Revisiting Memristor Properties

2020 ◽  
Vol 30 (12) ◽  
pp. 2050172
Author(s):  
Ling Chen ◽  
Zhilong He ◽  
Chuandong Li ◽  
Shiping Wen ◽  
Yiran Chen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Experience ◽  
Time Dependent ◽  
Long Term Memory ◽  
Term Memory ◽  
Dependent Plasticity ◽  
Memory Property ◽  
Rate Dependent

Memristor is a natural synapse because of its nanoscale and memory property, which influences the performance of memristive artificial neural networks. A three-variable memristor model is simplified with 15 kinds of properties, including the learning experience, the forgetting curve, the spiking time-dependent plasticity (STDP), the spiking rate dependent plasticity (SRDP), and the integration property. Through the analysis of the model, one more unobserved property called pseudo-polarity reversibility property is predicted by assuming the long-term memory is independent of memductance.

scholarly journals Perceptual Generalization and Context in a Network Memory Inspired Long-Term Memory for Artificial Cognition

2019 ◽  
Vol 29 (06) ◽  
pp. 1850053 ◽  
Author(s):  
Richard J. Duro ◽  
Jose A. Becerra ◽  
Juan Monroy ◽  
Francisco Bellas
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Long Term Memory ◽  
Cognitive Architectures ◽  
Term Memory ◽  
Associative Structure ◽  
Complementary System ◽  
Types Of Knowledge ◽  
Artificial Neural

In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what different authors call “automation” of what is learnt, as a complementary system to more common prospective functions. The LTM proposed here provides for a relational storage of knowledge nuggets given the form of artificial neural networks (ANNs) that is representative of the contexts in which they are relevant in a configural associative structure. It also addresses the problem of continuous perceptual spaces and the task- and context-related generalization or categorization of perceptions in an autonomous manner within the embodied sensorimotor apparatus of the robot. These issues are analyzed and a solution is proposed through the introduction of two new types of knowledge nuggets: P-nodes representing perceptual classes and C-nodes representing contexts. The approach is studied and its performance evaluated through its implementation and application to a real robotic experiment.

scholarly journals The Cognitive Philosophy of Communication

Philosophies ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 39 ◽  
Author(s):  
Trond A. Tjøstheim ◽  
Andreas Stephens ◽  
Andrey Anikin ◽  
Arthur Schwaninger
Keyword(s):  
Neural Networks ◽  
Working Memory ◽  
Artificial Neural Networks ◽  
Natural Phenomenon ◽  
Long Term Memory ◽  
Term Memory ◽  
Numerous Species ◽  
Philosophy Of Communication ◽  
Artificial Neural

Numerous species use different forms of communication in order to successfully interact in their respective environment. This article seeks to elucidate limitations of the classical conduit metaphor by investigating communication from the perspectives of biology and artificial neural networks. First, communication is a biological natural phenomenon, found to be fruitfully grounded in an organism’s embodied structures and memory system, where specific abilities are tied to procedural, semantic, and episodic long-term memory as well as to working memory. Second, the account explicates differences between non-verbal and verbal communication and shows how artificial neural networks can communicate by means of ontologically non-committal modelling. This approach enables new perspectives of communication to emerge regarding both sender and receiver. It is further shown that communication features gradient properties that are plausibly divided into a reflexive and a reflective form, parallel to knowledge and reflection.

Acute Exercise and Sustained Attention on Memory Function

2020 ◽  
Vol 44 (3) ◽  
pp. 326-332
Author(s):  
Audreaiona Waters ◽  
Liye Zou ◽  
Myungjin Jung ◽  
Qian Yu ◽  
Jingyuan Lin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sustained Attention ◽  
Acute Exercise ◽  
Short Term Memory ◽  
Memory Function ◽  
Long Term Memory ◽  
Short Term ◽  
Attention Task ◽  
Term Memory

Objective: Sustained attention is critical for various activities of daily living, including engaging in health-enhancing behaviors and inhibition of health compromising behaviors. Sustained attention activates neural networks involved in episodic memory function, a critical cognition for healthy living. Acute exercise has been shown to activate these same neural networks. Thus, it is plausible that engaging in a sustained attention task and engaging in a bout of acute exercise may have an additive effect in enhancing memory function, which was the purpose of this experiment. Methods: 23 young adults (Mage = 20.7 years) completed 2 visits, with each visit occurring approximately 24 hours apart, in a counterbalanced order, including: (1) acute exercise with sustained attention, and (2) sustained attention only. Memory was assessed using a word-list paradigm and included a short- and long-term memory assessment. Sustained attention was induced via a sustained attention to response task (SART). Acute exercise involved a 15-minute bout of moderate-intensity exercise. Results: Short-term memory performance was significantly greater than long-term memory, Mdiff = 1.86, p < .001, and short-term memory for Exercise with Sustained Attention was significantly greater than short-term memory for Sustained Attention Only, Mdiff = 1.50, p = .01. Conclusion: Engaging in an acute bout of exercise before a sustained attention task additively influenced short-term memory function.

scholarly journals Neuronal reactivation during post-learning sleep consolidates long-term memory in Drosophila

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Ugur Dag ◽  
Zhengchang Lei ◽  
Jasmine Q Le ◽  
Allan Wong ◽  
Daniel Bushey ◽  
...  
Keyword(s):  
Memory Consolidation ◽  
Dopaminergic Neurons ◽  
Learning Experience ◽  
Long Term Memory ◽  
Term Memory ◽  
Long Term Storage ◽  
Memory Acquisition ◽  
Underlying Mechanisms ◽  
Term Storage

Animals consolidate some, but not all, learning experiences into long-term memory. Across the animal kingdom, sleep has been found to have a beneficial effect on the consolidation of recently formed memories into long-term storage. However, the underlying mechanisms of sleep dependent memory consolidation are poorly understood. Here, we show that consolidation of courtship long-term memory in Drosophila is mediated by reactivation during sleep of dopaminergic neurons that were earlier involved in memory acquisition. We identify specific fan-shaped body neurons that induce sleep after the learning experience and activate dopaminergic neurons for memory consolidation. Thus, we provide a direct link between sleep, neuronal reactivation of dopaminergic neurons, and memory consolidation.

Time-dependent effects of cycloheximide on long-term memory in the cuttlefish

2003 ◽  
Vol 75 (1) ◽  
pp. 141-146 ◽  
Author(s):  
Véronique Agin ◽  
Raymond Chichery ◽  
Eric Maubert ◽  
Marie-Paule Chichery
Keyword(s):  
Time Dependent ◽  
Long Term Memory ◽  
Term Memory
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