Literature DB >> 9759349

Modeling memory: what do we learn from attractor neural networks?

N Brunel1, J P Nadal.   

Abstract

In this paper we summarize some of the main contributions of models of recurrent neural networks with associative memory properties. We compare the behavior of these attractor neural networks with empirical data from both physiology and psychology. This type of network could be used in models with more complex functions.

Mesh:

Year:  1998        PMID: 9759349     DOI: 10.1016/s0764-4469(97)89830-7

Source DB:  PubMed          Journal:  C R Acad Sci III        ISSN: 0764-4469


  3 in total

1.  Stabilization of memory States by stochastic facilitating synapses.

Authors:  Paul Miller
Journal:  J Math Neurosci       Date:  2013-12-06       Impact factor: 1.300

2.  Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity.

Authors:  Benjamin Ballintyn; Benjamin Shlaer; Paul Miller
Journal:  J Comput Neurosci       Date:  2019-05-27       Impact factor: 1.621

3.  Stimulus number, duration and intensity encoding in randomly connected attractor networks with synaptic depression.

Authors:  Paul Miller
Journal:  Front Comput Neurosci       Date:  2013-05-09       Impact factor: 2.380

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.