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.

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.

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.

Conceptual short-term memory (CSTM) supports core claims of Christiansen and Chater

2016 ◽  
Vol 39 ◽  
Author(s):  
Mary C. Potter
Keyword(s):  
Working Memory ◽  
Rapid Serial Visual Presentation ◽  
Language Comprehension ◽  
Short Term Memory ◽  
Visual Presentation ◽  
Long Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Use Of Knowledge

AbstractRapid serial visual presentation (RSVP) of words or pictured scenes provides evidence for a large-capacity conceptual short-term memory (CSTM) that momentarily provides rich associated material from long-term memory, permitting rapid chunking (Potter 1993; 2009; 2012). In perception of scenes as well as language comprehension, we make use of knowledge that briefly exceeds the supposed limits of working memory.

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

Depth of Processing Differentially Affects Working Memory and Long-Term Memory

2007 ◽  
Author(s):  
Nathan S. Rose ◽  
Joel Myerson ◽  
Henry L. Roediger ◽  
Sandra Hale
Keyword(s):  
Working Memory ◽  
Long Term Memory ◽  
Depth Of Processing ◽  
Term Memory

Evidence for a single mechanism gating perceptual and long-term memory information into working memory

2020 ◽  
Author(s):  
Sam Verschooren ◽  
Yoav Kessler ◽  
Tobias Egner
Keyword(s):  
Working Memory ◽  
Long Term Memory ◽  
Sources Of Information ◽  
Term Memory ◽  
Source Selection ◽  
Single Mechanism ◽  
Gating Mechanism ◽  
Opening And Closing ◽  
Selection Of

An influential view of working memory (WM) holds that its’ contents are controlled by a selective gating mechanism that allows for relevant perceptual information to enter WM when opened, but shields WM contents from interference when closed. In support of this idea, prior studies using the reference-back paradigm have established behavioral costs for opening and closing the gate between perception and WM. WM also frequently requires input from long-term memory (LTM), but it is currently unknown whether a similar gate controls the selection of LTM representations into WM, and how WM gating of perceptual vs. LTM sources of information relate to each other. To address these key theoretical questions, we devised a novel version of the reference-back paradigm, where participants switched between gating perceptual and LTM information into WM. We observed clear evidence for gate opening and closing costs in both cases. Moreover, the pattern of costs associated with gating and source-switching indicated that perceptual and LTM information is gated into WM via a single gate, and rely on a shared source-selection mechanism. These findings extend current models of WM gating to encompass LTM information, and outline a new functional WM architecture.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

Ion-gating synaptic transistors with long-term synaptic weight modulation

2021 ◽  
Vol 9 (16) ◽  
pp. 5396-5402
Author(s):  
Youngjun Park ◽  
Min-Kyu Kim ◽  
Jang-Sik Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Weight ◽  
Recognition Accuracy ◽  
Artificial Neural ◽  
High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

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