Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 57,294 bioRxiv papers from 263,837 authors.
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Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor network models can perform this process. Although phenomenological evidence for pattern completion and attractor dynamics have been described in various recurrent neural circuits, the crucial role that recurrent circuitry plays in implementing these processes has not been shown. Here we show that although odor representations in mouse olfactory bulb degrade under anesthesia, responses in downstream piriform cortex remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Thus, an auto-associative cortical circuit stabilizes output in response to degraded input, and the recurrent circuitry that defines these networks is required for this stabilization.
400 downloads biochemistry
Specialized epitope tags are widely used for detecting, manipulating or purifying proteins, but often their versatility is limited. Here, we introduce the ALFA-tag, a novel, rationally designed epitope tag that serves an exceptionally broad spectrum of applications in life sciences while outperforming established tags like the HA, FLAG or myc tags. The ALFA-tag forms a small and stable α-helix that is functional irrespective of its position on the target protein in prokaryotic and eukaryotic hosts. We developed a nanobody (NbALFA) binding ALFA-tagged proteins from native or fixed specimen with low picomolar affinity. It is ideally suited for super-resolution microscopy, immunoprecipitations and Western blotting, and also allows in-vivo detection of proteins. By solving the crystal structure of the complex we were able to design a nanobody mutant (NbALFAPE) that permits efficient one-step purifications of native ALFA-tagged proteins, complexes and even entire living cells using peptide elution under physiological conditions.
400 downloads neuroscience
Yun Wang, Peng Xie, Hui Gong, Zhi Zhou, Xiuli Kuang, Yimin Wang, An-an Li, Yaoyao Li, Lijuan Liu, Matthew B Veldman, Tanya L Daigle, Karla E Hirokawa, Lei Qu, Phil Lesnar, Shengdian Jiang, Yang Yu, Wayne Wakeman, Shaoqun Zeng, Xiangning Li, Jing Yuan, Thuc Nghi Nguyen, Rachael Larsen, Sara Kedebe, Yuanyuan Song, Lulu Yin, Sujun Zhao, Aaron Feiner, Elise Shen, Chris Hill, Quanxin Wang, Stephanie Mok, Susan M Sunkin, Z. Josh Huang, Luke Esposito, Zizhen Yao, Michael J Hawrylycz, Bosiljka Tasic, Lydia Ng, Staci A. Sorensen, X. William Yang, Julie A Harris, Christof Koch, Qingming Luo, Hanchuan Peng, Hongkui Zeng
Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types and their connectivity. Yet our knowledge about the diversity of neuronal morphology, in particular its distant axonal projections, is still extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale in mice, we established a pipeline that encompasses five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types across the mouse brain in an efficient way by combining transgenic or viral Cre delivery with novel transgenic reporter lines, and generated a large set of high-resolution whole-brain fluorescent imaging datasets containing thousands of reconstructable neurons using the fluorescence micro-optical sectioning tomography (fMOST) system. We developed a set of software tools based on the visualization and analysis suite, Vaa3D, for large-volume image data processing and computation-assisted morphological reconstruction. In a proof-of-principle case, we reconstructed full morphologies of 96 neurons from the claustrum and cortex that belong to a single transcriptomically-defined neuronal subclass. We developed a data-driven clustering approach to classify them into multiple morphological and projection types, suggesting that these neurons work in a targeted and coordinated manner to process cortical information. Imaging data and the new computational reconstruction tools are publicly available to enable community-based efforts towards large-scale full morphology reconstruction of neurons throughout the entire mouse brain.
399 downloads synthetic biology
Therapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (10^3 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massively diverse sequence space to identify globally optimized antibody variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (10^4) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence. Deep learning is then used to predict millions of antigen binders from an in silico library of ~10^8 variants, where experimental testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set of in silico predicted binders is then subjected to multiple developability filters, resulting in thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate high-dimensional protein sequence space, deep learning offers great potential for antibody engineering and optimization.
399 downloads microbiology
Natural transformation, i.e. the uptake of DNA and its stable integration in the chromosome, is a major mechanism of horizontal gene transfer and is common in bacteria. The vast majority of bacterial genomes carry the specific genes involved in natural transformation, yet only a fraction of species are deemed naturally transformable. This is typically explained by the inability of standard laboratory conditions to induce this phenotypic trait. However, even when the inducing conditions are known, large intraspecific variations have been reported. In this study, we investigated the conservation and distribution of natural transformability in the human pathogen Legionella pneumophila . Using a panel of 113 clinical isolates, we reveal that natural transformability is relatively conserved but shows large variations inconsistent with the phylogeny. By conducting a genome-wide association study (GWAS) we identified the conjugative plasmid pLPL as a source of these intraspecific variations. Conjugative transfer of the plasmid to transformable isolates abolished transformation and curing it restores transformability, thereby experimentally validating the GWAS result. We further show that the plasmid inhibits transformation by simultaneously silencing the genes required for DNA uptake and recombination, comEC , comEA , comF and comM . We identified a plasmid-encoded small RNA (sRNA), RocRp, as solely responsible for the silencing of natural transformation. RocRp is homologous to the highly conserved and chromosome-encoded RocR which controls the transient expression of the DNA uptake system. We show that RocRp can take over the function of RocR, by acting as a substitute, ensuring that the bacterial host of the conjugative plasmid does not become naturally transformable. Distinct homologs of this plasmid-encoded sRNA are found in diverse conjugative elements in other Legionella species, suggesting that silencing natural transformation is beneficial to these genetic elements. We propose that transformation-interfering factors are frequent genetic cargo of mobile genetic elements, accounting for intraspecific variations in natural transformation but also responsible for the apparent non-transformability of certain species.
398 downloads cell biology
Brain microvascular endothelial cells (BMECs) possess unique properties underlying the blood-brain-barrier (BBB), which are crucial for homeostatic brain functions and interactions with the immune system. Modulation of BBB function is essential for treatment of neurological diseases and effective tumor targeting. Studies to-date have been hampered by the lack of physiological models using cultivated human BMECs that sustain BBB properties. Recently, differentiation of induced pluripotent stem cells (iPSCs) into cells with BBB-like properties has been reported, providing a robust in vitro model for drug screening and mechanistic understanding of neurological diseases. However, the precise identity of these iBMECs remains unclear. Employing single-cell RNA sequencing, bioinformatic analysis and immunofluorescence for several pathways, transcription factors (TFs), and surface markers, we examined the molecular and functional properties of iBMECs differentiated either in the absence or presence of retinoic acid. We found that iBMECs lack both endothelial-lineage genes and ETS TFs that are essential for the establishment and maintenance of EC identity. Moreover, iBMECs fail to respond to angiogenic stimuli and form lumenized vessels in vivo. We demonstrate that human iBMECs are not barrier-forming ECs but rather EpCAM+ neuroectodermal epithelial cells (NE-EpiCs) that form tight junctions resembling those present in BBB-forming BMECs. Finally, overexpression of ETS TFs (ETV2, FLI1, and ERG) reprograms NE-EpiCs to become more like the BBB-forming ECs. Thus, although directed differentiation of human iBMECs primarily gives rise to epithelial cells, overexpression of several ETS TFs can divert them toward a vascular BBB in vitro.
397 downloads neuroscience
Signals sent back to the neocortex from the hippocampus control the long-term storage of memories in the neocortex, but the cellular mechanisms underlying this process remain elusive. Here, we show that learning is controlled by specific medial-temporal input to neocortical layer 1. To show this we used direct cortical microstimulation detection task that allowed the precise region of learning to be examined and manipulated. Chemogenetically suppressing the last stage of the medial temporal loop, i.e. perirhinal cortex input to neocortical layer 1, profoundly disrupted early memory formation but had no effect on behavior in trained animals. The learning involved the emergence of a small population of layer 5 pyramidal neurons (~10%) with significantly increased firing involving high-frequency bursts of action potentials that were also blocked by suppression of perirhinal input. Moreover, we found that dendritic excitability was correspondingly enhanced in a similarly-sized population of pyramidal neurons and suppression of dendritic activity via optogenetic activation of dendrite-targeting inhibitory neurons also suppressed learning. Finally, single-cell stimulation of cortical layer 5 pyramidal neurons showed that burst but not regular firing retrieved previously learned behavior. We conclude that the medial temporal input to the neocortex controls learning through a process in L1 that elevates dendritic calcium and promotes burst firing.
397 downloads genetics
Pierrick Wainschtein, Deepti P Jain, Loic Yengo, Zhili Zheng, TOPMed Anthropometry Working Group, Trans-Omics for Precision Medicine Consortium, L Adrienne Cupples, Aladdin H Shadyab, Barbara McKnight, Benjamin M Shoemaker, Braxton D Mitchell, Bruce M Psaty, Charles Kooperberg, Dan Roden, Dawood Darbar, Donna K. Arnett, Elizabeth A Regan, Eric Boerwinkle, Jerome I Rotter, Matthew A Allison, Merry-Lynn N McDonald, Mina K. Chung, Nicholas L Smith, Patrick T Ellinor, Ramachandran S Vasan, Rasika A. Mathias, Stephen S Rich, Susan R Heckbert, Susan Redline, Xiuqing Guo, Y-D Ida Chen, Ching-Ti Liu, Mariza de Andrade, Lisa R. Yanek, Christine M Albert, Ryan D. Hernandez, Stephen T McGarvey, Kari E. North, Leslie A Lange, Bruce S. Weir, Cathy C. Laurie, Jian Yang, Peter M. Visscher
Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data, but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs. It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as over-estimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.
395 downloads neuroscience
Recent reports revealed heterogeneity of oligodendrocyte precursor cells (OPCs). It remains unclear if heterogeneity reflects different types of cells with distinct functions, or rather transiently acquired states of cells with the same function. By integrating lineage formation of individual OPC clones, single-cell transcriptomics, calcium imaging and manipulation of neural activity, we show that OPCs in the zebrafish spinal cord can be divided into two functionally distinct entities. One subgroup forms elaborate networks of processes and exhibits a high degree of calcium signalling, but infrequently differentiates, despite contact to permissive axons. Instead, these OPCs divide in an activity and calcium dependent manner to produce another subgroup with higher process motility and less calcium signaling, which readily differentiates. Our data show that OPC subgroups are functionally diverse in responding to neurons and reveal that activity regulates proliferation of a subset of OPCs that is distinct from the cells that generate differentiated oligodendrocytes.
394 downloads systems biology
In order to understand changes in gene expression that occur as a result of age, which might create a permissive or causal environment for age-related diseases, we produced a multi-timepoint Age-related Gene Expression Signature (AGES) from liver, kidney, skeletal muscle and hippocampus of rats, comparing 6, 9, 12, 18, 21, 24 and 27-month old animals. We focused on genes that changed in one direction throughout the lifespan of the animal, either early in life (early logistic changes); at mid-age (mid-logistic); late in life (late-logistic); or linearly, throughout the lifespan. The pathways perturbed as a result of chronological age demonstrate organ-specific and more global effects of aging, and point to mechanisms that might be counter-regulated pharmacologically in order to treat age-associated diseases. A small number of genes were regulated by aging in the same manner in every tissue, suggesting they may be more universal markers of aging.
394 downloads animal behavior and cognition
In most mammalian species, females regularly interact with kin, and it may thus be difficult to understand the evolution of some aggressive and harmful competitive behaviour among females, such as infanticide. Here, we investigate the evolutionary determinants of infanticide by females by combining a quantitative analysis of the taxonomic distribution of infanticide with a qualitative synthesis of the circumstances of infanticidal attacks in published reports. Our results show that female infanticide is widespread across mammals and varies in relation to social organization and life-history, being more frequent where females breed in groups and have intense bouts of high reproductive output. Specifically, female infanticide occurs where the proximity of conspecific offspring directly threatens the killer's reproductive success by limiting access to critical resources for her dependent progeny, including food, shelters, care or a social position. In contrast, infanticide is not immediately modulated by the degree of kinship among females, and females occasionally sacrifice closely related juveniles. Our findings suggest that the potential direct fitness rewards of gaining access to reproductive resources have a stronger influence on the expression of female aggression than the indirect fitness costs of competing against kin.
394 downloads evolutionary biology
Previous genome-scale studies of populations living today in Ethiopia have found evidence of recent gene flow from an Eurasian source, dating to the last 3,000 years. Haplotype and genotype data based analyses of modern and ancient data (aDNA) have considered Sardinia-like proxy, broadly Levantine or Neolithic Levantine populations as a range of possible sources for this gene flow. Given the ancient nature of this gene flow and the extent of population movements and replacements that affected West Asia in the last 3000 years, aDNA evidence would seem as the best proxy for determining the putative population source. We demonstrate, however, that the deeply divergent, autochthonous African component which accounts for ~50% of most contemporary Ethiopian genomes, affects the overall allele frequency spectrum to an extent that makes it hard to control for it and, at once, to discern between subtly different, yet important, Eurasian sources (such as Anatolian or Levant Neolithic ones). Here we re-assess pattern of allele sharing between the Eurasian component of Ethiopians (here called NAF for Non African) and ancient and modern proxies area after having extracted NAF from Ethiopians through ancestry deconvolution, and unveil a genomic signature compatible with population movements that affected the Mediterranean area and the Levant after the fall of the Minoan civilization.
394 downloads genetics
Isogenic pairs of cell lines, which differ by a single genetic modification, are powerful tools for understanding gene function. Generating such pairs for mammalian cells, however, is labor-intensive, time-consuming, and impossible in some cell types. Here we present an approach to create isogenic pairs of cells and screen them with genome-wide CRISPR-Cas9 libraries to generate genetic interaction maps. We queried the anti-apoptotic genes BCL2L1 and MCL1, and the DNA damage repair gene PARP1, via 25 genome-wide screens across 4 cell lines. For all three genes, we identify a rich set of both expected and novel buffering and synthetic lethal interactions. Further, we compare the interactions observed in genetic space to those found when targeting these genes with small molecules and identify hits that may inform the clinical uses for these inhibitors. We anticipate that this methodology will be broadly useful to comprehensively study genes of interest across many cell types.
392 downloads biophysics
Transcription-factor (TF) proteins recognize specific genomic sequences, despite an overwhelming excess of non-specific DNA, to regulate complex gene expression programs. While there have been significant advances in understanding how DNA sequence and shape contribute to recognition, some fundamental aspects of protein-DNA binding remain poorly understood. Many DNA-binding proteins induce changes in the DNA structure outside the intrinsic B-DNA envelope. How the energetic cost associated with distorting DNA contributes to recognition has proven difficult to study and measure experimentally because the distorted DNA structures exist as low-abundance conformations in the naked B-DNA ensemble. Here, we use a novel high-throughput assay called SaMBA (Saturation Mismatch-Binding Assay) to investigate the role of DNA conformational penalties in TF-DNA recognition. The approach introduces mismatched base-pairs (i.e. mispairs) within TF binding sites to pre-induce a variety of DNA structural distortions much larger than those induced by changes in Watson-Crick sequence. Strikingly, while most mismatches either weakened TF binding (~70%) or had negligible effects (~20%), approximately 10% of mismatches increased binding and at least one mismatch was found that increased the binding affinity for each of 21 examined TFs. Mismatches also converted sites from the non-specific affinity range into specific sites, and high-affinity sites into 'super-sites' stronger than any known canonical binding site. These findings reveal a complex binding landscape that cannot be explained based on DNA sequence alone. Analysis of crystal structures together with NMR and molecular dynamics simulations revealed that many of the mismatches that increase binding induce distortions similar to those induced by TF binding, thus pre-paying some of the energetic cost to deform the DNA. Our work indicates that conformational penalties are a major determinant of protein-DNA recognition, and reveals mechanisms by which mismatches can recruit TFs and thus modulate replication and repair activities in the cell.
391 downloads neuroscience
Our nervous system is organized into circuits with specifically matched and tuned cell-to-cell connections that are essential for proper function. The mechanisms by which presynaptic axon terminals and postsynaptic dendrites recognize each other and establish the correct number of connections are still incompletely understood. Sperry's chemoaffinity hypothesis proposes that pre- and postsynaptic partners express specific combinations of molecules that enable them to recognize each other. Alternatively, Peters' rule proposes that presynaptic axons and postsynaptic dendrites use non-partner-derived global positional cues to independently reach their target area, and once there they randomly connect with any available neuron. These connections can then be further refined by additional mechanisms based on synaptic activity. We used the tractable genetic model system, the Drosophila embryo and larva, to test these hypotheses and elucidate the roles of 1) global positional cues, 2) partner-derived cues and 3) synaptic activity in the establishment of selective connections in the developing nerve cord. We altered the position or activity of presynaptic partners and analyzed the effect of these manipulations on the number of synapses with specific postsynaptic partners, strength of functional connections, and behavior controlled by these neurons. For this purpose, we combined developmental live imaging, electron microscopy reconstruction of circuits, functional imaging of neuronal activity, and behavioral experiments in wildtype and experimental animals. We found that postsynaptic dendrites are able to find, recognize, and connect to their presynaptic partners even when these have been shifted to ectopic locations through the overexpression of receptors for midline guidance cues. This suggests that neurons use partner-derived cues that allow them to identify and connect to each other. However, while partner-derived cues are sufficient for recognition between specific partners and establishment of connections; without orderly positioning of axon terminals by positional cues and without synaptic activity during embryonic development, the numbers of functional connections are altered with significant consequences for behavior. Thus, multiple mechanisms including global positional cues, partner-derived cues, and synaptic activity contribute to proper circuit assembly in the developing Drosophila nerve cord.
390 downloads cancer biology
Visual analysis of histopathology slides of lung cell tissues is one of the main methods used by pathologists to assess the stage, types and sub-types of lung cancers. Adenocarcinoma and squamous cell carcinoma are two most prevalent sub-types of lung cancer, but their distinction can be challenging and time-consuming even for the expert eye. In this study, we trained a deep learning convolutional neural network (CNN) model (inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately classify whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. Our method slightly outperforms a human pathologist, achieving better sensitivity and specificity, with ~0.97 average Area Under the Curve (AUC) on a held-out population of whole-slide scans. Furthermore, we trained the neural network to predict the ten most commonly mutated genes in lung adenocarcinoma. We found that six of these genes - STK11, EGFR, FAT1, SETBP1, KRAS and TP53 - can be predicted from pathology images with an accuracy ranging from 0.733 to 0.856, as measured by the AUC on the held-out population. These findings suggest that deep learning models can offer both specialists and patients a fast, accurate and inexpensive detection
389 downloads bioinformatics
Proteus is a package for downstream analysis of MaxQuant evidence data in the R environment. It provides tools for peptide and protein aggregation, quality checks, data exploration and visualisation. Interactive analysis is implemented in the Shiny framework, where individual peptides or protein may be examined in the context of a volcano plot. Proteus performs differential expression analysis with the well-established tool limma, which offers robust treatment of missing data, frequently encountered in label-free mass-spectrometry experiments. We demonstrate on real and simulated data that limma results in improved sensitivity over random imputation combined with a t-test as implemented in the popular package Perseus. Embedding Proteus in R provides access to a wide selection of statistical and graphical tools for further analysis and reproducibility by scripting. Availability and implementation: The open-source R package, including example data and tutorials, is available to install from GitHub (https://github.com/bartongroup/proteus).
389 downloads genomics
Dave T Gerrard, Andrew A Berry, Rachel E Jennings, Matthew J Birket, Sarah J Withey, Patrick Short, Sandra Jimenez-Gancedo, Panos Firbas, Ian Donaldson, Andrew D. Sharrocks, Karen Piper Hanley, Matthew E Hurles, Jose Luis Gomez-Skarmeta, Nicoletta Bobola, Neil A Hanley
How the genome activates or silences transcriptional programmes governs organ formation. Little is known in human embryos undermining our ability to benchmark the fidelity of in vitro stem cell differentiation or cell programming, or interpret the pathogenicity of noncoding variation. Here, we studied histone modifications across thirteen tissues during human organogenesis. We integrated the data with transcription to build the first overview of how the human genome differentially regulates alternative organ fates including by repression. Promoters from nearly 20,000 genes partitioned into discrete states without showing bivalency. Key developmental gene sets were actively repressed outside of the appropriate organ. Candidate enhancers, functional in zebrafish, allowed imputation of tissue-specific and shared patterns of transcription factor binding. Overlaying more than 700 noncoding mutations from patients with developmental disorders allowed correlation to unanticipated target genes. Taken together, the data provide a new, comprehensive genomic framework for investigating normal and abnormal human development.
388 downloads cell biology
Labeling and tracking biomolecules with fluorescent probes on the single molecule level enables quantitative insights into their dynamics in living cells. We previously developed Riboglow, a platform to label RNAs in live mammalian cells, consisting of a short RNA tag and a small organic probe that increases fluorescence upon binding RNA. Here, we demonstrate that Riboglow is capable of detecting and tracking single RNA molecules. We benchmark RNA tracking by comparing results with the established MS2 RNA tagging system. To demonstrate versatility of Riboglow, we assay translation on the single molecule level, where the translated mRNA is tagged with Riboglow and the nascent polypeptide is labeled with a fluorescent antibody. The growing effort to investigate RNA biology on the single molecule level requires sophisticated and diverse fluorescent probes for multiplexed, multi-color labeling of biomolecules of interest, and we present Riboglow as a new member in this toolbox.
388 downloads neuroscience
Serial and parallel processing in visual search have been long debated in psychology but the processing mechanism remains an open issue. Serial processing allows only one object at a time to be processed, whereas parallel processing assumes that various objects are processed simultaneously. Here we present novel neural models for the two types of processing mechanisms based on analysis of simultaneously recorded spike trains using electrophysiological data from prefrontal cortex of rhesus monkeys while processing task-relevant visual displays. We combine mathematical models describing neuronal attention and point process models for spike trains. The same model can explain both serial and parallel processing by adopting different parameter regimes. We present statistical methods to distinguish between serial and parallel processing based on both maximum likelihood estimates and decoding analysis of the attention when two stimuli are presented simultaneously. Results show that both processing mechanisms are in play for the simultaneously recorded neurons, but neurons tend to follow parallel processing in the beginning after the onset of the stimulus pair, whereas they tend to serial processing later on. This could be explained by parallel processing being related to sensory bottom-up signals or feedforward processing, which typically occur in the beginning after stimulus onset, whereas top-down signals related to cognitive modulatory influences guiding attentional effects in recurrent feedback connections occur after a small delay, and is related to serial processing, where all processing capacities are being directed towards the attended object.
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