Five recommendations from this prince of a publication. In order of wonderfulness.
1. A biggie. A computational model of multisensory integration, with attention to how cue reliability alters the pattern of integration. We need to know this! Note there is a News & Views discussing it at the front of the journal.
Tomokazu Ohshiro, Dora E Angelaki & Gregory C DeAngelis A normalization model of multisensory integration. Nature Neuroscience 14, 775–782 (2011)
Abstract: Responses of neurons that integrate multiple sensory inputs are traditionally characterized in terms of a set of empirical principles. However, a simple computational framework that accounts for these empirical features of multisensory integration has not been established. We propose that divisive normalization, acting at the stage of multisensory integration, can account for many of the empirical principles of multisensory integration shown by single neurons, such as the principle of inverse effectiveness and the spatial principle. This model, which uses a simple functional operation (normalization) for which there is considerable experimental support, also accounts for the recent observation that the mathematical rule by which multisensory neurons combine their inputs changes with cue reliability. The normalization model, which makes a strong testable prediction regarding cross-modal suppression, may therefore provide a simple unifying computational account of the important features of multisensory integration by neurons.
2. This article helps us move from an anatomic understanding of top-down and bottom-up neural activity to an informational model centered around predictions, observations, and errors. Also useful for those interested in clinical patterns of brain waves. Food for thought re: next-generation diagnostics.
Transitions in neural oscillations reflect prediction errors generated in audiovisual speech. Luc H Arnal, Valentin Wyart, Anne-Lise Giraud. Nature Neuroscience 14,797–801(2011)
Abstract: According to the predictive coding theory, top-down predictions are conveyed by backward connections and prediction errors are propagated forward across the cortical hierarchy. Using MEG in humans, we show that violating multisensory predictions causes a fundamental and qualitative change in both the frequency and spatial distribution of cortical activity. When visual speech input correctly predicted auditory speech signals, a slow delta regime (3–4 Hz) developed in higher-order speech areas. In contrast, when auditory signals invalidated predictions inferred from vision, a low-beta (14–15 Hz) / high-gamma (60–80 Hz) coupling regime appeared locally in a multisensory area (area STS). This frequency shift in oscillatory responses scaled with the degree of audio-visual congruence and was accompanied by increased gamma activity in lower sensory regions. These findings are consistent with the notion that bottom-up prediction errors are communicated in predominantly high (gamma) frequency ranges, whereas top-down predictions are mediated by slower (beta) frequencies.
3. Okay, it’s about flies, but it’s still about behavior. How conspecifics’ pheromones determine which behaviors in a fly’s repertoire are activated/suppressed. A mechanism for social hierarchy without group selection.
Liming Wang & David J Anderson (omega). Hierarchical chemosensory regulation of male-male social interactions in Drosophila. Nature Neuroscience 14, 757–762 (2011)
Abstract: Pheromones regulate male social behaviors in Drosophila, but the identities and behavioral role(s) of these chemosensory signals, and how they interact, are incompletely understood. We found that (z)-7-tricosene, a male-enriched cuticular hydrocarbon that was previously shown to inhibit male-male courtship, was essential for normal levels of aggression. The mechanisms by which (z)-7-tricosene induced aggression and suppressed courtship were independent, but both required the gustatory receptor Gr32a. Sensitivity to (z)-7-tricosene was required for the aggression-promoting effect of 11-cis-vaccenyl acetate (cVA), an olfactory pheromone, but (z)-7-tricosene sensitivity was independent of cVA. (z)-7-tricosene and cVA therefore regulate aggression in a hierarchical manner. Furthermore, the increased courtship caused by depletion of male cuticular hydrocarbons was suppressed by a mutation in the olfactory receptor Or47b. Thus, male social behaviors are controlled by gustatory pheromones that promote aggression and suppress courtship, and whose influences are dominant to olfactory pheromones that enhance these behaviors.
4. Sex is happening all at once. Only the Orthodox, or old-school models, do we do one thing at a time and in order. This one is a review; most of this is known already but this is a good synthesis and overview especially for college and medical students. Great teaching resource if you are building a keynote/powerpoint presentation.
Reframing sexual differentiation of the brain (REVIEW). M McCarthy Arthur P Arnold Nature Neuroscience 14,677–683(2011)
Abstract: In the twentieth century, the dominant model of sexual differentiation stated that genetic sex (XX versus XY) causes differentiation of the gonads, which then secrete gonadal hormones that act directly on tissues to induce sex differences in function. This serial model of sexual differentiation was simple, unifying and seductive. Recent evidence, however, indicates that the linear model is incorrect and that sex differences arise in response to diverse sex-specific signals originating from inherent differences in the genome and involve cellular mechanisms that are specific to individual tissues or brain regions. Moreover, sex-specific effects of the environment reciprocally affect biology, sometimes profoundly, and must therefore be integrated into a realistic model of sexual differentiation. A more appropriate model is a parallel-interactive model that encompasses the roles of multiple molecular signals and pathways that differentiate males and females, including synergistic and compensatory interactions among pathways and an important role for the environment.
5. The robots are coming. Mathematical modeling (neural network) of visual search matches human performance. Important from the standpoint of reward prediction theory.
Wei Ji Ma et al & Alexandre Pouget (Omega). Behavior and neural basis of near-optimal visual search. Nature Neuroscience 14, 783–790 (2011)
Abstract: The ability to search efficiently for a target in a cluttered environment is one of the most remarkable functions of the nervous system. This task is difficult under natural circumstances, as the reliability of sensory information can vary greatly across space and time and is typically a priori unknown to the observer. In contrast, visual-search experiments commonly use stimuli of equal and known reliability. In a target detection task, we randomly assigned high or low reliability to each item on a trial-by-trial basis. An optimal observer would weight the observations by their trial-to-trial reliability and combine them using a specific nonlinear integration rule. We found that humans were near-optimal, regardless of whether distractors were homogeneous or heterogeneous and whether reliability was manipulated through contrast or shape. We present a neural-network implementation of near-optimal visual search based on probabilistic population coding. The network matched human performance.Follow @peterfreed