Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding

The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training schemes, the error backpropagation method that directly uses the firing time of neurons has attracted considerable attention because it can realize ideal temporal coding. This method uses time-to-first spike (TTFS) coding, in which each neuron fires at most once, and this restriction on the number of firings enables information to be processed at a very low firing frequency. This low firing frequency increases the energy efficiency of information processing in SNNs, which is important not only because of its similarity with information processing in the brain, but also from an engineering point of view. However, only an upper limit has been provided for TTFS-coded SNNs, and the information-processing capability of SNNs at lower firing frequencies has not been fully investigated. In this paper, we propose two spike timing-based sparse-firing (SSR) regularization methods to further reduce the firing frequency of TTFS-coded SNNs. The first is the membrane potential-aware SSR (M-SSR) method, which has been derived as an extreme form of the loss function of the membrane potential value. The second is the firing condition-aware SSR (F-SSR) method, which is a regularization function obtained from the firing conditions. Both methods are characterized by the fact that they only require information about the firing timing and associated weights. The effects of these regularization methods were investigated on the MNIST, Fashion-MNIST, and CIFAR-10 datasets using multilayer perceptron networks and convolutional neural network structures

Diverse coordinate frames on sensorimotor areas in visuomotor transformation

The visuomotor transformation during a goal-directed movement may involve a coordinate transformation from visual ‘extrinsic’ to muscle-like ‘intrinsic’ coordinate frames, which might be processed via a multilayer network architecture composed of neural basis functions. This theory suggests that the postural change during a goal-directed movement task alters activity patterns of the neurons in the intermediate layer of the visuomotor transformation that recieves both visual and proprioceptive inputs, and thus influence the multi-voxel pattern of the blood oxygenation level dependent signal. Using a recently developed multi-voxel pattern decoding method, we found extrinsic, intrinsic and intermediate coordinate frames along the visuomotor cortical pathways during a visuomotor control task. The presented results support the hypothesis that, in human, the extrinsic coordinate frame was transformed to the muscle-like frame over the dorsal pathway from the posterior parietal cortex and the dorsal premotor cortex to the primary motor cortex

OsYSL16 plays a role in the allocation of iron

Graminaceous plants acquire iron by secreting mugineic acid family phytosiderophores into the rhizosphere and taking up complexes of iron and phytosiderophores through YSL (yellow stripe 1-like) transporters. Rice OsYSL15 is a transporter of the iron(III)-2′-deoxymugineic acid complex. OsYSL16 has 85 % similarity to both OsYSL15 and the iron(II)-nicotianamine transporter OsYSL2. In the present study, we show that OsYSL16 functionally complemented a yeast mutant defective in iron uptake when grown on medium containing iron(III)-deoxymugineic acid, but not when grown on medium containing iron(II)-nicotianamine. OsYSL16-knockdown seedlings were smaller than wild-type seedlings when only iron(III)chloride was supplied as an iron source. The iron concentration in shoots of OsYSL16-knockdown plants was similar to that of the wild type; however, they showed more severe chlorosis than wild-type plants under iron-deficient conditions. Furthermore, OsYSL16-knockdown plants accumulated more iron in the vascular bundles of the leaves. Expression of the OsYSL16 promoter fused to the β-glucuronidase gene showed that OsYSL16 is expressed in the root epidermis and vascular bundles of whole plants. The expression was typically observed around the xylem. In the vascular bundles of unelongated nodes, it was detected in the xylem of old leaves and the phloem of new leaves. Graminaceous plants translocate iron from the roots to old leaves mainly via the xylem and to new leaves mainly via the phloem. Our results suggest that OsYSL16 plays a role in the allocation of iron(III)-deoxymugineic acid via the vascular bundles. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11103-012-9930-1) contains supplementary material, which is available to authorized users

Extreme Suppression of Lateral Floret Development by a Single Amino Acid Change in the VRS1 Transcription Factor

Increasing grain yield is an endless challenge for cereal crop breeding. In barley (Hordeum vulgare), grain number is controlled mainly by Six-rowed spike 1 (Vrs1), which encodes a homeodomain leucine zipper class I transcription factor. However, little is known about the genetic basis of grain size. Here, we show that extreme suppression of lateral florets contributes to enlarged grains in deficiens barley. Through a combination of fine-mapping and resequencing of deficiens mutants, we have identified that a single amino acid substitution at a putative phosphorylation site in VRS1 is responsible for the deficiens phenotype. deficiens mutant alleles confer an increase in grain size, a reduction in plant height, and a significant increase in thousand grain weight in contemporary cultivated germplasm. Haplotype analysis revealed that barley carrying the deficiens allele (Vrs1.t1) originated from two-rowed types carrying the Vrs1.b2 allele, predominantly found in germplasm from northern Africa. In situ hybridization of histone H4, a marker for cell cycle or proliferation, showed weaker expression in the lateral spikelets compared with central spikelets in deficiens. Transcriptome analysis revealed that a number of histone superfamily genes were up-regulated in the deficiens mutant, suggesting that enhanced cell proliferation in the central spikelet may contribute to larger grains. Our data suggest that grain yield can be improved by suppressing the development of specific organs that are not positively involved in sink/source relationships

A Highly Sensitive, Quick and Simple Quantification Method for Nicotianamine and 2′-Deoxymugineic Acid from Minimum Samples Using LC/ESI-TOF-MS Achieves Functional Analysis of These Components in Plants

A highly sensitive quantitative method for assaying nicotianamine (NA) and 2′-deoxymugineic acid (DMA) using liquid chromatography/electrospray ionization time-of-flight mass spectrometry (LC/ESI-TOF-MS) was developed. The amino and hydroxyl groups of NA and DMA were derivatized using 9-fluorenylmethoxycarbonyl chloride. The amounts of NA and DMA in 10 μl of xylem sap from rice cultivated under iron (Fe)-sufficient and Fe-deficient conditions were quantified without concentration. In Fe-sufficient plants, the concentrations of NA and DMA were almost equal to that of Fe. In Fe-deficient plants, the concentration of NA did not change significantly, whereas that of DMA increased markedly

Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding

Abstract The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years. Among the various training schemes, the error backpropagation method that directly uses the firing time of neurons has attracted considerable attention because it can realize ideal temporal coding. This method uses time-to-first-spike (TTFS) coding, in which each neuron fires at most once, and this restriction on the number of firings enables information to be processed at a very low firing frequency. This low firing frequency increases the energy efficiency of information processing in SNNs. However, only an upper limit has been provided for TTFS-coded SNNs, and the information-processing capability of SNNs at lower firing frequencies has not been fully investigated. In this paper, we propose two spike-timing-based sparse-firing (SSR) regularization methods to further reduce the firing frequency of TTFS-coded SNNs. Both methods are characterized by the fact that they only require information about the firing timing and associated weights. The effects of these regularization methods were investigated on the MNIST, Fashion-MNIST, and CIFAR-10 datasets using multilayer perceptron networks and convolutional neural network structures