scholarly journals A non-spatial account of place and grid cells based on clustering models of concept learning

2018 ◽  
Author(s):  
Robert M. Mok ◽  
Bradley C. Love
Keyword(s):  
Medial Temporal Lobe ◽  
Conceptual Knowledge ◽  
Learning Algorithm ◽  
Grid Cell ◽  
Relevant Information ◽  
Neural Circuitry ◽  
General Purpose ◽  
Clustering Model ◽  
Higher Dimensional ◽  
Processing Steps

ABSTRACTOne view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leads to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry.

scholarly journals A non-spatial account of place and grid cells based on clustering models of concept learning

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Robert M. Mok ◽  
Bradley C. Love
Keyword(s):  
Medial Temporal Lobe ◽  
Conceptual Knowledge ◽  
Learning Algorithm ◽  
Grid Cell ◽  
Relevant Information ◽  
Neural Circuitry ◽  
General Purpose ◽  
Clustering Model ◽  
Higher Dimensional ◽  
Processing Steps

AbstractOne view is that conceptual knowledge is organized using the circuitry in the medial temporal lobe (MTL) that supports spatial processing and navigation. In contrast, we find that a domain-general learning algorithm explains key findings in both spatial and conceptual domains. When the clustering model is applied to spatial navigation tasks, so-called place and grid cell-like representations emerge because of the relatively uniform distribution of possible inputs in these tasks. The same mechanism applied to conceptual tasks, where the overall space can be higher-dimensional and sampling sparser, leading to representations more aligned with human conceptual knowledge. Although the types of memory supported by the MTL are superficially dissimilar, the information processing steps appear shared. Our account suggests that the MTL uses a general-purpose algorithm to learn and organize context-relevant information in a useful format, rather than relying on navigation-specific neural circuitry.

A portable method for acquiring information extraction patterns without annotated corpora

2003 ◽  
Vol 9 (2) ◽  
pp. 151-179 ◽  
Author(s):  
NEUS CATALÀ ◽  
NÚRIA CASTELL ◽  
MARIO MARTÍN
Keyword(s):  
Information Extraction ◽  
Learning Algorithm ◽  
Relevant Information ◽  
General Purpose ◽  
Human Intervention ◽  
Lexical Knowledge ◽  
Distinctive Features ◽  
Domain Specific ◽  
Building Information ◽  
Automatic Acquisition

The main issue when building Information Extraction (IE) systems is how to obtain the knowledge needed to identify relevant information in a document. Most approaches require expert human intervention in many steps of the acquisition process. In this paper we describe ESSENCE, a new method for acquiring IE patterns that significantly reduces the need for human intervention. The method is based on ELA, a specifically designed learning algorithm for acquiring IE patterns without tagged examples. The distinctive features of ESSENCE and ELA are that (1) they permit the automatic acquisition of IE patterns from unrestricted and untagged text representative of the domain, due to (2) their ability to identify regularities around semantically relevant concept-words for the IE task by (3) using non-domain-specific lexical knowledge tools such as WordNet, and (4) restricting the human intervention to defining the task, and validating and typifying the set of IE patterns obtained. Since ESSENCE does not require a corpus annotated with the type of information to be extracted and it uses a general purpose ontology and widely applied syntactic tools, it reduces the expert effort required to build an IE system and therefore also reduces the effort of porting the method to any domain. The results of the application of ESSENCE to the acquisition of IE patterns in an MUC-like task are shown.

scholarly journals Modulation of human intracranial theta oscillations during freely moving spatial navigation and memory

2019 ◽  
Author(s):  
Zahra M. Aghajan ◽  
Diane Villaroman ◽  
Sonja Hiller ◽  
Tyler J. Wishard ◽  
Uros Topalovic ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Spatial Memory ◽  
High Frequency ◽  
Medial Temporal Lobe ◽  
Spatial Navigation ◽  
Grid Cell ◽  
Cell System ◽  
Theta Oscillations ◽  
Neural Basis ◽  
Freely Moving

SummaryHow the human brain supports accurate navigation of a learned environment has been an active topic of research for nearly a century1–5. In rodents, the theta rhythm within the medial temporal lobe (MTL) has been proposed as a neural basis for fragmenting incoming information and temporally organizing experiences and is thus widely implicated in spatial and episodic memory6. In addition, high-frequency theta (~8Hz) is associated with navigation, and loss of theta results in spatial memory deficits in rats 7. Recently, high-frequency theta oscillations during ambulatory movement have been identified in humans8,9, though their relationship to spatial memory remains unexplored. Here, we were able to record MTL activity during spatial memory and navigation in freely moving humans immersed in a room-scale virtual reality (VR) environment. Naturalistic movements were captured using motion tracking combined with wireless VR in participants implanted with an intracranial electroencephalographic (iEEG) recording system for the treatment of epilepsy. We found that prevalence of theta oscillations across brain sites during both learning and recall of spatial locations during ambulatory navigation is critically linked to memory performance. This finding supports the reinstatement hypothesis of episodic memory—thought to underlie our ability to recreate a prior experience10–12—and suggests that theta prevalence within the MTL may act as a potential representational state for memory reinstatement during spatial navigation. Additionally, we found that theta power is hexadirectionally modulated13–15 as a function of the direction of physical movement, most prominently after learning has occurred. This effect bears a resemblance to the rodent grid cell system16 and suggests an analog in human navigation. Taken together, our results provide the first characterization of neural oscillations in the human MTL during ambulatory spatial memory tasks and provide a platform for future investigations of neural mechanisms underlying freely moving navigation in humans.

scholarly journals Rapid neural representations of personally relevant faces

2019 ◽  
Author(s):  
Mareike Bayer ◽  
Oksana Berhe ◽  
Isabel Dziobek ◽  
Tom Johnstone
Keyword(s):  
Time Course ◽  
Primary Source ◽  
Relevant Information ◽  
Neural Circuitry ◽  
Romantic Partners ◽  
Personal Relevance ◽  
Neural Responses ◽  
Emotional Faces ◽  
Neural Representations ◽  
Processing Network

AbstractThe faces of those most personally relevant to us are our primary source of social information, making their timely perception a priority. Recent research indicates that gender, age and identity of faces can be decoded from EEG/MEG data within 100ms. Yet the time course and neural circuitry involved in representing the personal relevance of faces remain unknown. We applied simultaneous EEG-fMRI to examine neural responses to emotional faces of female participants’ romantic partners, friends, and a stranger. Combining EEG and fMRI in cross-modal representational similarity analyses, we provide evidence that representations of personal relevance start prior to structural encoding at 100ms in visual cortex, but also in prefrontal and midline regions involved in value representation, and monitoring and recall of self-relevant information. Representations related to romantic love emerged after 300ms. Our results add to an emerging body of research that suggests that models of face perception need to be updated to account for rapid detection of personal relevance in cortical circuitry beyond the core face processing network.

A Bird’s Eye View of 100 years of research on Hypertension: Machine Learning Classifications of Topics (Preprint)

2021 ◽  
Author(s):  
Mustapha Abba ◽  
Chidozie Nduka ◽  
Seun Anjorin ◽  
Shukri Mohamed ◽  
Emmanuel Agogo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Latent Dirichlet Allocation ◽  
Learning Algorithm ◽  
Temporal Trends ◽  
Relevant Information ◽  
Research Articles ◽  
Major Cardiovascular Events ◽  
And Shifts ◽  
Negative Sentiment ◽  
Over Time

BACKGROUND Due to scientific and technical advancements in the field, published hypertension research has developed during the last decade. Given the huge amount of scientific material published in this field, identifying the relevant information is difficult. We employed topic modelling, which is a strong approach for extracting useful information from enormous amounts of unstructured text. OBJECTIVE To utilize a machine learning algorithm to uncover hidden topics and subtopics from 100 years of peer-reviewed hypertension publications and identify temporal trends. METHODS The titles and abstracts of hypertension papers indexed in PubMed were examined. We used the Latent Dirichlet Allocation (LDA) model to select 20 primary subjects and then ran a trend analysis to see how popular they were over time. RESULTS We gathered 581,750 hypertension-related research articles from 1900 to 2018 and divided them into 20 categories. Preclinical, risk factors, complications, and therapy studies were the categories used to categorise the publications. We discovered themes that were becoming increasingly ‘hot,' becoming less ‘cold,' and being published seldom. Risk variables and major cardiovascular events subjects displayed very dynamic patterns over time (how? – briefly detail here). The majority of the articles (71.2%) had a negative valency, followed by positive (20.6%) and neutral valencies (8.2 percent). Between 1980 and 2000, negative sentiment articles fell somewhat, while positive and neutral sentiment articles climbed significantly. CONCLUSIONS This unique machine learning methodology provided fascinating insights on current hypertension research trends. This method allows researchers to discover study subjects and shifts in study focus, and in the end, it captures the broader picture of the primary concepts in current hypertension research articles. CLINICALTRIAL Not applicable

A Framework for an Artificial-Neural-Network-Based Electronic Nose

2022 ◽  
pp. 350-374
Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Electronic Nose ◽  
Learning Algorithm ◽  
General Purpose ◽  
Food Spoilage ◽  
Standard Data ◽  
Semiconductor Gas Sensor ◽  
Artificial Neural

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.

Improving Results Aggregation Strategies in Distributed Information Retrieval

2015 ◽  
Vol 17 ◽  
pp. 94-104
Author(s):  
Benjamin Ghansah ◽  
Sheng Li Wu ◽  
Nathaniel Ekow Ghansah
Keyword(s):  
Information Retrieval ◽  
User Satisfaction ◽  
Information Needs ◽  
Relevant Information ◽  
General Purpose ◽  
Distributed Information ◽  
Distributed Information Retrieval ◽  
Result Diversification ◽  
Result Merging ◽  
Ranked List

The top-ranked documents from various information sources that are merged together into a unified ranked list may cover the same piece of relevant information, and cannot satisfy different user needs. Result diversification(RD) solves this problem by diversifying results to cover more information needs. In recent times, RD has attracted much attention as a means of increasing user satisfaction in general purpose search engines. A myriad of approaches have been proposed in the related works for the diversification problem. However, no concrete study of search result diversification has been done in a Distributed Information Retrieval(DIR) setting. In this paper, we survey, classify and propose a theoretical framework that aims at improving diversification at the result merging phase of a DIR environment.

Temporal Lobe Epilepsy

2017 ◽  
Author(s):  
Edward H. Bertram
Keyword(s):  
Animal Models ◽  
Temporal Lobe Epilepsy ◽  
Entorhinal Cortex ◽  
Temporal Lobe ◽  
Medial Temporal Lobe ◽  
Focal Epilepsy ◽  
Neural Circuitry ◽  
Human Condition ◽  
Physiological Changes ◽  
Limbic Structures

Temporal lobe epilepsy, as discussed in this chapter, is a focal epilepsy that involves primarily the limbic structures of the medial temporal lobe (amygdala, hippocampus, and entorhinal cortex). In recent years animal models have been developed that mirror the pathology and pathophysiology of this disease. This chapter reviews the human condition, the structural and physiological changes that support the development of seizures. The neural circuitry of seizure initiation will be reviewed with a goal of creating a framework for developing more effective treatments for this disease.

scholarly journals Attention promotes episodic encoding by stabilizing hippocampal representations

2016 ◽  
Vol 113 (4) ◽  
pp. E420-E429 ◽  
Author(s):  
Mariam Aly ◽  
Nicholas B. Turk-Browne
Keyword(s):  
High Resolution ◽  
Medial Temporal Lobe ◽  
Activity Patterns ◽  
Relevant Information ◽  
Attentional State ◽  
Template Activity ◽  
Mechanistic Insight ◽  
Episodic Encoding ◽  
The Brain ◽  
Insight Into

Attention influences what is later remembered, but little is known about how this occurs in the brain. We hypothesized that behavioral goals modulate the attentional state of the hippocampus to prioritize goal-relevant aspects of experience for encoding. Participants viewed rooms with paintings, attending to room layouts or painting styles on different trials during high-resolution functional MRI. We identified template activity patterns in each hippocampal subfield that corresponded to the attentional state induced by each task. Participants then incidentally encoded new rooms with art while attending to the layout or painting style, and memory was subsequently tested. We found that when task-relevant information was better remembered, the hippocampus was more likely to have been in the correct attentional state during encoding. This effect was specific to the hippocampus, and not found in medial temporal lobe cortex, category-selective areas of the visual system, or elsewhere in the brain. These findings provide mechanistic insight into how attention transforms percepts into memories.

Le système de renseignements C.F.C.

1970 ◽  
Vol 46 (1) ◽  
pp. 39-43
Author(s):  
W. Pleines ◽  
L. Letourneau
Keyword(s):  
Maintenance Phase ◽  
Data Bank ◽  
Relevant Information ◽  
General Purpose ◽  
Computer Applications ◽  
Permanent Plots ◽  
Enormous Amount ◽  
Forest Experiment Station ◽  
Tree Data ◽  
Reporting Phase

Forest surveys based on permanent plots possess peculiarities unusual in other computer applications. The enormous amount of information (30,000 plots, 1,000,000 trees), must be checked, corrected. Relevant information must be selected from the data bank for statistical computations. Because information for decision-making changes, the computer programs must be flexible.This article explains how this was done. In a temporary phase, all card data were "converted" to standard codes and format and written on magnetic tapes. In the file maintenance phase, the data bank is checked and corrected. Volumes are computed, plots checked by accumulating tree data, etc. The file creation phase builds a unit record from plot and tree information. Stratification data can also be merged on the new file. The reporting phase consists of modified versions of programs of the Northeastern Forest Experiment Station in the U.S. (Wilson and Peters, 1967). TABLE computes statistics by strata and condenses them in matrices, OUTPUT prints them in desired form.This computer system is a harmonious combination of special and general purpose programs. CIP experiences in developing these programs may help other foresters hence more exchange of information about data processing is desired.

Sign in / Sign up

Export Citation Format

Share Document