Adaptation supports short-term memory in a visual change detection task.

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of th...

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Autores principales:	Brian Hu, Marina E Garrett, Peter A Groblewski, Douglas R Ollerenshaw, Jiaqi Shang, Kate Roll, Sahar Manavi, Christof Koch, Shawn R Olsen, Stefan Mihalas
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/4d36df7b84fd4ca4b6541beb260bd301
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id	oai:doaj.org-article:4d36df7b84fd4ca4b6541beb260bd301
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spelling	oai:doaj.org-article:4d36df7b84fd4ca4b6541beb260bd3012021-12-02T19:57:46ZAdaptation supports short-term memory in a visual change detection task.1553-734X1553-735810.1371/journal.pcbi.1009246https://doaj.org/article/4d36df7b84fd4ca4b6541beb260bd3012021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009246https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.Brian HuMarina E GarrettPeter A GroblewskiDouglas R OllerenshawJiaqi ShangKate RollSahar ManaviChristof KochShawn R OlsenStefan MihalasPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009246 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Brian Hu Marina E Garrett Peter A Groblewski Douglas R Ollerenshaw Jiaqi Shang Kate Roll Sahar Manavi Christof Koch Shawn R Olsen Stefan Mihalas Adaptation supports short-term memory in a visual change detection task.
description	The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process.
format	article
author	Brian Hu Marina E Garrett Peter A Groblewski Douglas R Ollerenshaw Jiaqi Shang Kate Roll Sahar Manavi Christof Koch Shawn R Olsen Stefan Mihalas
author_facet	Brian Hu Marina E Garrett Peter A Groblewski Douglas R Ollerenshaw Jiaqi Shang Kate Roll Sahar Manavi Christof Koch Shawn R Olsen Stefan Mihalas
author_sort	Brian Hu
title	Adaptation supports short-term memory in a visual change detection task.
title_short	Adaptation supports short-term memory in a visual change detection task.
title_full	Adaptation supports short-term memory in a visual change detection task.
title_fullStr	Adaptation supports short-term memory in a visual change detection task.
title_full_unstemmed	Adaptation supports short-term memory in a visual change detection task.
title_sort	adaptation supports short-term memory in a visual change detection task.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4d36df7b84fd4ca4b6541beb260bd301
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Adaptation supports short-term memory in a visual change detection task.

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