Corticotropin-releasing hormone signaling from prefrontal cortex to lateral septum suppresses interaction with familiar mice.

Social preference, the decision to interact with one member of the same species over another, is critical to optimize social interactions. Thus, adult rodents favor interacting with novel conspecifics over familiar ones, but whether this social preference stems from neural circuits facilitating interactions with novel individuals or suppressing interactions with familiar ones remains unknown. Here, we identify neurons in the infra-limbic area (ILA) of the mouse prefrontal cortex that express the neuropeptide corticotropin-releasing hormone (CRH) and project to the dorsal region of the rostral lateral septum (rLS). We show how release of CRH during familiar encounters disinhibits rLS neurons, thereby suppressing social interactions with familiar mice and contributing to social novelty preference. We further demonstrate how the maturation of CRH expression in ILA during the first 2 post-natal weeks enables the developmental shift from a preference for littermates in juveniles to a preference for novel mice in adults.

PERIRHINAL CORTEX LEARNS A PREDICTIVE MAP (INTERNAL MODEL) OF THE TASK ENVIRONMENT.

Goal-directed tasks involve acquiring an internal model, known as a predictive map, of relevant stimuli and associated outcomes to guide behavior. Here, we identified neural signatures of a predictive map of task behavior in perirhinal cortex (Prh). Mice learned to perform a tactile working memory task by classifying sequential whisker stimuli over multiple training stages. Chemogenetic inactivation demonstrated that Prh is involved in task learning. Chronic two-photon calcium imaging, population analysis, and computational modeling revealed that Prh encodes stimulus features as sensory prediction errors. Prh forms stable stimulus-outcome associations that expand in a retrospective manner and generalize as animals learn new contingencies. Stimulus-outcome associations are linked to prospective network activity encoding possible expected outcomes. This link is mediated by cholinergic signaling to guide task performance, demonstrated by acetylcholine imaging and perturbation. We propose that Prh combines error-driven and map-like properties to acquire a predictive map of learned task behavior.

The geometry of cortical representations of touch in rodents.

Neurons often encode highly heterogeneous non-linear functions of multiple task variables, a signature of a high-dimensional geometry. We studied the representational geometry in the somatosensory cortex of mice trained to report the curvature of objects touched by their whiskers. High-speed videos of the whiskers revealed that the task can be solved by linearly integrating multiple whisker contacts over time. However, the neural activity in somatosensory cortex reflects non-linear integration of spatio-temporal features of the sensory inputs. Although the responses at first appeared disorganized, we identified an interesting structure in the representational geometry: different whisker contacts are disentangled variables represented in approximately, but not fully, orthogonal subspaces of the neural activity space. This geometry allows linear readouts to perform a broad class of tasks of different complexities without compromising the ability to generalize to novel situations.

Correction: Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions.

[This corrects the article DOI: 10.1371/journal.pcbi.1007862.].

Sensorimotor strategies and neuronal representations for shape discrimination.

Humans and other animals can identify objects by active touch, requiring the coordination of exploratory motion and tactile sensation. Both the motor strategies and neural representations employed could depend on the subject's goals. We developed a shape discrimination task that challenged head-fixed mice to discriminate concave from convex shapes. Behavioral decoding revealed that mice did this by comparing contacts across whiskers. In contrast, a separate group of mice performing a shape detection task simply summed up contacts over whiskers. We recorded populations of neurons in the barrel cortex, which processes whisker input, and found that individual neurons across the cortical layers encoded touch, whisker motion, and task-related signals. Sensory representations were task-specific: during shape discrimination, but not detection, neurons responded most to behaviorally relevant whiskers, overriding somatotopy. Thus, sensory cortex employs task-specific representations compatible with behaviorally relevant computations.

Ketamine disrupts naturalistic coding of working memory in primate lateral prefrontal cortex networks.

Ketamine is a dissociative anesthetic drug, which has more recently emerged as a rapid-acting antidepressant. When acutely administered at subanesthetic doses, ketamine causes cognitive deficits like those observed in patients with schizophrenia, including impaired working memory. Although these effects have been linked to ketamine's action as an N-methyl-D-aspartate receptor antagonist, it is unclear how synaptic alterations translate into changes in brain microcircuit function that ultimately influence cognition. Here, we administered ketamine to rhesus monkeys during a spatial working memory task set in a naturalistic virtual environment. Ketamine induced transient working memory deficits while sparing perceptual and motor skills. Working memory deficits were accompanied by decreased responses of fast spiking inhibitory interneurons and increased responses of broad spiking excitatory neurons in the lateral prefrontal cortex. This translated into a decrease in neuronal tuning and information encoded by neuronal populations about remembered locations. Our results demonstrate that ketamine differentially affects neuronal types in the neocortex; thus, it perturbs the excitation inhibition balance within prefrontal microcircuits and ultimately leads to selective working memory deficits.

Scaling of sensory information in large neural populations shows signatures of information-limiting correlations.

How is information distributed across large neuronal populations within a given brain area? Information may be distributed roughly evenly across neuronal populations, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigate how sensory information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex. We show that information scales sublinearly due to correlated noise in these populations. We compartmentalized noise correlations into information-limiting and nonlimiting components, then extrapolate to predict how information grows with even larger neural populations. We predict that tens of thousands of neurons encode 95% of the information about visual stimulus direction, much less than the number of neurons in primary visual cortex. These findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most sensory information from smaller subpopulations.

Representation of foreseeable choice outcomes in orbitofrontal cortex triplet-wise interactions.

Shared neuronal variability has been shown to modulate cognitive processing. However, the relationship between shared variability and behavioral performance is heterogeneous and complex in frontal areas such as the orbitofrontal cortex (OFC). Mounting evidence shows that single-units in OFC encode a detailed cognitive map of task-space events, but the existence of a robust neuronal ensemble coding for the predictability of choice outcome is less established. Here, we hypothesize that the coding of foreseeable outcomes is potentially unclear from the analysis of units activity and their pairwise correlations. However, this code might be established more conclusively when higher-order neuronal interactions are mapped to the choice outcome. As a case study, we investigated the trial-to-trial shared variability of neuronal ensemble activity during a two-choice interval-discrimination task in rodent OFC, specifically designed such that a lose-switch strategy is optimal by repeating the rewarded stimulus in the upcoming trial. Results show that correlations among triplets are higher during correct choices with respect to incorrect ones, and that this is sustained during the entire trial. This effect is not observed for pairwise nor for higher than third-order correlations. This scenario is compatible with constellations of up to three interacting units assembled during trials in which the task is performed correctly. More interestingly, a state-space spanned by such constellations shows that only correct outcome states that can be successfully predicted are robust over 100 trials of the task, and thus they can be accurately decoded. However, both incorrect and unpredictable outcome representations were unstable and thus non-decodeable, due to spurious negative correlations. Our results suggest that predictability of successful outcomes, and hence the optimal behavioral strategy, can be mapped out in OFC ensemble states reliable over trials of the task, and revealed by sufficiency complex neuronal interactions.

The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior.

Identifying the features of population responses that are relevant to the amount of information encoded by neuronal populations is a crucial step toward understanding population coding. Statistical features, such as tuning properties, individual and shared response variability, and global activity modulations, could all affect the amount of information encoded and modulate behavioral performance. We show that two features in particular affect information: the modulation of population responses across conditions (population signal) and the inverse population covariability along the modulation axis (projected precision). We demonstrate that fluctuations of these two quantities are correlated with fluctuations of behavioral performance in various tasks and brain regions consistently across 4 monkeys (1 female and 1 male Macaca mulatta; and 2 male Macaca fascicularis). In contrast, fluctuations in mean correlations among neurons and global activity have negligible or inconsistent effects on the amount of information encoded and behavioral performance. We also show that differential correlations reduce the amount of information encoded in finite populations by reducing projected precision. Our results are consistent with predictions of a model that optimally decodes population responses to produce behavior.SIGNIFICANCE STATEMENT The last two or three decades of research have seen hot debates about what features of population tuning and trial-by-trial variability influence the information carried by a population of neurons, with some camps arguing, for instance, that mean pairwise correlations or global fluctuations are important while other camps report opposite results. In this study, we identify the most important features of neural population responses that determine the amount of encoded information and behavioral performance by combining analytic calculations with a novel nonparametric method that allows us to isolate the effects of different statistical features. We tested our hypothesis on 4 macaques, three decision-making tasks, and two brain areas. The predictions of our theory were in agreement with the experimental data.

Neuronal Variability as a Proxy for Network State.

Characterizing how network state modulates cortical dynamics and information processing is an important step for understanding the neural code. In 2010, Churchland et al. reported wide experimental evidence showing that spontaneous and stimulus-evoked conditions are two distinct states, as indicated by a marked reduction of neuronal variability after stimulus onset.

What can neuronal populations tell us about cognition?

Nowadays, it is possible to record the activity of hundreds of cells at the same time in behaving animals. However, these data are often treated and analyzed as if they consisted of many independently recorded neurons. How can neuronal populations be uniquely used to learn about cognition? We describe recent work that shows that populations of simultaneously recorded neurons are fundamental to understand the basis of decision-making, including processes such as ongoing deliberations and decision confidence, which generally fall outside the reach of single-cell analysis. Thus, neuronal population data allow addressing novel questions, but they also come with so far unsolved challenges.

Lateral orbitofrontal cortex anticipates choices and integrates prior with current information.

Adaptive behavior requires integrating prior with current information to anticipate upcoming events. Brain structures related to this computation should bring relevant signals from the recent past into the present. Here we report that rats can integrate the most recent prior information with sensory information, thereby improving behavior on a perceptual decision-making task with outcome-dependent past trial history. We find that anticipatory signals in the orbitofrontal cortex about upcoming choice increase over time and are even present before stimulus onset. These neuronal signals also represent the stimulus and relevant second-order combinations of past state variables. The encoding of choice, stimulus and second-order past state variables resides, up to movement onset, in overlapping populations. The neuronal representation of choice before stimulus onset and its build-up once the stimulus is presented suggest that orbitofrontal cortex plays a role in transforming immediate prior and stimulus information into choices using a compact state-space representation.

An overview of the wcd EST clustering tool.

UNLABELLED: The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d(2) distance function or edit distance, improving existing implementations of d(2). It supports merging, refinement and reclustering of clusters. It is 'drop in' compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. It is distributed with an EMBOSS wrapper allowing wcd to be installed as part of an EMBOSS installation (and so provided by a web server). AVAILABILITY: wcd is distributed under a GPL licence and is available from http://code.google.com/p/wcdest. SUPPLEMENTARY INFORMATION: Additional experimental results. The wcd manual, a companion paper describing underlying algorithms, and all datasets used for experimentation can also be found at www.bioinf.wits.ac.za/~scott/wcdsupp.html.

Relationship of serological subtype, basic core promoter and precore mutations to genotypes/subgenotypes of hepatitis B virus.

Using phylogenetic analysis and pairwise comparison of 670 complete hepatitis B virus (HBV) genomes, we demonstrated that nucleotide divergence greater than 7.5% can be used to separate strains into genotypes A-H. Strains can be separated into subgenotypes when two criteria are met: nucleotide divergence of about 4% but less than 7.5% and good bootstrap support. There is a highly statistically significant association between serological subtypes and genotypes (chi2-test for association, P < 0.0001): adw is associated with genotypes A, B, F, G, and H, adr with C and ayw with D and E. The logistic regression method showed that 1802-1803CG are characteristic of genotypes A, D, and E whereas 1802-1803TT are characteristic of genotypes B, C, and F. 1858C is positively associated with genotypes A, F, and H and 1858T with genotypes B, D, and E. Subgenotypes C2, F1/F4 can be differentiated from subgenotypes C1, F2/F3, respectively, because the latter have 1858C as opposed to 1858T in the former. 1888A was positively associated with subgenotype A1 and TAA at 1817 with genotype G. The Haploplot method revealed high linkage between loci 1858 and 1896 but strong evidence of recombination between loci 1862 and 1896. Loci 1809-1812, 1862, and 1888 may have co-evolved. Using a computer program, we showed that serological subtype deduced from the S region (position 155-835) and mutations/variations within the basic core promoter/precore region (1653-1900), allowed genotyping of HBV with 97% sensitivity and 99% specificity. Certain subgenotypes or subgenotype groups could also be differentiated.