Unsupervised Learning with Self-Organizing Spiking Neural Networks

We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks

Roadmap on printable electronic materials for next-generation sensors

The dissemination of sensors is key to realizing a sustainable, ‘intelligent’ world, where everyday objects and environments are equipped with sensing capabilities to advance the sustainability and quality of our lives—e.g., via smart homes, smart cities, smart healthcare, smart logistics, Industry 4.0, and precision agriculture. The realization of the full potential of these applications critically depends on the availability of easy-to-make, low-cost sensor technologies. Sensors based on printable electronic materials offer the ideal platform: they can be fabricated through simple methods (e.g., printing and coating) and are compatible with high-throughput roll-to-roll processing. Moreover, printable electronic materials often allow the fabrication of sensors on flexible/stretchable/biodegradable substrates, thereby enabling the deployment of sensors in unconventional settings. Fulfilling the promise of printable electronic materials for sensing will require materials and device innovations to enhance their ability to transduce external stimuli—light, ionizing radiation, pressure, strain, force, temperature, gas, vapours, humidity, and other chemical and biological analytes. This Roadmap brings together the viewpoints of experts in various printable sensing materials—and devices thereof—to provide insights into the status and outlook of the field. Alongside recent materials and device innovations, the roadmap discusses the key outstanding challenges pertaining to each printable sensing technology. Finally, the Roadmap points to promising directions to overcome these challenges and thus enable ubiquitous sensing for a sustainable, ‘intelligent’ world

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Not AvailableCastor is a prime industrial crop belonging to a monotypic genus and its genetic improvement depends on creating desired variability in the primary gene pool. This study reports the development of tetraploid castor plants through colchicine treatment. Seeds of three castor genotypes were soaked in aqueous solutions of colchicine with variable concentrations, and the LD50 value was determined. Of 1010 treated field-raised plants, three were identified as potential polyploids based on increases in a guard cell size and reductions in the number of stomata. The putative polyploid plants were selfed and the progeny were subjected to meiotic analysis. All the progeny were found to be tetraploid. The pairing of chromosomes was abnormal with univalent to octavalent configurations during meiosis-I, but the later parts of meiosis were normal. Seasonal variations in pollen fertility indicated the possible role of temperature-sensitive male sterility in causing the sterility in tetraploid plants. The tetraploid plants were phenotypically comparable with their diploid counterparts, but produced substantially bigger seeds. Thus, these tetraploid plants are valuable resources for basic and applied research in castor.ICA