Future Directions for Neural Networks and Intelligent Systems from the Brain Imaging Research

2000 ◽  
pp. 191-212 ◽  
Author(s):  
John G. Taylor
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Intelligent Systems ◽  
Future Directions ◽  
Imaging Research ◽  
The Brain

Intelligent Traffic Sign Classifiers

2011 ◽  
pp. 956-962
Author(s):  
Raúl Vicen Bueno ◽  
Elena Torijano Gordo ◽  
Antonio García González ◽  
Manuel Rosa Zurera ◽  
Roberto Gil Pita
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Main Part ◽  
Point Of View ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Artificial Neural ◽  
The Brain

The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.

Intelligent Radar Detectors

2011 ◽  
pp. 933-939
Author(s):  
Raúl Vicen Bueno ◽  
Manuel Rosa Zurera ◽  
María Pilar Jarabo Amores ◽  
Roberto Gil Pita ◽  
David de la Mata Moya
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Main Part ◽  
Point Of View ◽  
Communications Systems ◽  
Reflected Signal ◽  
Artificial Neural ◽  
The Brain

The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.

Artificial Neural Networks for Prediction

2011 ◽  
pp. 54-58
Author(s):  
Rafael Marti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intelligent Systems ◽  
Intelligent System ◽  
Human Capabilities ◽  
Design And Implementation ◽  
Starting Point ◽  
Input Signals ◽  
Artificial Neural ◽  
The Brain

The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.

scholarly journals An Important Step toward Understanding the Role of Body-based Cues on Human Spatial Memory for Large-Scale Environments

2021 ◽  
Vol 33 (2) ◽  
pp. 167-179
Author(s):  
Derek J. Huffman ◽  
Arne D. Ekstrom
Keyword(s):  
Virtual Reality ◽  
Spatial Memory ◽  
Large Scale ◽  
Virtual Reality Technology ◽  
Future Directions ◽  
Imaging Research ◽  
Physical Limitations ◽  
Exciting Possibility ◽  
The Brain

Moving our body through space is fundamental to human navigation; however, technical and physical limitations have hindered our ability to study the role of these body-based cues experimentally. We recently designed an experiment using novel immersive virtual-reality technology, which allowed us to tightly control the availability of body-based cues to determine how these cues influence human spatial memory [Huffman, D. J., & Ekstrom, A. D. A modality-independent network underlies the retrieval of large-scale spatial environments in the human brain. Neuron, 104, 611–622, 2019]. Our analysis of behavior and fMRI data revealed a similar pattern of results across a range of body-based cues conditions, thus suggesting that participants likely relied primarily on vision to form and retrieve abstract, holistic representations of the large-scale environments in our experiment. We ended our paper by discussing a number of caveats and future directions for research on the role of body-based cues in human spatial memory. Here, we reiterate and expand on this discussion, and we use a commentary in this issue by A. Steel, C. E. Robertson, and J. S. Taube (Current promises and limitations of combined virtual reality and functional magnetic resonance imaging research in humans: A commentary on Huffman and Ekstrom (2019). Journal of Cognitive Neuroscience, 2020) as a helpful discussion point regarding some of the questions that we think will be the most interesting in the coming years. We highlight the exciting possibility of taking a more naturalistic approach to study the behavior, cognition, and neuroscience of navigation. Moreover, we share the hope that researchers who study navigation in humans and nonhuman animals will synergize to provide more rapid advancements in our understanding of cognition and the brain.

scholarly journals BIDS-EEG: an extension to the Brain Imaging Data Structure (BIDS) Specification for electroencephalography

2018 ◽  
Author(s):  
Cyril R Pernet ◽  
Stefan Appelhoff ◽  
Guillaume Flandin ◽  
Christophe Phillips ◽  
Arnaud Delorme ◽  
...  
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Study Data ◽  
Scientific Data ◽  
Research Community ◽  
Imaging Data ◽  
Eeg Data ◽  
Imaging Research ◽  
Brain Imaging Data ◽  
The Brain

The Brain Imaging Data Structure (BIDS) project is a quickly evolving effort among the human brain imaging research community to create standards allowing researchers to readily organize and share study data within and between laboratories. The first BIDS standard was proposed for the MRI/fMRI research community and has now been widely adopted. More recently a magnetoencephalography (MEG) data extension, BIDS-MEG, has been published. Here we present an extension to BIDS for electroencephalography (EEG) data, BIDS-EEG, along with tools and references to a series of public EEG datasets organized using this new standard. A shortened version is now published in Nature Scientific Data: https://www.nature.com/articles/s41597-019-0104-8.

Alcohol Use Disorder

2018 ◽  
Author(s):  
Igor Ponomarev
Keyword(s):  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Critical Discussion ◽  
Epigenetic Mechanisms ◽  
Future Directions ◽  
Clinically Significant ◽  
Early Life Exposures ◽  
The Brain ◽  
Epigenetic Research

Alcohol use disorder (AUD) is characterized by clinically significant impairments in health and social function. Epigenetic mechanisms of gene regulation may provide an attractive explanation for how early life exposures to alcohol contribute to the development of AUD and exert lifelong effects on the brain. This chapter provides a critical discussion of the role of epigenetic mechanisms in AUD etiology and the potential of epigenetic research to improve diagnosis, evaluate risks for alcohol-induced pathologies, and promote development of novel therapies for the prevention and treatment of AUD. Challenges of the current epigenetic approaches and future directions are also discussed.

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