Improving circuit miniaturization and its efficiency using Rough Set Theory

High-speed, accuracy, meticulousness and quick response are notion of the vital necessities for modern digital world. An efficient electronic circuit unswervingly affects the maneuver of the whole system. Different tools are required to unravel different types of engineering tribulations. Improving the efficiency, accuracy and low power consumption in an electronic circuit is always been a bottle neck problem. So the need of circuit miniaturization is always there. It saves a lot of time and power that is wasted in switching of gates, the wiring-crises is reduced, cross-sectional area of chip is reduced, the number of transistors that can implemented in chip is multiplied many folds. Therefore to trounce with this problem we have proposed an Artificial intelligence (AI) based approach that make use of Rough Set Theory for its implementation. Theory of rough set has been proposed by Z Pawlak in the year 1982. Rough set theory is a new mathematical tool which deals with uncertainty and vagueness. Decisions can be generated using rough set theory by reducing the unwanted and superfluous data. We have condensed the number of gates without upsetting the productivity of the given circuit. This paper proposes an approach with the help of rough set theory which basically lessens the number of gates in the circuit, based on decision rules.Comment: The International Conference on Machine Intelligence Research and Advancement,ICMIRA-201

Microcontact printing approaches to pattern the attachment of endothelial cells on silicone surfaces with microgrooves

Fluid shear stress (FSS) has been employed to create two-dimensional (2D) monolayers of endothelial cells (ECs) that resemble the organization of natural vasculature. However, shape-dependent EC properties that are independent of FSS remain largely undefined. More recently, surface micropatterning has been investigated as an approach to control the morphology and orientation of ECs using synthetic substratum. Most research studies have reported the effects of microtopography on sub-confluent layers of cells, which has been the standard in research investigations to date. In this thesis, surface micropatterning is used to mimic the natural EC vasculature in confluent layers of cells.Cell alignment and elongation of bovine aortic endothelial cells (BAECs) were studied on poly(dimethylsiloxane) (PDMS) surfaces consisting microgrooves of parallel channels and ridges with depths of 100 nm, 500 nm, 1 μm, and 5 μm. The silicone surfaces were preadsorbed with 10 μg/ml fibronectin (FN), an ECM protein, to encourage cell attachment. More than 70% of the cells aligned in the 500 nm and 1 μm depth microgrooves as compared to the BAECs cultured on unpatterned substrates, which showed no preferential alignment. Further, the 1 μm depth resulted in maximum elongation (> 3.0) of BAECs using Factor E, which quantifies morphological differences between cells on these microgrooves as compared to their counterparts on smooth silicone surfaces.The effects of microtopography-induced alignment on the spatial localization of caveolae were investigated. Caveolae are microdomains of the plasma membrane that contain and regulate a variety of signaling molecules, and hence play an important role in cell function. Immunostaining protocols were employed to characterize spatial localization of the endothelial nitric oxide synthase (eNOS) and its primary regulatory protein, caveolin- 1 (Cav-1). Analysis showed that the expression levels of eNOS and Cav-1 were significantly higher on 500 nm and 1 μm depth patterned surfaces. Based on the Mander’s coefficient of colocalization, the 1 μm depth exhibited the highest percent colocalization (R=76%) of eNOS and Cav-1. These signaling molecules were observed to align within the channels of the 5 μm depth microgrooves, and similar alignment was observed for actin filaments. This indicates possible interactions between eNOS and Cav- 1 with actin filaments. While PDMS microgrooves strongly influenced cell orientation and morphology, microcontact printing of fibronectin on smooth PDMS determined that these microgroove-based changes in cells are a result of the 2D surface geometry and the three-dimensional (3D) spatial arrangement of cells. In summary, PDMS substrates with patterned microgrooves provide a method for evaluating the interplay between cell orientation and spatial localization of membrane proteins, which may be patterned using microcontact printing techniques.M.S., Biomedical Engineering -- Drexel University, 201

Silent memory engrams as the basis for retrograde amnesia

Recent studies identified neuronal ensembles and circuits that hold specific memory information (memory engrams). Memory engrams are retained under protein synthesis inhibition-induced retrograde amnesia. These engram cells can be activated by optogenetic stimulation for full-fledged recall, but not by stimulation using natural recall cues (thus, amnesia). We call this state of engrams “silent engrams” and the cells bearing them “silent engram cells.” The retention of memory information under amnesia suggests that the time-limited protein synthesis following learning is dispensable for memory storage, but may be necessary for effective memory retrieval processes. Here, we show that the full-fledged optogenetic recall persists at least 8 d after learning under protein synthesis inhibition-induced amnesia. This long-term retention of memory information correlates with equally persistent retention of functional engram cell-to-engram cell connectivity. Furthermore, inactivation of the connectivity of engram cell ensembles with its downstream counterparts, but not upstream ones, prevents optogenetic memory recall. Consistent with the previously reported lack of retention of augmented synaptic strength and reduced spine density in silent engram cells, optogenetic memory recall under amnesia is stimulation strength-dependent, with low-power stimulation eliciting only partial recall. Finally, the silent engram cells can be converted to active engram cells by overexpression of α-p-21–activated kinase 1, which increases spine density in engram cells. These results indicate that memory information is retained in a form of silent engram under protein synthesis inhibition-induced retrograde amnesia and support the hypothesis that memory is stored as the specific connectivity between engram cells.RIKEN Brain Science InstituteHoward Hughes Medical InstituteJPB Foundatio

Assessing impact of channel selection on decoding of motor and cognitive imagery from MEG data

Objective: Magnetoencephalography (MEG) based Brain-Computer Interface (BCI) involves a large number of sensors allowing better spatiotemporal resolution for assessing brain activity patterns. There have been many efforts to develop BCI using MEG with high accuracy, though an increase in the number of channels means an increase in computational complexity. However, not all sensors necessarily contribute significantly to an increase in classification accuracy, and specifically in the case of MEG-based BCI no channel selection methodology has been performed. Therefore, this study investigates the effect of channel selection on the performance of MEG-based BCI. Approach: MEG data were recorded for two sessions from 15 healthy participants performing motor imagery, cognitive imagery and a mixed imagery task pair using a unique paradigm. Performance of four state-of-the-art channel selection methods (i.e. Class-Correlation (CC), ReliefF (RF), Random Forest (RandF), and Infinite Latent Feature Selection (ILFS) were applied across six binary tasks in three different frequency bands) was evaluated in this study on two state-of-the-art features i.e. bandpower and CSP. Main results: All four methods provided a statistically significant increase in classification accuracy (CA) compared to a baseline method using all gradiometer sensors, i.e. 204 channels with band-power features from alpha (8-12Hz), beta (13-30Hz), or broadband (alpha+beta ) (8-30Hz). It is also observed that the alpha frequency band performed better than the beta and broadband frequency bands. The performance of the beta band gave the lowest CA compared with the other two bands. Channel selection improved accuracy irrespective of feature types. Moreover, all the methods reduced the number of channels significantly, from 204 to a range of 1-25, using bandpower as a feature and from 15-105 for CSP. The optimal channel number also varied not only in each session but also for each participant. Reducing the number of channels will help to decrease the computation cost and maintain numerical stability in cases of low trial numbers. Significance: The study showed significant improvement in performance of MEG-BCI with channel selection irrespective of feature type and hence can be successfully applied for BCI applications

Engram cells retain memory under retrograde amnesia

Memory consolidation is the process by which a newly formed and unstable memory transforms into a stable long-term memory. It is unknown whether the process of memory consolidation occurs exclusively through the stabilization of memory engrams. By using learning-dependent cell labeling, we identified an increase of synaptic strength and dendritic spine density specifically in consolidated memory engram cells. Although these properties are lacking in engram cells under protein synthesis inhibitor–induced amnesia, direct optogenetic activation of these cells results in memory retrieval, and this correlates with retained engram cell–specific connectivity. We propose that a specific pattern of connectivity of engram cells may be crucial for memory information storage and that strengthened synapses in these cells critically contribute to the memory retrieval process.RIKEN Brain Science InstituteHoward Hughes Medical InstituteJPB Foundatio

Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive memory decline and subsequent loss of broader cognitive functions. Memory decline in the early stages of AD is mostly limited to episodic memory, for which the hippocampus has a crucial role. However, it has been uncertain whether the observed amnesia in the early stages of AD is due to disrupted encoding and consolidation of episodic information, or an impairment in the retrieval of stored memory information. Here we show that in transgenic mouse models of early AD, direct optogenetic activation of hippocampal memory engram cells results in memory retrieval despite the fact that these mice are amnesic in long-term memory tests when natural recall cues are used, revealing a retrieval, rather than a storage impairment. Before amyloid plaque deposition, the amnesia in these mice is age-dependent, which correlates with a progressive reduction in spine density of hippocampal dentate gyrus engram cells. We show that optogenetic induction of long-term potentiation at perforant path synapses of dentate gyrus engram cells restores both spine density and long-term memory. We also demonstrate that an ablation of dentate gyrus engram cells containing restored spine density prevents the rescue of long-term memory. Thus, selective rescue of spine density in engram cells may lead to an effective strategy for treating memory loss in the early stages of AD.RIKEN Brain Science InstituteHoward Hughes Medical InstituteJPB Foundatio

A magnetoencephalography dataset for motor and cognitive imagery-based brain–computer interface

However, the performance of current MEG-BCI systems is still inadequate and one of the main reasons for this is the unavailability of open-source MEG-BCI datasets. MEG systems are expensive and hence MEG datasets are not readily available for researchers to develop effective and efficient BCI-related signal processing algorithms. In this work, we release a 306-channel MEG-BCI data recorded at 1KHz sampling frequency during four mental imagery tasks (i.e. hand imagery, feet imagery, subtraction imagery, and word generation imagery). The dataset contains two sessions of MEG recordings performed on separate days from 17 healthy participants using a typical BCI imagery paradigm. The current dataset will be the only publicly available MEG imagery BCI dataset as per our knowledge. The dataset can be used by the scientific community towards the development of a novel pattern recognition machin

Design, synthesis and anticonvulsant activity of some new 5,7-dibromoisatin semicarbazone derivatives

A series of 5,7-dibromoisatin semicarbazones have been synthesized in good yield, involving aryl urea and aryl semicarbazide formation. The structures of the synthesized compounds were confirmed on the basis of their spectral data. All the compounds were evaluated for anticonvulsant and CNS depressant activities. Anticonvulsant activity was determined after intraperitoneal (i.p.) administration to mice by maximal electroshock (MES) induced seizure method and minimal motor impairment was determined by rotarod test. A computational study was carried out for prediction of pharmacokinetic properties and making them potentially promising agents for the treatment of epilepsy. Compounds (Z)-1-(5,7-dibromo-2-oxoindolin-3-ylidene)-4-(4-chlorophenyl)semicarbazide (DH-05), (Z)-1-(5,7-dibromo-2-oxoindolin-3-ylidene)-4-(3-chloro-4-fluorophenyl)semicarbazide (DH-11) and (Z)-1-(5,7-dibromo-1-methyl-2-oxoindolin-3-ylidene)-4-(3-chloro-4-fluorophenyl)semicarbazide (DH-12) exhibited prominent anticonvulsant effect in the series with little or no neurotoxicity and little CNS depressant effect as compared to standard drug

Engrams and circuits crucial for systems consolidation of a memory

Episodic memories initially require rapid synaptic plasticity within the hippocampus for their formation and are gradually consolidated in neocortical networks for permanent storage. However, the engrams and circuits that support neocortical memory consolidation have thus far been unknown.We found that neocortical prefrontal memory engram cells, which are critical for remote contextual fear memory, were rapidly generated during initial learning through inputs from both the hippocampal-entorhinal cortex network and the basolateral amygdala. After their generation, the prefrontal engram cells, with support from hippocampal memory engram cells, became functionally mature with time. Whereas hippocampal engram cells gradually became silent with time, engram cells in the basolateral amygdala, which were necessary for fear memory, were maintained. Our data provide new insights into the functional reorganization of engrams and circuits underlying systems consolidation of memory