Associative memory based on synchronized firing of spiking neurons with time-delayed interactions

1998 ◽  
Vol 58 (3) ◽  
pp. 3628-3639 ◽  
Author(s):  
Masahiko Yoshioka ◽  
Masatoshi Shiino
Keyword(s):  
Associative Memory ◽  
Spiking Neurons

scholarly journals Model architecture for associative memory in a neural network of spiking neurons

2012 ◽  
Vol 391 (3) ◽  
pp. 843-848 ◽  
Author(s):  
Everton J. Agnes ◽  
Rubem Erichsen ◽  
Leonardo G. Brunnet
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Spiking Neurons

Associative memory in networks of spiking neurons

2001 ◽  
Vol 14 (6-7) ◽  
pp. 825-834 ◽  
Author(s):  
Friedrich T. Sommer ◽  
Thomas Wennekers
Keyword(s):  
Associative Memory ◽  
Spiking Neurons

scholarly journals HOW A PEAK CAN BE SEARCHED FOR IN AN ALMOST EVERYWHERE FLATLAND OF ALTITUDE ZERO? — TINY FLAT ISLAND IN HUGE LAKE

2014 ◽  
pp. 32-37
Author(s):  
Akira Imada
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Configuration Space ◽  
Fitness Landscape ◽  
Spiking Neurons ◽  
The Other ◽  
Memory Model ◽  
Almost Everywhere

We are exploring a weight configuration space searching for solutions to make our neural network with spiking neurons do some tasks. For the task of simulating an associative memory model, we have already known one such solution — a weight configuration learned a set of patterns using Hebb’s rule, and we guess we have many others which we have not known so far. In searching for such solutions, we observed that the so-called fitness landscape was almost everywhere completely flatland of altitude zero in which the Hebbian weight configuration is the only unique peak, and in addition, the sidewall of the peak is not gradient at all. In such circumstances how could we search for the other peaks? This paper is a call for challenges to the problem.

scholarly journals Connection Strategies in Associative Memory Models with Spiking and Non-spiking Neurons

2009 ◽  
pp. 42-51
Author(s):  
Weiliang Chen ◽  
Reinoud Maex ◽  
Rod Adams ◽  
Volker Steuber ◽  
Lee Calcraft ◽  
...  
Keyword(s):  
Associative Memory ◽  
Memory Models ◽  
Spiking Neurons

scholarly journals Associative memory in a network of ‘spiking’ neurons

1992 ◽  
Vol 3 (2) ◽  
pp. 139-164 ◽  
Author(s):  
Wulfram Gerstner ◽  
J Leo van Hemmen
Keyword(s):  
Associative Memory ◽  
Spiking Neurons

scholarly journals A bidirectional associative memory based on cortical spiking neurons using temporal coding

2021 ◽  
Author(s):  
Masood Zamani
Keyword(s):  
Neural Networks ◽  
Associative Memory ◽  
Action Potentials ◽  
Storage Capacity ◽  
Spike Timing ◽  
Temporal Coding ◽  
Spiking Neurons ◽  
Biological Plausibility ◽  
Bidirectional Associative Memory ◽  
Coding Scheme

In this thesis, we proposed a spiking bidirectional associative memory (BAM) using temporal coding. The information processing in biological neurons is beyond of[sic] that applied in the current Artificial Neural Networks (ANNs). The coding scheme used in ANNs known as “mean firing rate” cannot answer the fast and complex computations occurring in the cortex. In biological neural networks the information is coded and processed based on the timing of action potentials. To improve the biological plausibility of the standard BAM, we employed spiking neurons for its processing units, and information is presented to the BAM in the form of temporal coding. The neurons employed in the model are heterogeneous, and being able to generate various spike-timing patterns. Genetic Algorithm and Co-evolution are used for training, and the experiment results of the proposed BAM are compared to those of the standard BAM. The results show improvements in recall, storage capacity and convergence which are of interest to design a BAM.

Sign in / Sign up

Export Citation Format

Share Document