Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance and across seconds when animals switch attention. While both phenomena occur in the same circuit, it is unknown whether they rely on similar mechanisms. We imaged primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM, and VIP inhibitory cells. Circuit modeling revealed that cell class-specific top-down inputs best explained attentional modulation, while reorganization of local functional connectivity accounted for learning-related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter timescales

Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex

Analysis of Pools of Targeted Salmonella Deletion Mutants Identifies Novel Genes Affecting Fitness during Competitive Infection in Mice

Pools of mutants of minimal complexity but maximal coverage of genes of interest facilitate screening for genes under selection in a particular environment. We constructed individual deletion mutants in 1,023 Salmonella enterica serovar Typhimurium genes, including almost all genes found in Salmonella but not in related genera. All mutations were confirmed simultaneously using a novel amplification strategy to produce labeled RNA from a T7 RNA polymerase promoter, introduced during the construction of each mutant, followed by hybridization of this labeled RNA to a Typhimurium genome tiling array. To demonstrate the ability to identify fitness phenotypes using our pool of mutants, the pool was subjected to selection by intraperitoneal injection into BALB/c mice and subsequent recovery from spleens. Changes in the representation of each mutant were monitored using T7 transcripts hybridized to a novel inexpensive minimal microarray. Among the top 120 statistically significant spleen colonization phenotypes, more than 40 were mutations in genes with no previously known role in this model. Fifteen phenotypes were tested using individual mutants in competitive assays of intraperitoneal infection in mice and eleven were confirmed, including the first two examples of attenuation for sRNA mutants in Salmonella. We refer to the method as Array-based analysis of cistrons under selection (ABACUS)

The Ccr4-Not Complex Interacts with the mRNA Export Machinery

The Ccr4-Not complex is a key eukaryotic regulator of gene transcription and cytoplasmic mRNA degradation. Whether this complex also affects aspects of post-transcriptional gene regulation, such as mRNA export, remains largely unexplored. Human Caf1 (hCaf1), a Ccr4-Not complex member, interacts with and regulates the arginine methyltransferase PRMT1, whose targets include RNA binding proteins involved in mRNA export. However, the functional significance of this regulation is poorly understood.Here we demonstrate using co-immunoprecipitation approaches that Ccr4-Not subunits interact with Hmt1, the budding yeast ortholog of PRMT1. Furthermore, using genetic and biochemical approaches, we demonstrate that Ccr4-Not physically and functionally interacts with the heterogenous nuclear ribonucleoproteins (hnRNPs) Nab2 and Hrp1, and that the physical association depends on Hmt1 methyltransferase activity. Using mass spectrometry, co-immunoprecipitation and genetic approaches, we also uncover physical and functional interactions between Ccr4-Not subunits and components of the nuclear pore complex (NPC) and we provide evidence that these interactions impact mRNA export.Taken together, our findings suggest that Ccr4-Not has previously unrealized functional connections to the mRNA processing/export pathway that are likely important for its role in gene expression. These results shed further insight into the biological functions of Ccr4-Not and suggest that this complex is involved in all aspects of mRNA biogenesis, from the regulation of transcription to mRNA export and turnover

Inhibitory microcircuits for top-down plasticity of sensory representations

Rewards influence plasticity of early sensory representations. The underlying changes in cir-cuitry are however unclear. Recent experimental findings suggest that inhibitory circuits regu-late learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs,including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major rolein adjusting stimulus representations. We investigate how top-down modulation by rewards in-teracts with local plasticity to induce long-lasting changes in circuitry. Using a computationalmodel of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can storeinformation about rewarded stimuli to instruct long-term changes in excitatory connectivity inthe absence of further reward. In our model, stimulus-tuned somatostatin-positive interneuronsdevelop strong connections to parvalbumin-positive interneurons during reward such that theyselectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leadingto increased stimulus representation. We make specific testable predictions and show that thistwo-stage model allows for translation invariance of the learned representation

Inhibitory microcircuits for top-down processing of sensory representations

Humans and animals are remarkable at detecting stimuli that predict rewards. While the underlying neural mechanisms are unknown, reward influences plasticity of sensory representations in early sensory areas. The underlying changes in excitatory and inhibitory circuitry are however unclear. Recently, experimental findings suggest that the inhibitory circuits can regulate learning. In addition, the inhibitory neurons in superficial layers are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that plasticity of interneuron circuits plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards can interact with local excitatory and inhibitory plasticity to induce long-lasting changes in sensory circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron networks can store information about the rewarded stimulus to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons (SSTs) develop strong connections to parvalbumin-positive interneurons (PVs) during reward presentation such that they selectively disinhibit the pyramidal layer henceforth. This triggers plasticity in the excitatory neurons, which leads to increased stimulus representation. We make specific testable predictions in terms of the activity of different neuron types. We finally show that this two-stage model allows for translation invariance of the learned representation

Kinetic assessment of breast tumors using high spatial resolution signal enhancement ratio (SER) imaging

The goal of this study was to investigate the relationship between an empirical contrast kinetic parameter, the signal enhancement ratio (SER), for three-timepoint, high spatial resolution contrast-enhanced (CE) MRI, and a commonly analyzed pharmacokinetic parameter, k(ep), using dynamic high temporal resolution CE-MRI. Computer simulation was performed to investigate: 1) the relationship between the SER and the contrast agent concentration ratio (CACR) of two postcontrast time-points (t(p1) and t(p2)); 2) the relationship between the CACR and the redistribution rate constant (k(ep)) based on a two-compartment pharmacokinetic model; and 3) the sensitivity of the relationship between the SER and k(ep) to native tissue T(1) relaxation time, T(10), and to errors in an assumed vascular input function. The relationship between SER and k(ep) was verified experimentally using a mouse model of breast cancer. The results showed that a monotonic mathematical relationship between SER and k(ep) could be established if the acquisition parameters and the two postinjection timepoints of SER, t(p1), t(p2), were appropriately chosen. The in vivo study demonstrated a close correlation between SER and k(ep) on a pixel-by-pixel basis (Spearman rank correlation coefficient = 0.87 ± 0.03). The SER is easy to calculate and may have a unique role in breast tissue characterization

CCN GAC Workshop: Issues with learning in biological recurrent neural networks

This perspective piece came about through the Generative Adversarial Collaboration (GAC) series of workshops organized by the Computational Cognitive Neuroscience (CCN) conference in 2020. We brought together a number of experts from the field of theoretical neuroscience to debate emerging issues in our understanding of how learning is implemented in biological recurrent neural networks. Here, we will give a brief review of the common assumptions about biological learning and the corresponding findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks commonly used in artificial intelligence. We will then outline the key issues discussed in the workshop: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. Finally, we conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help to bring clarity to these issues

Interrelationships between Yeast Ribosomal Protein Assembly Events and Transient Ribosome Biogenesis Factors Interactions in Early Pre-Ribosomes

Early steps of eukaryotic ribosome biogenesis require a large set of ribosome biogenesis factors which transiently interact with nascent rRNA precursors (pre-rRNA). Most likely, concomitant with that initial contacts between ribosomal proteins (r-proteins) and ribosome precursors (pre-ribosomes) are established which are converted into robust interactions between pre-rRNA and r-proteins during the course of ribosome maturation. Here we analysed the interrelationship between r-protein assembly events and the transient interactions of ribosome biogenesis factors with early pre-ribosomal intermediates termed 90S pre-ribosomes or small ribosomal subunit (SSU) processome in yeast cells. We observed that components of the SSU processome UTP-A and UTP-B sub-modules were recruited to early pre-ribosomes independently of all tested r-proteins. On the other hand, groups of SSU processome components were identified whose association with early pre-ribosomes was affected by specific r-protein assembly events in the head-platform interface of the SSU. One of these components, Noc4p, appeared to be itself required for robust incorporation of r-proteins into the SSU head domain. Altogether, the data reveal an emerging network of specific interrelationships between local r-protein assembly events and the functional interactions of SSU processome components with early pre-ribosomes. They point towards some of these components being transient primary pre-rRNA in vivo binders and towards a role for others in coordinating the assembly of major SSU domains