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Diego Mendoza-Halliday, Santiago Torres, Robert Desimone, Julio Martinez-Trujillo; Functional and anatomical characterization of visual working memory coding. Journal of Vision 2018;18(10):106. doi: 10.1167/18.10.106.
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© ARVO (1962-2015); The Authors (2016-present)
Numerous studies using a variety of experimental methods have shown evidence of neural activity encoding visual working memory (VWM) representations across a wide range of cortical areas. This has led to a major controversy regarding which areas directly subserve VWM maintenance, and what role each area plays. Here we describe functional and anatomical properties of the neuronal code for VWM based on our results from neurophysiological studies in monkeys, and propose several underlying principles of brain organization that may help resolve such controversy: (i) Sustained activity encoding VWM emerges in visual association areas immediately downstream from early visual cortex, and is also present in executive areas such as the lateral prefrontal cortex (LPFC). (ii) Early visual areas exclusively encode sensory inputs; however, their synaptic activity may be modulated by higher-level areas encoding VWM, putatively influencing sensory processing. (iii) The cortical architecture of areas subserving VWM maintenance is characterized by more excitatory and less inhibitory neurons than that of areas exclusively subserving sensory processing. (iv) Population representation strength remains relatively stable throughout the memory period, yet the underlying rate code is dynamic. (v) In LPFC, coding functions vary widely across neurons, with some neurons encoding perceived and memorized visual features to similar degrees and others preferentially or exclusively encoding either one. Our results suggest that VWM coding is neither a ubiquitous property across cortical areas nor an exclusive property of high-level executive areas alone; instead, it is carried out by a finite network of areas/neurons with specialized functional and anatomical properties.
Meeting abstract presented at VSS 2018
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