Working memory performance is tied to stimulus complexity.

Working memory is the cognitive capability to maintain and process information over short periods. Behavioral and computational studies have shown that visual information is associated with working memory performance. However, the underlying neural correlates remain unknown. To identify how visual information affects working memory performance, we conducted behavioral experiments in pigeons (Columba livia) and single unit recordings in the avian prefrontal analog, the nidopallium caudolaterale (NCL). Complex pictures featuring luminance, spatial and color information, were associated with higher working memory performance compared to uniform gray pictures in conjunction with distinct neural coding patterns. For complex pictures, we found a multiplexed neuronal code displaying visual and value-related features that switched to a representation of the upcoming choice during a delay period. When processing gray stimuli, NCL neurons did not multiplex and exclusively represented the choice already during stimulus presentation and throughout the delay period. The prolonged representation possibly resulted in a decay of the memory trace ultimately leading to a decrease in performance. In conclusion, we found that high stimulus complexity is associated with neuronal multiplexing of the working memory representation possibly allowing a facilitated read-out of the neural code resulting in enhancement of working memory performance.

Long non-coding RNAs as the regulatory hubs in rice response to salt stress.

Salinity seriously constrains growth and fertility of rice worldwide. Long non-coding RNAs (lncRNAs) play crucial roles in plant abiotic stress response. However, salt responsive lncRNAs are poorly understood in rice. Herein, salt responsive lncRNAs (DE-lncRNAs) were identified in FL478 (salt tolerant) compared to its susceptible parent (IR29) using RNA-seq in root tissues at seedling stage. In FL478 and IR29, 8724 and 9235 transcripts with length of > 200 bp were nominated as potential lncRNAs, respectively. Rigorous filtering left four (in FL478) and nine (in IR29) DE-lncRNAs with only 2 DE-lncRNAs in common. ATAC-seq data showed that the genomic regions of all four lncRNAs in FL478 and 6/9 in IR29 are significantly accessible for transcription. Weighted correlation network analysis (WGCNA) revealed that lncRNA.2-FL was highly correlated with 173 mRNAs as trans-targets and a gene encoding pentatricopeptide repeat (PPR) protein was predicted as cis-target of lncRNA.2-FL. In silico mutagenesis analysis proposed the same transcription factor binding sites (TFBSs) in vicinity of the trans- and cis-regulatory target genes of lncRNA.2-FL, which significantly affect their transcription start site (TSS). This study provides new insights into involvement of the DE-lncRNAs in rice response to salt stress. Among them, lncRNA.2-FL may play a significant regulatory role in the salt stress tolerance of FL478.

Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia).

Recognizing and categorizing visual stimuli are cognitive functions vital for survival, and an important feature of visual systems in primates as well as in birds. Visual stimuli are processed along the ventral visual pathway. At every stage in the hierarchy, neurons respond selectively to more complex features, transforming the population representation of the stimuli. It is therefore easier to read-out category information in higher visual areas. While explicit category representations have been observed in the primate brain, less is known on equivalent processes in the avian brain. Even though their brain anatomies are radically different, it has been hypothesized that visual object representations are comparable across mammals and birds. In the present study, we investigated category representations in the pigeon visual forebrain using recordings from single cells responding to photographs of real-world objects. Using a linear classifier, we found that the population activity in the visual associative area mesopallium ventrolaterale (MVL) distinguishes between animate and inanimate objects, although this distinction is not required by the task. By contrast, a population of cells in the entopallium, a region that is lower in the hierarchy of visual areas and that is related to the primate extrastriate cortex, lacked this information. A model that pools responses of simple cells, which function as edge detectors, can account for the animate vs. inanimate categorization in the MVL, but performance in the model is based on different features than in MVL. Therefore, processing in MVL cells is very likely more abstract than simple computations on the output of edge detectors.

From grid cells to place cells with realistic field sizes.

While grid cells in the medial entorhinal cortex (MEC) of rodents have multiple, regularly arranged firing fields, place cells in the cornu ammonis (CA) regions of the hippocampus mostly have single spatial firing fields. Since there are extensive projections from MEC to the CA regions, many models have suggested that a feedforward network can transform grid cell firing into robust place cell firing. However, these models generate place fields that are consistently too small compared to those recorded in experiments. Here, we argue that it is implausible that grid cell activity alone can be transformed into place cells with robust place fields of realistic size in a feedforward network. We propose two solutions to this problem. Firstly, weakly spatially modulated cells, which are abundant throughout EC, provide input to downstream place cells along with grid cells. This simple model reproduces many place cell characteristics as well as results from lesion studies. Secondly, the recurrent connections between place cells in the CA3 network generate robust and realistic place fields. Both mechanisms could work in parallel in the hippocampal formation and this redundancy might account for the robustness of place cell responses to a range of disruptions of the hippocampal circuitry.