Literature DB >> 26180122

Neural circuit basis of visuo-spatial working memory precision: a computational and behavioral study.

Rita Almeida1, João Barbosa2, Albert Compte3.   

Abstract

The amount of information that can be retained in working memory (WM) is limited. Limitations of WM capacity have been the subject of intense research, especially in trying to specify algorithmic models for WM. Comparatively, neural circuit perspectives have barely been used to test WM limitations in behavioral experiments. Here we used a neuronal microcircuit model for visuo-spatial WM (vsWM) to investigate memory of several items. The model assumes that there is a topographic organization of the circuit responsible for spatial memory retention. This assumption leads to specific predictions, which we tested in behavioral experiments. According to the model, nearby locations should be recalled with a bias, as if the two memory traces showed attraction or repulsion during the delay period depending on distance. Another prediction is that the previously reported loss of memory precision for an increasing number of memory items (memory load) should vanish when the distances between items are controlled for. Both predictions were confirmed experimentally. Taken together, our findings provide support for a topographic neural circuit organization of vsWM, they suggest that interference between similar memories underlies some WM limitations, and they put forward a circuit-based explanation that reconciles previous conflicting results on the dependence of WM precision with load.
Copyright © 2015 the American Physiological Society.

Entities:  

Keywords:  attractor model; capacity; precision; short-term memory; working memory

Mesh:

Year:  2015        PMID: 26180122      PMCID: PMC4571767          DOI: 10.1152/jn.00362.2015

Source DB:  PubMed          Journal:  J Neurophysiol        ISSN: 0022-3077            Impact factor:   2.714


  44 in total

1.  Sustained activity in topographic areas of human posterior parietal cortex during memory-guided saccades.

Authors:  Denis Schluppeck; Clayton E Curtis; Paul W Glimcher; David J Heeger
Journal:  J Neurosci       Date:  2006-05-10       Impact factor: 6.167

2.  Variability in encoding precision accounts for visual short-term memory limitations.

Authors:  Ronald van den Berg; Hongsup Shin; Wen-Chuang Chou; Ryan George; Wei Ji Ma
Journal:  Proc Natl Acad Sci U S A       Date:  2012-05-11       Impact factor: 11.205

3.  Discrete fixed-resolution representations in visual working memory.

Authors:  Weiwei Zhang; Steven J Luck
Journal:  Nature       Date:  2008-04-02       Impact factor: 49.962

4.  Ghosts in the machine: memory interference from the previous trial.

Authors:  Charalampos Papadimitriou; Afreen Ferdoash; Lawrence H Snyder
Journal:  J Neurophysiol       Date:  2014-11-05       Impact factor: 2.714

5.  A biophysical model of multiple-item working memory: a computational and neuroimaging study.

Authors:  J Macoveanu; T Klingberg; J Tegnér
Journal:  Neuroscience       Date:  2006-06-13       Impact factor: 3.590

6.  Distortions in recall from visual memory: two classes of attractors at work.

Authors:  Jie Huang; Robert Sekuler
Journal:  J Vis       Date:  2010-02-24       Impact factor: 2.240

7.  Homogeneity computation: how interitem similarity in visual short-term memory alters recognition.

Authors:  Shivakumar Viswanathan; Daniel R Perl; Kristina M Visscher; Michael J Kahana; Robert Sekuler
Journal:  Psychon Bull Rev       Date:  2010-02

8.  Dynamic shifts of limited working memory resources in human vision.

Authors:  Paul M Bays; Masud Husain
Journal:  Science       Date:  2008-08-08       Impact factor: 47.728

9.  The Influence of Similarity on Visual Working Memory Representations.

Authors:  Po-Han Lin; Steven J Luck
Journal:  Vis cogn       Date:  2009-04

10.  A dynamic neural field model of visual working memory and change detection.

Authors:  Jeffrey S Johnson; John P Spencer; Steven J Luck; Gregor Schöner
Journal:  Psychol Sci       Date:  2009-05-01
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  26 in total

1.  A Flexible Model of Working Memory.

Authors:  Flora Bouchacourt; Timothy J Buschman
Journal:  Neuron       Date:  2019-05-15       Impact factor: 17.173

2.  Chunking as a rational strategy for lossy data compression in visual working memory.

Authors:  Matthew R Nassar; Julie C Helmers; Michael J Frank
Journal:  Psychol Rev       Date:  2018-07       Impact factor: 8.934

3.  Slot-like capacity and resource-like coding in a neural model of multiple-item working memory.

Authors:  Dominic Standage; Martin Paré
Journal:  J Neurophysiol       Date:  2018-06-27       Impact factor: 2.714

4.  Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not.

Authors:  Mikael Lundqvist; Pawel Herman; Earl K Miller
Journal:  J Neurosci       Date:  2018-08-08       Impact factor: 6.167

5.  Synaptic efficacy shapes resource limitations in working memory.

Authors:  Nikhil Krishnan; Daniel B Poll; Zachary P Kilpatrick
Journal:  J Comput Neurosci       Date:  2018-03-15       Impact factor: 1.621

6.  Stability of working memory in continuous attractor networks under the control of short-term plasticity.

Authors:  Alexander Seeholzer; Moritz Deger; Wulfram Gerstner
Journal:  PLoS Comput Biol       Date:  2019-04-19       Impact factor: 4.475

7.  A dynamic neural field model of continuous input integration.

Authors:  Weronika Wojtak; Stephen Coombes; Daniele Avitabile; Estela Bicho; Wolfram Erlhagen
Journal:  Biol Cybern       Date:  2021-08-21       Impact factor: 2.086

Review 8.  Working Memory 2.0.

Authors:  Earl K Miller; Mikael Lundqvist; André M Bastos
Journal:  Neuron       Date:  2018-10-24       Impact factor: 17.173

9.  Why Does the Neocortex Need the Cerebellum for Working Memory?

Authors:  Heike Stein
Journal:  J Neurosci       Date:  2021-07-28       Impact factor: 6.167

Review 10.  Balancing Flexibility and Interference in Working Memory.

Authors:  Timothy J Buschman
Journal:  Annu Rev Vis Sci       Date:  2021-06-03       Impact factor: 7.745

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