Nested-Clique Network Model of Neural Associative Memory

2017 ◽  
Vol 29 (6) ◽  
pp. 1681-1695 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Matthew G. Parker
Keyword(s):  
Associative Memory ◽  
Network Model ◽  
Associative Memories ◽  
Learning Capacity ◽  
Retrieval Rate ◽  
Graph Structures ◽  
Clique Graph ◽  
Simulation Results ◽  
Local Coding ◽  
Gb Model

Clique-based neural associative memories introduced by Gripon and Berrou (GB), have been shown to have good performance, and in our previous work we improved the learning capacity and retrieval rate by local coding and precoding in the presence of partial erasures. We now take a step forward and consider nested-clique graph structures for the network. The GB model stores patterns as small cliques, and we here replace these by nested cliques. Simulation results show that the nested-clique structure enhances the clique-based model.

Clique-Based Neural Associative Memories with Local Coding and Precoding

2016 ◽  
Vol 28 (8) ◽  
pp. 1553-1573 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Matthew G. Parker ◽  
Zahra Ferdosi ◽  
Mohammad H. Tadayon
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Coding Theory ◽  
Simple Graph ◽  
Graph Representation ◽  
Associative Memories ◽  
Additive Codes ◽  
Partial Error ◽  
Pattern Retrieval ◽  
Local Coding

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

scholarly journals A Study of Associative Memories with Hopfield neural network Model for handwritten character recognition

2018 ◽  
Vol 3 (01) ◽  
Author(s):  
Sandeep Kumar ◽  
Manu Pratap Singh
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Network Model ◽  
Neural Network Model ◽  
Character Recognition ◽  
Hopfield Neural Network ◽  
Recurrent Network ◽  
Associative Memories ◽  
Bidirectional Associative Memory ◽  
Content Addressable Memory

Neural network is the most important model which has been studied in past decades by several researchers. Hopfield model is one of the network model proposed by J.J. Hopfield that describes the organization of neurons in such a way that they function as associative memory or also called content addressable memory. This is a recurrent network similar to recurrent layer of the hamming network but which can effectively perform the operation of both layer hamming network. The design of recurrent network has always been interesting problems to research and a lot of work is going on present application. In present paper we will discuss about the design of Hopfield Neural Network (HNNs), bidirectional associative memory (BAMs) and multidirectional associative memory (MAMs) for handwritten characters recognition. Recognized characters are Hindi alphabets.

scholarly journals The Role of Visual Association Cortex in Associative Memory Formation across Development

2018 ◽  
Vol 30 (3) ◽  
pp. 365-380 ◽  
Author(s):  
Maya L. Rosen ◽  
Margaret A. Sheridan ◽  
Kelly A. Sambrook ◽  
Matthew R. Peverill ◽  
Andrew N. Meltzoff ◽  
...  
Keyword(s):  
Associative Memory ◽  
Visual Processing ◽  
Memory Performance ◽  
Neural Mechanism ◽  
Association Cortex ◽  
Associative Memories ◽  
Associate Learning ◽  
Visual Association Cortex ◽  
Age Related

Associative learning underlies the formation of new episodic memories. Associative memory improves across development, and this age-related improvement is supported by the development of the hippocampus and pFC. Recent work, however, additionally suggests a role for visual association cortex in the formation of associative memories. This study investigated the role of category-preferential visual processing regions in associative memory across development using a paired associate learning task in a sample of 56 youths (age 6–19 years). Participants were asked to bind an emotional face with an object while undergoing fMRI scanning. Outside the scanner, participants completed a memory test. We first investigated age-related changes in neural recruitment and found linear age-related increases in activation in lateral occipital cortex and fusiform gyrus, which are involved in visual processing of objects and faces, respectively. Furthermore, greater activation in these visual processing regions was associated with better subsequent memory for pairs over and above the effect of age and of hippocampal and pFC activation on performance. Recruitment of these visual processing regions mediated the association between age and memory performance, over and above the effects of hippocampal activation. Taken together, these findings extend the existing literature to suggest that greater recruitment of category-preferential visual processing regions during encoding of associative memories is a neural mechanism explaining improved memory across development.

Method to enhance traffic capacity for multilayer networks

2020 ◽  
Vol 34 (13) ◽  
pp. 2050140
Author(s):  
Yongqiang Zhang ◽  
Yaming Li ◽  
Min Li ◽  
Jinlong Ma
Keyword(s):  
Network Model ◽  
High Speed ◽  
Great Influence ◽  
Packet Transmission ◽  
Multilayer Network ◽  
Transmission Effect ◽  
Traffic Capacity ◽  
Network Load ◽  
Simulation Results ◽  
High Degree

The network structure acquires great influence on the traffic capacity for complex network. Since the nodes with high degree usually bear large load in the process of packet transmission, we propose a new multilayer network model which can balance the load of low-speed and high-speed layers. The simulation results show that compared with the randomly select nodes multilayer network model, the new network model makes the multilayer network load more balanced, thereby enhancing traffic capacity of the network and reducing the possibility of congestion. This network model gives full play to the transmission advantages of the high-speed layer of the multilayer network, and can reduce the consumption of resources while achieving the same transmission effect, which is of guiding significance for the planning of network lines.

SIMULATING NETWORK STRUCTURES USING BERNOULLI'S PRINCIPLE

2012 ◽  
Vol 15 (05) ◽  
pp. 1250032 ◽  
Author(s):  
YICHEOL HAN ◽  
STEPHAN J. GOETZ ◽  
JEONGJAE LEE ◽  
SEONGSOO YOON
Keyword(s):  
Network Model ◽  
Network Structure ◽  
Flow Resistance ◽  
Network Structures ◽  
Model Based ◽  
Network Connections ◽  
Network Properties ◽  
Simulation Results

Using the fact that connections between vertices of a network often represent directed and weighted flows, we apply hydraulic principles to develop novel insights into network structure and growth. We develop a network model based on Bernoulli's principle and use it to analyze changes in network properties. Simulation results show that velocity of flow, resistance, fitness and existing connections in a system determine network connections of a vertex as well as overall network structure. We demonstrate how network structure is affected by changes in velocity and resistance, and how one vertex can monopolize connections within a network. Using Bernoulli's principle, we are able to independently reproduce key results in the network literature.

Neural network associative memories with local coding

2015 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Zahra Ferdosi ◽  
Matthew G. Parker ◽  
Mohammad H. Tadayon
Keyword(s):  
Neural Network ◽  
Associative Memories ◽  
Local Coding

scholarly journals Associative Models for Storing and Retrieving Concept Lattices

2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
María Elena Acevedo ◽  
Cornelio Yáñez-Márquez ◽  
Marco Antonio Acevedo
Keyword(s):  
Associative Memory ◽  
Concept Lattice ◽  
Human Being ◽  
Associative Memories ◽  
Associative Model ◽  
Bidirectional Associative Memory ◽  
Perfect Recall ◽  
Concept Lattices ◽  
Alpha Beta ◽  
Associative Models

Alpha-beta bidirectional associative memories are implemented for storing concept lattices. We use Lindig's algorithm to construct a concept lattice of a particular context; this structure is stored into an associative memory just as a human being does, namely, associating patterns. Bidirectionality and perfect recall of Alpha-Beta associative model make it a great tool to store a concept lattice. In the learning phase, objects and attributes obtained from Lindig's algorithm are associated by Alpha-Beta bidirectional associative memory; in this phase the data is stored. In the recalling phase, the associative model allows to retrieve objects from attributes or vice versa. Our model assures the recalling of every learnt concept.

Real-Time Associative Memory–Based Rear-End Collision Warning System

2017 ◽  
Vol 2621 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Donghoun Lee ◽  
Sehyun Tak ◽  
Sungjin Park ◽  
Hwasoo Yeo
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Associative Memory ◽  
Intelligent Transportation System ◽  
Warning System ◽  
Time Interval ◽  
Associative Memories ◽  
Collision Warning ◽  
Sampling Ratio ◽  
Artificial Neural Network Ann

In the intelligent transportation system field, there has been a growing interest in developing collision warning systems based on artificial neural network (ANN) techniques in an effort to address several issues associated with parametric approaches. Previous ANN-based collision warning algorithms were generally based on predetermined associative memories derived before driving. Because collision risk is highly related to the current traffic situation, such as traffic state transition from free flow to congestion, however, updating associative memory in real time should be considered. To improve further the performance of the warning system, a systemic architecture is proposed to implement the multilayer perceptron neural network–based rear-end collision warning system (MCWS), which updates the associative memory with the vehicle distance sensor and smartphone data in a cloud computing environment. For the practical use of the proposed MCWS, its collision warning accuracy is evaluated with respect to various time intervals for updating the associative memories and market penetration rates. Results show that the MCWS exhibits a steady improvement in its warning performance as the time interval decreases, whereas the MCWS works more efficiently as the sampling ratio increases overall. When the sampling ratio reaches 50%, the MCWS shows a particularly stable warning accuracy, regardless of the time interval. These findings suggest that the MCWS has great potential to provide an acceptable level of warning accuracy for practical use, as it can obtain the well-trained associative memories reflecting current traffic situations by using information from widespread smartphones.

scholarly journals Inheritance of associative memories in C. elegans nematodes

2020 ◽  
Author(s):  
Noa Deshe ◽  
Yifat Eliezer ◽  
Lihi Hoch ◽  
Eyal Itskovits ◽  
Shachaf Ben-Ezra ◽  
...  
Keyword(s):  
Associative Memory ◽  
Stress Responses ◽  
Rna Binding ◽  
Associative Memories ◽  
C Elegans ◽  
Fitness Advantage ◽  
Neuropeptide Secretion ◽  
Two Generations ◽  
Chemosensory Neuron ◽  
Systemic Stress

SummaryThe notion that associative memories may be transmitted across generations is intriguing, yet controversial. Here, we trained C. elegans nematodes to associate an odorant with stressful starvation conditions, and surprisingly, this associative memory was evident two generations down of the trained animals. The inherited memory endowed the progeny with a fitness advantage, as memory reactivation induced rapid protective stress responses that allowed the animals to prepare in advance for an impending adversity. Sperm, but not oocytes, transmitted the associative memory, and the inheritance required H3K9 and H3K36 methylations, the small RNA-binding Argonaute NRDE-3, and intact neuropeptide secretion. Remarkably, activation of a single chemosensory neuron sufficed to induce a serotonin-mediated systemic stress response in both the parental trained generation and in its progeny. These findings challenge long-held concepts by establishing that associative memories may indeed be transferred across generations.

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