A Model of Semantic Completion in Generative Episodic Memory.

Many studies have suggested that episodic memory is a generative process, but most computational models adopt a storage view. In this article, we present a model of the generative aspects of episodic memory. It is based on the central hypothesis that the hippocampus stores and retrieves selected aspects of an episode as a memory trace, which is necessarily incomplete. At recall, the neocortex reasonably fills in the missing parts based on general semantic information in a process we call semantic completion. The model combines two neural network architectures known from machine learning, the vector-quantized variational autoencoder (VQ-VAE) and the pixel convolutional neural network (PixelCNN). As episodes, we use images of digits and fashion items (MNIST) augmented by different backgrounds representing context. The model is able to complete missing parts of a memory trace in a semantically plausible way up to the point where it can generate plausible images from scratch, and it generalizes well to images not trained on. Compression as well as semantic completion contribute to a strong reduction in memory requirements and robustness to noise. Finally, we also model an episodic memory experiment and can reproduce that semantically congruent contexts are always recalled better than incongruent ones, high attention levels improve memory accuracy in both cases, and contexts that are not remembered correctly are more often remembered semantically congruently than completely wrong. This model contributes to a deeper understanding of the interplay between episodic memory and semantic information in the generative process of recalling the past.

Simulated annealing optimization in wavefront shaping controlled transmission.

In this research, we present results of simulated annealing (SA), a heuristic optimization algorithm, for focusing light through a turbid medium. Performance of the algorithm on phase and amplitude modulations has been evaluated. A number of tips to tune the optimization parameters are provided. The effect of measurement noise on the performance of the SA algorithm is explored. Additionally, SA performance is compared with continuous sequential and briefly with other optimization algorithms.

Opening the Black Box of Host Range, Vectorial Diversity, and Genetic Variants of Genus Anaplasma: The Contributing Factors Toward Its Zoonosis.

Background: Genus Anaplasma of the family Anaplasmataceae possesses bacteria of hematopoietic origin, which are obligate intracellular Gram-negative bacteria transmitted mainly by tick vectors. The members of this group of infectious agents are not new as etiological agents of animal diseases worldwide. However, now, reports of their zoonotic potential have gained currency to study these pathogens. The emergence of new species of Anaplasma and the spread of existing species to new areas and hosts highlight the importance of monitoring and improving diagnostic and treatment options for zoonotic diseases caused by Anaplasma. Conclusion: This review focuses on the general and distinctive characteristics of Anaplasma spp., with particular emphasis on the novel species and their diverse spectrum of hosts as potential risk factors impacting its emerging zoonosis.

Multifractal detrended fluctuation analysis of continuous neural time series in primate visual cortex.

BACKGROUND: Local field potential (LFP) recordings have become an important tool to study the activity of populations of neurons. The functional activity of LFPs is usually compared with the activity of neighboring single spike neurons with sampling rates much higher than those of the continuous field potential channel (5 kHz). However, comparison of these signals generated with the lower sampling rate technique is important. NEW METHOD: In this study, we provide an analysis of extracellular field potential time series using the sophisticated nonlinear multifractal detrended fluctuation analysis (MF-DFA). Using the MF-DFA, we demonstrate that the integral of the singularity spectrum is a powerful new method to measure the response tuning of spikes in the continuous field potential channel. RESULTS: Results show that the spikes in the continuous channel at frequency ranges above the LFP component signals were consistently tuned similar to those in the spike channel. Our results also show that using a low-pass filter (<250 Hz), which is commonly applied as a preprocessing step to insulate LFPs from spikes, significantly influences the nonlinearity of the multifractal time series. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: Our approach for inferring the tuning curve of spiking activity from the continuous channel has some advantages compared to conventional methods such as spike trains. The MF-DFA does not require any preprocessing of the raw signal data and makes no assumptions about the time series characteristics. This method is robust and can be applied to short time series of continuous raw signals.