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Noise in neural populations accounts for errors in working memory.

Bays, PM; (2014) Noise in neural populations accounts for errors in working memory. J Neurosci , 34 (10) 3632 - 3645. 10.1523/JNEUROSCI.3204-13.2014. Green open access

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Abstract

Errors in short-term memory increase with the quantity of information stored, limiting the complexity of cognition and behavior. In visual memory, attempts to account for errors in terms of allocation of a limited pool of working memory resources have met with some success, but the biological basis for this cognitive architecture is unclear. An alternative perspective attributes recall errors to noise in tuned populations of neurons that encode stimulus features in spiking activity. I show that errors associated with decreasing signal strength in probabilistically spiking neurons reproduce the pattern of failures in human recall under increasing memory load. In particular, deviations from the normal distribution that are characteristic of working memory errors and have been attributed previously to guesses or variability in precision are shown to arise as a natural consequence of decoding populations of tuned neurons. Observers possess fine control over memory representations and prioritize accurate storage of behaviorally relevant information, at a cost to lower priority stimuli. I show that changing the input drive to neurons encoding a prioritized stimulus biases population activity in a manner that reproduces this empirical tradeoff in memory precision. In a task in which predictive cues indicate stimuli most probable for test, human observers use the cues in an optimal manner to maximize performance, within the constraints imposed by neural noise.

Type: Article
Title: Noise in neural populations accounts for errors in working memory.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1523/JNEUROSCI.3204-13.2014
Publisher version: http://dx.doi.org/10.1523/JNEUROSCI.3204-13.2014
Language: English
Additional information: © 2014 Bays This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
Keywords: Poisson noise, divisive normalization, neural gain, population coding, resource, short term memory
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/1424279
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