Is Neuromorphic MNIST Neuromorphic? Analyzing the Discriminative Power of Neuromorphic Datasets in the Time Domain.
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ABSTRACT:
SUBMITTER: Iyer LR
PROVIDER: S-EPMC8027306 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionParkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices.MethodsIn this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms.ResultsSimulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage.DiscussionThis study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.
| S-EPMC10754992 | biostudies-literature
Project description:In this paper, we evaluate the uniqueness of several information-theoretic measures for graphs based on so-called information functionals and compare the results with other information indices and non-information-theoretic measures such as the well-known Balaban J index. We show that, by employing an information functional based on degree-degree associations, the resulting information index outperforms the Balaban J index tremendously. These results have been obtained by using nearly 12 million exhaustively generated, non-isomorphic and unweighted graphs. Also, we obtain deeper insights on these and other topological descriptors when exploring their uniqueness by using exhaustively generated sets of alkane trees representing connected and acyclic graphs in which the degree of a vertex is at most four.
| S-EPMC3290601 | biostudies-literature
Project description:Valley pseudospin is an electronic degree of freedom that promises highly efficient information processing applications. However, valley-polarized excitons usually have short pico-second lifetimes, which limits the room-temperature applicability of valleytronic devices. Here, we demonstrate room-temperature valley transistors that operate by generating free carrier valley polarization with a long lifetime. This is achieved by electrostatic manipulation of the non-trivial band topology of the Weyl semiconductor tellurium (Te). We observe valley-polarized diffusion lengths of more than 7 μm and fabricate valley transistors with an ON/OFF ratio of 105 at room temperature. Moreover, we demonstrate an ion insertion/extraction device structure that enables 32 non-volatile memory states with high linearity and symmetry in the Te valley transistor. With ultralow power consumption (~fW valley contribution), we enable the inferring process of artificial neural networks, exhibiting potential for applications in low-power neuromorphic computing.
| S-EPMC9755139 | biostudies-literature
Project description:Aspirin-exacerbated respiratory disease (AERD) has association with severe asthma and aspirin can cause asthma to worsen, often in the form of a severe and sudden attack. The oral aspirin challenge (OAC) is the gold standard to confirm the diagnosis of AERD but it is time consuming and produces serious complications in some cases. Therefore more efficient and practical method is needed to predict AERD patients. The aim of the present study was to identify AERD-related gene expression in peripheral blood mononuclear cells (PBMCs) and examine the diagnostic potential of these candidate gene(s) for predicting AERD. To do this, RNAs from 24 subjects with AERD and 18 subjects with aspirin-tolerant asthma (ATA) were subjected to microarray analysis of ~34,560 genes. In total, 10 genes were selected as candidate gene markers by applying p ≤ 0.001(t-test) and ≥ 8-fold change and to correct for multiple comparisons, the false discovery rate (FDR) analyses were performed. By applying multiple logistic regression analysis, among possible 1023 models (210–1), a model consisting of CNKSR3, SPTBN2, and IMPACT was selected as candidate set, because this set showed the best AUC (0.98) with 88% sensitivity and 89% specificity. For validation, mRNA levels by real-time PCR on PBMCs from two population sets in a gene-chip study and another replication sample: 20 AERD, 20 ATA, and 8 normal controls, were significantly different between groups with 100% sensitivity and 100% specificity in each of the two population sets. However, IMPACT gene didn’t differentiate between AERD and normal controls. The set of the two genes (CNKSR3 and SPTBN2) showed the best AUC (0.96) with 88% sensitivity and 94% specificity in a gene chip study sample. In addition, this set showed perfect discriminative power with AUC (1.0, 100% sensitivity and 100% specificity) in each of the two population sets: the gene-chip samples and the replication samples. It also showed perfect discrimination for AERD form NC (AUC 1.0) and ATA from NC (AUC 1.0). In conclusion, we developed the two gene markers (CNKSR3 and SPTBN2) of PBMC which differentiate between AERD and ATA with a perfect discriminative power. These gene markers may be an efficient and practical method useful for predicting AERD.
2013-04-08 | GSE45847 | GEO
Project description:Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.
| S-EPMC3458522 | biostudies-literature
Project description:Background and Purpose: Olfactory dysfunction is one of the most common non-motor symptoms in Parkinson's disease (PD) preceding the motor symptoms for years. This study aimed to evaluate different olfactory domains in PD patients in comparison with healthy controls and to explore the relationships among olfactory deficit and other clinical manifestations in patients with PD. Methods: Sniffin' Sticks test, which detects olfactory threshold, discrimination, and identification (TDI), were conducted in 500 PD patients and 115 controls. Furthermore, demographic and clinical data including motor and other non-motor symptoms were collected. Results: In the single olfactory model, the identification test showed the area under the receiver operating characteristic (ROC) curve (AUC = 0.818), followed by threshold test (AUC = 0.731) and discrimination test (AUC = 0.723). Specifically, the identification test has a similar discriminative power as the TDI score (0.818 and 0.828, respectively, p = 0.481). In the integrated olfactory model involved with other non-motor manifestations, identification test scores performed as good as the TDI score in differentiating PD patients from controls (0.916 and 0.918, respectively, p = 0.797). In PD patients, age and cognition together explained 7.5% of the variance of the threshold score, while age, cognition, and gender accounted for the 15.2% explained variance of the discrimination score, while cognition, age, the ability of daily living, and gender together interpreted 11.1% of the variance of the identification score. Conclusion: Our results indicated that the identification domain was the most practical olfactory factor in differentiating PD patients, and the combination of several different manifestations was better than a single symptom. Furthermore, the olfactory identification score may be associated with the ability of daily living.
| S-EPMC7280480 | biostudies-literature
Project description:Aspirin-exacerbated respiratory disease (AERD) has association with severe asthma and aspirin can cause asthma to worsen, often in the form of a severe and sudden attack. The oral aspirin challenge (OAC) is the gold standard to confirm the diagnosis of AERD but it is time consuming and produces serious complications in some cases. Therefore more efficient and practical method is needed to predict AERD patients. The aim of the present study was to identify AERD-related gene expression in peripheral blood mononuclear cells (PBMCs) and examine the diagnostic potential of these candidate gene(s) for predicting AERD. To do this, RNAs from 24 subjects with AERD and 18 subjects with aspirin-tolerant asthma (ATA) were subjected to microarray analysis of ~34,560 genes. In total, 10 genes were selected as candidate gene markers by applying p ≤ 0.001(t-test) and ≥ 8-fold change and to correct for multiple comparisons, the false discovery rate (FDR) analyses were performed. By applying multiple logistic regression analysis, among possible 1023 models (210–1), a model consisting of CNKSR3, SPTBN2, and IMPACT was selected as candidate set, because this set showed the best AUC (0.98) with 88% sensitivity and 89% specificity. For validation, mRNA levels by real-time PCR on PBMCs from two population sets in a gene-chip study and another replication sample: 20 AERD, 20 ATA, and 8 normal controls, were significantly different between groups with 100% sensitivity and 100% specificity in each of the two population sets. However, IMPACT gene didn’t differentiate between AERD and normal controls. The set of the two genes (CNKSR3 and SPTBN2) showed the best AUC (0.96) with 88% sensitivity and 94% specificity in a gene chip study sample. In addition, this set showed perfect discriminative power with AUC (1.0, 100% sensitivity and 100% specificity) in each of the two population sets: the gene-chip samples and the replication samples. It also showed perfect discrimination for AERD form NC (AUC 1.0) and ATA from NC (AUC 1.0). In conclusion, we developed the two gene markers (CNKSR3 and SPTBN2) of PBMC which differentiate between AERD and ATA with a perfect discriminative power. These gene markers may be an efficient and practical method useful for predicting AERD. Total 24 AERD (Aspirin-exacerbated respiratory disease) and 18 ATA(aspirin-tolerant asthma) samples were used to find bio-markers using 35K op-array.
2013-04-08 | E-GEOD-45847 | biostudies-arrayexpress
Project description:BackgroundChromatin immunoprecipitation on tiling arrays (ChIP-chip) has been employed to examine features such as protein binding and histone modifications on a genome-wide scale in a variety of cell types. Array data from the latter studies typically have a high proportion of enriched probes whose signals vary considerably (due to heterogeneity in the cell population), and this makes their normalization and downstream analysis difficult.ResultsHere we present strategies for analyzing such experiments, focusing our discussion on the analysis of Bromodeoxyruridine (BrdU) immunoprecipitation on tiling array (BrdU-IP-chip) datasets. BrdU-IP-chip experiments map large, recently replicated genomic regions and have similar characteristics to histone modification/location data. To prepare such data for downstream analysis we employ a dynamic programming algorithm that identifies a set of putative unenriched probes, which we use for both within-array and between-array normalization. We also introduce a second dynamic programming algorithm that incorporates a priori knowledge to identify and quantify positive signals in these datasets.ConclusionHighly enriched IP-chip datasets are often difficult to analyze with traditional array normalization and analysis strategies. Here we present and test a set of analytical tools for their normalization and quantification that allows for accurate identification and analysis of enriched regions.
| S-EPMC2759964 | biostudies-literature
Project description:We investigate a photonic regenerative memory based upon a neuromorphic oscillator with a delayed self-feedback (autaptic) connection. We disclose the existence of a unique temporal response characteristic of localized structures enabling an ideal support for bits in an optical buffer memory for storage and reshaping of data information. We link our experimental implementation, based upon a nanoscale nonlinear resonant tunneling diode driving a laser, to the paradigm of neuronal activity, the FitzHugh-Nagumo model with delayed feedback. This proof-of-concept photonic regenerative memory might constitute a building block for a new class of neuron-inspired photonic memories that can handle high bit-rate optical signals.
| S-EPMC4726037 | biostudies-other
Project description:PurposeTo assess the contribution of a generative adversarial network (GAN) to improve intermanufacturer reproducibility of radiomic features (RFs).Materials and methodsThe authors retrospectively developed a cycle-GAN to translate texture information from chest radiographs acquired using one manufacturer (Siemens) to chest radiographs acquired using another (Philips), producing fake chest radiographs with different textures. The authors prospectively evaluated the ability of this texture-translation cycle-GAN to reduce the intermanufacturer variability of RFs extracted from the lung parenchyma. This study assessed the cycle-GAN's ability to fool several machine learning (ML) classifiers tasked with recognizing the manufacturer on the basis of chest radiography inputs. The authors also evaluated the cycle-GAN's ability to mislead radiologists who were asked to perform the same recognition task. Finally, the authors tested whether the cycle-GAN had an impact on radiomic diagnostic accuracy for chest radiography in patients with congestive heart failure (CHF).ResultsRFs, extracted from chest radiographs after the cycle-GAN's texture translation (fake chest radiographs), showed decreased intermanufacturer RF variability. Using cycle-GAN-generated chest radiographs as inputs, ML classifiers categorized the fake chest radiographs as belonging to the target manufacturer rather than to a native one. Moreover, cycle-GAN fooled two experienced radiologists who identified fake chest radiographs as belonging to a target manufacturer class. Finally, reducing intermanufacturer RF variability with cycle-GAN improved the discriminative power of RFs for patients without CHF versus patients with CHF (from 55% to 73.5%, P < .001).ConclusionBoth ML classifiers and radiologists had difficulty recognizing the chest radiographs' manufacturer. The cycle-GAN improved RF intermanufacturer reproducibility and discriminative power for identifying patients with CHF. This deep learning approach may help counteract the sensitivity of RFs to differences in acquisition.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Alderson in this issue.
| S-EPMC8082326 | biostudies-literature