scholarly journals Neuromorphic Spatiotemporal Information Processing Using Neuro-Photodetector Systems

2020 ◽  
Vol 10 (23) ◽  
pp. 8358
Author(s):  
Mohit Kumar ◽  
Joondong Kim
Keyword(s):  
Information Processing ◽  
Visual Recognition ◽  
Recognition System ◽  
Photonic Devices ◽  
Memory Storage ◽  
Long Term Memory ◽  
Image Sensing ◽  
Spatiotemporal Information ◽  
Joint Task ◽  
Short And Long Term

Spatiotemporal information processing within the human brain is done by a joint task of neurons and synapses with direct optical inputs. Therefore, to mimic this neurofunction using photonic devices could be an essential step to design future artificial visual recognition and memory storage systems. Herein, we proposed and developed a proof-of-principle two-terminal device that exhibits key features of neuron (integration, leaky, and relaxation) and synapse (short- and long-term memory) together in response with direct optical input stimuli. Importantly, these devices with processing and memory features, are further effectively integrated to build an artificial neural network, which are enabled to do neuromorphic spatiotemporal image sensing. Our approach provides a simple but effective route to implement for an artificial visual recognition system, which also has applications in edge computing and the internet of things.

scholarly journals Towards an integration of deep learning and neuroscience

2016 ◽  
Author(s):  
Adam Henry Marblestone ◽  
Greg Wayne ◽  
Konrad P Kording
Keyword(s):  
Machine Learning ◽  
Memory Storage ◽  
Cost Functions ◽  
Long Term Memory ◽  
Recent Developments ◽  
Short And Long Term ◽  
The Brain ◽  
And Training ◽  
Learning Data

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) these cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

scholarly journals Temporal Information Processing in Short- and Long-Term Memory of Patients with Schizophrenia

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e26140 ◽  
Author(s):  
Steffen Landgraf ◽  
Joerg Steingen ◽  
Yvonne Eppert ◽  
Ulrich Niedermeyer ◽  
Elke van der Meer ◽  
...  
Keyword(s):  
Information Processing ◽  
Temporal Information ◽  
Long Term Memory ◽  
Term Memory ◽  
Temporal Information Processing ◽  
Short And Long Term

Introduction

2021 ◽  
pp. 1-24
Author(s):  
Erik D. Reichle
Keyword(s):  
Information Processing ◽  
Storage Systems ◽  
Human Mind ◽  
Memory Storage ◽  
Long Term Memory ◽  
Behavioral Experiments ◽  
Term Memory ◽  
Modal Model ◽  
The Mind

This chapter provides an introduction to reading research and computer models. The chapter discusses the information-processing metaphors that have been used to study the human mind, drawing parallels between components of the latter and similar distinctions in computers (e.g., short- vs. long-term memory/storage systems). The chapter also introduces the modal model, or most commonly used metaphor for describing the human mind. The chapter then provides examples illustrating how behavioral experiments can be used to make informed inferences about the operation of the mind and its information-processing components. The chapter closes with a discussion of reading, including how it is similar and dissimilar to spoken language, and how the latter differs from other forms of communication (e.g., animal).

Short- and long-term memory tasks predict working memory performance, and vice versa.

2019 ◽  
Vol 73 (2) ◽  
pp. 79-93 ◽  
Author(s):  
Ian Neath ◽  
Jean Saint-Aubin ◽  
Tamra J. Bireta ◽  
Andrew J. Gabel ◽  
Chelsea G. Hudson ◽  
...  
Keyword(s):  
Working Memory ◽  
Memory Performance ◽  
Long Term Memory ◽  
Term Memory ◽  
Short And Long Term

The Improvement of Short- and Long-term Memory of Young Children by BF-7

2010 ◽  
Vol 39 (3) ◽  
pp. 376-382 ◽  
Author(s):  
Do-Hee Kim ◽  
Ok-Hyeon Kim ◽  
Joo-Hong Yeo ◽  
Kwang-Gill Lee ◽  
Geum-Duck Park ◽  
...  
Keyword(s):  
Young Children ◽  
Long Term Memory ◽  
Term Memory ◽  
Short And Long Term

Specialized systems for the processing of mnemonic information within the primate frontal cortex

1996 ◽  
Vol 351 (1346) ◽  
pp. 1455-1462 ◽  
Keyword(s):  
Frontal Cortex ◽  
Specific Activity ◽  
Human Subjects ◽  
Long Term Memory ◽  
Executive Processing ◽  
Severe Impairment ◽  
Normal Human ◽  
Short And Long Term ◽  
Cortical Association Areas

The lateral frontal cortex is involved in various aspects of executive processing within short- and long-term memory. It is argued that the different parts of the lateral frontal cortex make distinct contributions to memory that differ in terms of the level of executive processing that is carried out in interaction with posterior cortical systems. According to this hypothesis, the mid-dorsolateral frontal cortex (areas 46 and 9) is a specialized system for the monitoring and manipulation of information within working memory, whereas the mid-ventrolateral frontal cortex (areas 47/12 and 45) is involved in the active retrieval of information from the posterior cortical association areas. Data are presented which support this two-level hypothesis that posits two distinct levels of interaction of the lateral frontal cortex with posterior cortical association areas. Functional activation studies with normal human subjects have demonstrated specific activity within the mid-dorsolateral region of the frontal cortex during the performance of tasks requiring monitoring of self-generated and externally generated sequences of responses. In the monkey, lesions restricted to this region of the frontal cortex yield a severe impairment in performance of the above tasks, this impairment appearing against a background of normal performance on several basic mnemonic tasks. By contrast, a more severe impairment follows damage to the mid-ventrolateral frontal region and functional activation studies have demonstrated specific changes in activity in this region in relation to the active retrieval of information from memory.

scholarly journals Pedagogical Strategies for the Enhancement of Medical Education

2021 ◽  
Author(s):  
Mohammad B. Azzam ◽  
Ronald A. Easteal
Keyword(s):  
Medical Education ◽  
Cognitive Science ◽  
Retrieval Practice ◽  
Memory Storage ◽  
Long Term Memory ◽  
Memory And Learning ◽  
Pedagogical Strategies ◽  
Term Memory ◽  
Sleep Consolidation

AbstractClearly, memory and learning are essential to medical education. To make memory and learning more robust and long-term, educators should turn to the advances in neuroscience and cognitive science to direct their efforts. This paper describes the memory pathways and stages with emphasis leading to long-term memory storage. Particular stress is placed on this storage as a construct known as schema. Leading from this background, several pedagogical strategies are described: cognitive load, dual encoding, spiral syllabus, bridging and chunking, sleep consolidation, and retrieval practice.

Research on visual recognition intelligent system of city brand image based on fuzzy theory and regional culture

2021 ◽  
pp. 1-9
Author(s):  
Lin Han ◽  
Lu Han
Keyword(s):  
Neural Network ◽  
Visual Recognition ◽  
Intelligent System ◽  
Brand Image ◽  
Fuzzy Theory ◽  
Recognition System ◽  
Market Competitiveness ◽  
Visual Identity ◽  
Fuzzy Neural ◽  
Intelligent Recognition

With the rapid development of China’s market economy, brand image is becoming more and more important for an enterprise to enhance its market competitiveness and occupy a favorable market share. However, the brand image of many established companies gradually loses with the development of society and the improvement of people’s aesthetic pursuit. This has forced it to change its corporate brand image and regain the favor of the market. Based on this, this article combines the related knowledge and concepts of fuzzy theory, from the perspective of visual identity design, explores the development of corporate brand image visual identity intelligent system, and aims to design a set of visual identity system that is different from competitors in order to shape the enterprise. Distinctive brand image and improve its market competitiveness. This article first collected a large amount of information through the literature investigation method, and made a systematic and comprehensive introduction to fuzzy theory, visual recognition technology and related theoretical concepts of brand image, which laid a sufficient theoretical foundation for the later discussion of the application of fuzzy theory in the design of brand image visual recognition intelligent system; then the fuzzy theory algorithm is described in detail, a fuzzy neural network is proposed and applied to the design of the brand image visual recognition intelligent system, and the design experiment of the intelligent recognition system is carried out; finally, through the use of the specific case of KFC brand logo, the designed intelligent recognition system was tested, and it was found that the visual recognition intelligent system had an overall accuracy rate of 96.08% for the KFC brand logo. Among them, the accuracy rate of color recognition was the highest, 96.62%; comparing the changes in the output value of the training sample and the test sample, the output convergence effect of the color network is the best; through the comparison test of the BP neural network, the recognition effect of the fuzzy neural network is better.

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