Context-dependent persistency as a coding mechanism for robust and widely distributed value coding

1. Download : Download high-res image (180KB)
2. Download : Download full-size image

     

New excitement in cognitive space: between place cells and spatial memory

Hippocampal principal neurons-'place cells'-exhibit location-specific firing. Recent work addresses the link between place cell activity and hippocampal memory function. New tasks that challenge spatial memory allow recording from single neurons, as well as ensembles of neurons, during memory computations, and insights into the cellular mechanisms of spatial memory are beginning to emerge.     

戊四氮致痫大鼠情感反应、学习记忆功能及海马N-甲基-D-门冬氨酸受体mRNA表达的改变

目的：探讨实验性癫痫持续状态（SE）对大鼠认知功能的影响及N-甲基-D-门冬氨酸（NMDA）受体表达的变化。方法：戊四氮诱导大鼠SE,采用抬高迷宫和Morris水迷宫观察大鼠情感反应和学习记忆功能的改变。RT-PCR方法检测大鼠海马NMDA受体亚单位NR1mRNA的表达。结果：sE组大鼠在抬高迷宫开放臂中逃避时间延长（P〈0．01）,进入次数增多（P〈0、01）;水迷宫中逃避潜伏期延长（P〈0.01）,搜寻策略变差（P〈0．05）,平台象限游泳时间百分比降低（P〈0．01）,穿越平台次数减少（P〈0．01）。同时伴有海马NR1mRNA表达下调（P〈0．01）。结论：SE可使大鼠情感行为改变和学习记忆功能受损,NR1可能参与这一变化的病理生理过程。     

Over‐expressed MST1 impaired spatial memory via disturbing neural oscillation patterns in mice

The activated mammalian Ste20‐like serine/threonine kinases 1 (MST1) was found in the central nervous system diseases, such as cerebral ischemia, stroke and ALS, which were related with cognitions. The aim of this study was to examine the effect of elevated MST1 on memory functions in C57BL/6J mice. We also explored the underlying mechanism about the pattern alteration of neural oscillations, closely associated with cognitive dysfunctions, at different physiological rhythms, which were related to a wide range of basic and higher‐level cognitive activities. A mouse model of the adeno‐associated virus (AAV)‐mediated overexpression of MST1 was established. The behavioral experiments showed that spatial memory was significantly damaged in MST1 mice. The distribution of either theta or gamma power was clearly disturbed in MST1 animals. Moreover, the synchronization in both theta and gamma rhythms, and theta‐gamma cross‐frequency coupling were significantly weakened in MST1 mice. In addition, the expressions of GABAA receptor, GAD67 and parvalbumin (PV) were obviously increased in MST1 mice. Meanwhile, blocking MST1 activity could inhibit the activation of FOXO3a and YAP. The above data suggest that MST1‐overexpression may induce memory impairments via disturbing the patterns of neural activities, which is possibly associated with the abnormal GABAergic expression level.     

An approach to rapid processing of camera trap images with minimal human input

1. Camera traps have become an extensively utilized tool in ecological research, but the manual processing of images created by a network of camera traps rapidly becomes an overwhelming task, even for small camera trap studies.
2. We used transfer learning to create convolutional neural network (CNN) models for identification and classification. By utilizing a small dataset with an average of 275 labeled images per species class, the model was able to distinguish between species and remove false triggers.
3. We trained the model to detect 17 object classes with individual species identification, reaching an accuracy up to 92% and an average F1 score of 85%. Previous studies have suggested the need for thousands of images of each object class to reach results comparable to those achieved by human observers; however, we show that such accuracy can be achieved with fewer images.
4. With transfer learning and an ongoing camera trap study, a deep learning model can be successfully created by a small camera trap study. A generalizable model produced from an unbalanced class set can be utilized to extract trap events that can later be confirmed by human processors.

     

A Neural Basis for Categorizing Sensory Stimuli to Enhance Decision Accuracy

1. Download : Download high-res image (174KB)
2. Download : Download full-size image

     

Optimizing Artificial Neural Network-Based Models to Predict Rice Blast Epidemics in Korea

To predict rice blast, many machine learning methods have been proposed. As the quality and quantity of input data are essential for machine learning techniques, this study develops three artificial neural network (ANN)-based rice blast prediction models by combining two ANN models, the feed-forward neural network (FFNN) and long short-term memory (LSTM), with diverse input datasets, and compares their performance. The Blast_Weather_FFNN model had the highest recall score (66.3%) for rice blast prediction. This model requires two types of input data: blast occurrence data for the last 3 years and weather data (daily maximum temperature, relative humidity, and precipitation) between January and July of the prediction year. This study showed that the performance of an ANN-based disease prediction model was improved by applying suitable machine learning techniques together with the optimization of hyperparameter tuning involving input data. Moreover, we highlight the importance of the systematic collection of long-term disease data.     

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

I hypothesize that re‐occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core. In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery. Upon repeated stimulus, peripheral network nodes remodel the network core that encodes the attractor of the new response. This “core‐periphery learning” theory reviews and generalizes the heretofore fragmented knowledge on attractor formation by neural networks, periphery‐driven innovation, and a number of recent reports on the adaptation of protein, neuronal, and social networks. The core‐periphery learning theory may increase our understanding of signaling, memory formation, information encoding and decision‐making processes. Moreover, the power of network periphery‐related “wisdom of crowds” inventing creative, novel responses indicates that deliberative democracy is a slow yet efficient learning strategy developed as the success of a billion‐year evolution. Also see the video abstract here: https://youtu.be/IIjP7zWGjVE .     

Machine learning in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.     

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN–2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.     

Stellate Cells in the Medial Entorhinal Cortex Are Required for Spatial Learning

1. Download high-res image (129KB)
2. Download full-size image