Simultaneous representation of sensory and mnemonic information in human visual cortex

Traversing sensory environments requires keeping relevant information in mind while simultaneously processing new inputs. Visual information is kept in working memory via feature selective responses in early visual cortex, but recent work had suggested that new sensory inputs wipe out this information. Here we show region-wide multiplexing abilities in classic sensory areas, with population-level response patterns in visual cortex representing the contents of working memory concurrently with new sensory inputs.

[1]  M. Stokes.  ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework , 2015, Trends in Cognitive Sciences.

[2]  John-Dylan Haynes,et al.  Visual Working Memory Enhances the Neural Response to Matching Visual Input , 2017, The Journal of Neuroscience.

[3]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[4]  F. Tong,et al.  Decoding reveals the contents of visual working memory in early visual areas , 2009, Nature.

[5]  Rosanne L Rademaker,et al.  The impact of interference on short-term memory for visual orientation. , 2015, Journal of experimental psychology. Human perception and performance.

[6]  Mark W. Woolrich,et al.  FSL , 2012, NeuroImage.

[7]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[8]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[9]  Matthew W Self,et al.  Layer-specificity in the effects of attention and working memory on activity in primary visual cortex , 2017, Nature Communications.

[10]  Adam C. Riggall,et al.  The Relationship between Working Memory Storage and Elevated Activity as Measured with Functional Magnetic Resonance Imaging , 2012, The Journal of Neuroscience.

[11]  Julio C. Martinez-Trujillo,et al.  Sharp emergence of feature-selective sustained activity along the dorsal visual pathway , 2014, Nature Neuroscience.

[12]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[13]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[14]  Adrian T. Lee,et al.  fMRI of human visual cortex , 1994, Nature.

[15]  T. Pasternak,et al.  Directional Signals in the Prefrontal Cortex and in Area MT during a Working Memory for Visual Motion Task , 2006, The Journal of Neuroscience.

[16]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[17]  John T. Serences,et al.  Attention modulates spatial priority maps in the human occipital, parietal and frontal cortices , 2013, Nature Neuroscience.

[18]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[19]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[20]  John T Serences,et al.  Neural mechanisms of information storage in visual short-term memory , 2016, Vision Research.

[21]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[22]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[23]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[24]  Philipp Berens,et al.  CircStat: AMATLABToolbox for Circular Statistics , 2009, Journal of Statistical Software.

[25]  Edward F. Ester,et al.  PSYCHOLOGICAL SCIENCE Research Article Stimulus-Specific Delay Activity in Human Primary Visual Cortex , 2022 .

[26]  Yaoda Xu,et al.  Decoding the content of visual short-term memory under distraction in occipital and parietal areas , 2015, Nature Neuroscience.

[27]  Edward Awh,et al.  A Neural Measure of Precision in Visual Working Memory , 2013, Journal of Cognitive Neuroscience.

[28]  John-Dylan Haynes,et al.  Decoding the Contents of Visual Short-Term Memory from Human Visual and Parietal Cortex , 2012, The Journal of Neuroscience.

[29]  John T. Serences,et al.  Visual attention mitigates information loss in small- and large-scale neural codes , 2015, Trends in Cognitive Sciences.

[30]  Paul M Bays,et al.  The precision of visual working memory is set by allocation of a shared resource. , 2009, Journal of vision.

[31]  Lotfi B Merabet,et al.  Visual Topography of Human Intraparietal Sulcus , 2007, The Journal of Neuroscience.

[32]  James W Bisley,et al.  Activity of neurons in cortical area MT during a memory for motion task. , 2004, Journal of neurophysiology.

[33]  E. Callaway,et al.  Parallel processing strategies of the primate visual system , 2009, Nature Reviews Neuroscience.

[34]  D. Heeger,et al.  Decoding and Reconstructing Color from Responses in Human Visual Cortex , 2009, The Journal of Neuroscience.