpikeFusion .
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