Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.

Winograd algorithm can effectively reduce the computational complexity of convolution operation. Effectively using the parallelism of Winograd convolution algorithm can effectively improve the performance of accelerator architectures on FPGA. The stride represents the number of elements that the window slides when filter is scanned on the input feature map. The Winograd algorithm with the stride of 2 implemented in previous studies divided the input feature maps into multiple groups of Winograd algorithms to complete the operations, resulting in additional precomputation and hardware resource overhead. In this paper, we propose a new Winograd convolution algorithm with the stride of 2. This method uses the unified Winograd transformation matrices instead of the grouping method to complete the calculation. Therefore, the method proposed in this paper can realize 2D Winograd convolution and 3D Winograd convolution by nested 1D Winograd convolution, just like the Winograd convolution algorithm with the stride of 1. In this paper, Winograd transformation matrices with kernel size of 3, 5, and 7 are provided. In particular, for convolution with the kernel of 3, this method reduces the addition operations of Winograd algorithm by 30.0%-31.5% and removes unnecessary shift operations completely. In addition, we implement Winograd convolution algorithm with the stride of 2 through template design, and realize pipeline and data reuse. Compared to the state-of-the-art implementation, the proposed method results in a speedup of 1.24 and reduces resource usage.

This paper describes a study of how social media could be integrated and used in Mobile Experience Sampling. As addressed in previous studies, Experience Sampling Method relies on high response frequency. However participants may experience it as a burden which may cause delay or even suspension of data collection. We have developed a system that allows participants in Mobile Experience Sampling studies to share their questions and answers on social media. We tested our system in a group of 40 participants. The study shows that enabled sharing of ESM questions significantly increased response and participation rates, in our test by +43%, which also indicates its influence on participants' compliance and motivation levels. This paper presents the study and discusses some further use and influence of social media in Experience Sampling.

MOTIVATION: Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study. Traditional Pearson correlation analysis treating the observed data as absolute abundances of the microbes may lead to spurious results because the data only represent relative abundances. Special care and appropriate methods are required prior to correlation analysis for these compositional data. RESULTS: In this article, we first discuss the correlation definition of latent variables for compositional data. We then propose a novel method called CCLasso based on least squares with [Formula: see text] penalty to infer the correlation network for latent variables of compositional data from metagenomic data. An effective alternating direction algorithm from augmented Lagrangian method is used to solve the optimization problem. The simulation results show that CCLasso outperforms existing methods, e.g. SparCC, in edge recovery for compositional data. It also compares well with SparCC in estimating correlation network of microbe species from the Human Microbiome Project. AVAILABILITY AND IMPLEMENTATION: CCLasso is open source and freely available from https://github.com/huayingfang/CCLasso under GNU LGPL v3. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.