@article {3466, title = {Differences in dynamic and static coding within different subdivision of the prefrontal cortex}, year = {2017}, month = {11/2017}, address = {Washington, DC}, abstract = {

A longstanding question in neuroscience concerns what is the neural basis underlying working memory. Early work showed that neurons in the prefrontal cortex (PFC) hold information in working memory by having sustained firing rates for extended periods of time, while more recent work has shown that many neurons in the PFC appear to be selective for shorter periods of time and thus information in working memory is contained in a dynamic population code (Meyers et al. 2008, 2012, Stokes et al. 2013). As more results have accumulated, it has become increasingly clear that different studies are leading to different results, with some studies showing predominantly static codes, while other show primarily dynamic codes (King and Dehaene, 2014), however it remains unclear what is leading to these different findings. One possibility is that different brain regions code information differently, and that different studies have recorded neural activity from different regions. To examine this possibility, we recording neural activity from five different subdivision of the PFC (posterior-dorsal, mid-dorsal, anterior-dorsal, posterior-ventral, anterior-ventral PFC) and compared the neural coding properties in these subdivisions. A total of 1856 neurons in four monkeys trained to perform spatial and shape working memory tasks were analyzed. Our results show striking differences in how these subdivisions code information, with some subdivisions containing a completely dynamic code, and other subdivisions containing a completely static code.\  These findings give a potential explanation for discrepancies in the literature and should lead to a deeper understanding of how information is stored in working memory.

}, url = {http://www.abstractsonline.com/pp8/$\#$!/4376/presentation/4782}, author = {Ethan Meyers and Mitchell Riley and Xue-Lian Qi and Christos Constantinidis} } @article {3465, title = {Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC.}, journal = {Cerebral Cortex}, year = {2017}, month = {10/2017}, abstract = {

Objects that are highly distinct from their surroundings appear to visually "pop-out." This effect is present for displays in which: (1) a single cue object is shown on a blank background, and (2) a single cue object is highly distinct from surrounding objects; it is generally assumed that these 2 display types are processed in the same way. To directly examine this, we applied a decoding analysis to neural activity recorded from the lateral intraparietal (LIP) area and the dorsolateral prefrontal cortex (dlPFC). Our analyses showed that for the single-object displays, cue location information appeared earlier in LIP than in dlPFC. However, for the display with distractors, location information was substantially delayed in both brain regions, and information first appeared in dlPFC. Additionally, we see that pattern of neural activity is similar for both types of displays and across different color transformations of the stimuli, indicating that location information is being coded in the same way regardless of display type. These results lead us to hypothesize that 2 different pathways are involved processing these 2 types of pop-out displays.

}, keywords = {Attention, lateral intraparietal area, neural decoding, posterior parietal cortex, prefrontal cortex}, issn = {1047-3211}, doi = {10.1093/cercor/bhx243}, url = {https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhx243/4430784}, author = {Ethan Meyers and Andy Liang and Fumi Katsuki and Christos Constantinidis} } @article {1773, title = {How PFC and LIP process single and multiple-object {\textquoteleft}pop-out{\textquoteright} displays}, year = {2015}, abstract = {

Images in which one object is more salient than its surroundings lead to a {\textquoteleft}pop-out{\textquoteright} effect where subjects show very efficient behavioral responses to the salient object.\  This pop-out effect is present for displays in which: 1) a single object is on a blank background, and 2) a single object is highly distinct from other surrounding objects. Thus it is generally assumed that this pop-out effect arise from the same neural computations for both of these types of displays, and it is thought that this effect is mediated by {\textquotedblleft}bottom-up{\textquotedblright} attentional mechanisms.\ 

To directly examine whether these two types of displays are indeed processed the same way, we recorded neural activity in LIP and PFC which are two brain regions implicated in attentional processing. Using population decoding methods, in a population of 280 LIP and PFC neurons recorded from two monkeys we observed that when a single isolated object is displayed, information about the object{\textquoteright}s location appeared ~10 ms earlier in LIP than in PFC, which is consistent with a feed-forward account for processing isolated objects. However, when a salient object is presented among multiple distractor objects, information about the location of the salient object was delayed by 60-90 ms in both brain regions, and information now first appeared in PFC. Despite the differences in the latency of information between the two display types, the latency of population firing rate activity was similar for both types of displays. Additionally, we see that pattern of neural activity is very similar for both types of displays (and across different color transformations of the stimuli) indicating that information about the object{\textquoteright}s location is being coded in the same way regardless of display type. These results indicate that there is {\textquoteleft}top-down{\textquoteleft} neural component for processing pop-out displays, and that firing rate latencies can be quite distinct from the latency of when information first appear in a brain region.\ \ 

}, url = {https://www.sfn.org/~/media/SfN/Documents/Annual\%20Meeting/FinalProgram/NS2015/Full\%20Abstract\%20PDFs\%202015/SfN15_Abstracts_PDF_Nanos.ashx}, author = {Ethan Meyers}, editor = {Andy Liang and Christos Constantinidis} }