scholarly journals Multiple types of navigational information are independently encoded in the population activities of the dentate gyrus neurons

2020 ◽  
Author(s):  
Tomoyuki Murano ◽  
Ryuichi Nakajima ◽  
Akito Nakao ◽  
Nao Hirata ◽  
Satoko Amemori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Dentate Gyrus ◽  
Open Field ◽  
Activity Patterns ◽  
Spatial Coding ◽  
Motion Direction ◽  
Freely Moving ◽  
Types Of Information ◽  
Decoding Accuracy

AbstractThe dentate gyrus (DG) plays critical roles in cognitive functions such as learning, memory, and spatial coding, and its dysfunction is implicated in various neuropsychiatric disorders. However, it remains largely unknown how information is represented in this region. Here, we recorded neuronal activity in the DG using Ca2+ imaging in freely moving mice and analysed this activity using machine learning. The activity patterns of populations of DG neurons enabled us to successfully decode position, speed, and motion direction in an open field as well as current and future location in a T-maze, and each individual neuron was diversely and independently tuned to these multiple information types. In αCaMKII heterozygous knockout mice, which present deficits in spatial remote and working memory, the decoding accuracy of position in the open field and future location in the T-maze were selectively reduced. These results suggest that multiple types of information are independently distributed in DG neurons.

scholarly journals Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4821
Author(s):  
Rami Ahmad ◽  
Raniyah Wazirali ◽  
Qusay Bsoul ◽  
Tarik Abu-Ain ◽  
Waleed Abu-Ain
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Open Field ◽  
Network Lifetime ◽  
Detection Efficiency ◽  
Denial Of Service ◽  
Harmony Search ◽  
Machine Learning Algorithms ◽  
Transport Layer ◽  
Feature Selection Techniques

Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

scholarly journals Abnormal brain activity patterns during spatial working memory task in patients with end-stage renal disease on maintenance hemodialysis: a fMRI study

2020 ◽  
Author(s):  
Jinzhuang Huang ◽  
Lei Xie ◽  
Ruiwei Guo ◽  
Jinhong Wang ◽  
Jinquan Lin ◽  
...  
Keyword(s):  
Working Memory ◽  
Frontal Cortex ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Activity Patterns ◽  
Parietal Lobule ◽  
Stage Renal Disease ◽  
End Stage ◽  
Esrd Patients ◽  
Back Task

Abstract Hemodialysis (HD) is associated with cognitive impairment in patients with end-stage renal disease (ESRD). However, the neural mechanism of spatial working memory (SWM) impairment in HD-ESRD patients remains unclear. We investigated the abnormal alterations in SWM-associated brain activity patterns in HD-ESRD patients using blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) technique during n-back tasks. Twenty-two HD-ESRD patients and 22 well-matched controls underwent an fMRI scan while undergoing a three-load n-back tasks with different difficulty levels. Cognitive and mental states were assessed using a battery of neuropsychologic tests. The HD-ESRD patients exhibited worse memory abilities than controls. Compared with the control group, the HD-ESRD patient group showed lower accuracy and longer response time under the n-back tasks, especially in the 2-back task. The patterns of brain activation changed under different working memory loads in the HD-ESRD patients, showing decreased activity in the right medial frontal gyrus and inferior frontal gyrus under 0-back and 1-back task, while more decreased activation in the bilateral frontal cortex, parietal lobule, anterior/posterior cingulate cortex and insula cortex under 2-back task. With the increase of task difficulty, the activation degree of the frontal and parietal cortex decreased. More importantly, we found that lower activation in frontal cortex and parietal lobule was associated with worse cognitive function in the HD-ESRD patients. These results demonstrate that the abnormal brain activity patterns of frontal cortex and parietal lobule may reflect the neural mediation of SWM impairment.

scholarly journals Replay in minds and machines

2021 ◽  
Author(s):  
Lennart Wittkuhn ◽  
Samson Chien ◽  
Sam Hall-McMaster ◽  
Nicolas W. Schuck
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Activity Patterns ◽  
Representation Learning ◽  
Parallel Machine ◽  
Artificial Agents ◽  
Active Behavior ◽  
Performance Improvements ◽  
Experience Replay ◽  
Data Efficiency

Experience-related brain activity patterns have been found to reactivate during sleep, wakeful rest, and brief pauses from active behavior. In parallel, machine learning research has found that experience replay can lead to substantial performance improvements in artificial agents. Together, these lines of research have significantly expanded our understanding of the potential computational benefits replay may provide to biological and artificial agents alike. We provide an overview of findings in replay research from neuroscience and machine learning and summarize the computational benefits an agent can gain from replay that cannot be achieved through direct interactions with the world itself. These benefits include faster learning and data efficiency, less forgetting, prioritizing important experiences, as well as improved planning and generalization. In addition to the benefits of replay for improving an agent's decision-making policy, we highlight the less-well studied aspect of replay in representation learning, wherein replay could provide a mechanism to learn the structure and relevant aspects of the environment. Thus, replay might help the agent to build task-appropriate state representations.

scholarly journals ViraMiner: Deep Learning on Raw DNA Sequences for Identifying Viral Genomes in Human Samples

2019 ◽  
Author(s):  
Ardi Tampuu ◽  
Zurab Bzhalava ◽  
Joakim Dillner ◽  
Raul Vicente
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Dna Sequences ◽  
Recommendation System ◽  
Clinical Importance ◽  
Training Dataset ◽  
Viral Genomes ◽  
Human Samples ◽  
Types Of Information ◽  
Improved Accuracy

ABSTRACTDespite its clinical importance, detection of highly divergent or yet unknown viruses is a major challenge. When human samples are sequenced, conventional alignments classify many assembled contigs as “unknown” since many of the sequences are not similar to known genomes. In this work, we developed ViraMiner, a deep learning-based method to identify viruses in various human biospecimens. ViraMiner contains two branches of Convolutional Neural Networks designed to detect both patterns and pattern-frequencies on raw metagenomics contigs. The training dataset included sequences obtained from 19 metagenomic experiments which were analyzed and labeled by BLAST. The model achieves significantly improved accuracy compared to other machine learning methods for viral genome classification. Using 300 bp contigs ViraMiner achieves 0.923 area under the ROC curve. To our knowledge, this is the first machine learning methodology that can detect the presence of viral sequences among raw metagenomic contigs from diverse human samples. We suggest that the proposed model captures different types of information of genome composition, and can be used as a recommendation system to further investigate sequences labeled as “unknown” by conventional alignment methods. Exploring these highly-divergent viruses, in turn, can enhance our knowledge of infectious causes of diseases.

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