Brain responses to the acquired moral status of faces

We examined whether neural responses associated with judgments of socially relevant aspects of the human face extend to stimuli that acquire their significance through learning in a meaningful interactive context, specifically reciprocal cooperation. During fMRI, subjects made gender judgments on faces of people who had been introduced as fair (cooperators) or unfair (defector) players through repeated play of a sequential Prisoner's Dilemma game. To manipulate moral responsibility, players were introduced as either intentional or nonintentional agents. Our behavioral (likebility ratings and memory performance) as well as our imaging data confirm the saliency of social fairness for human interactions. Relative to neutral faces, faces of intentional cooperators engendered increased activity in left amygdala, bilateral insula, fusiform gyrus, STS, and reward-related areas. Our data indicate that rapid learning regarding the moral status of others is expressed in altered neural activity within a system associated with social cognition.     

A hierarchical neuronal model for generation and online recognition of birdsongs

The neuronal system underlying learning, generation and recognition of song in birds is one of the best-studied systems in the neurosciences. Here, we use these experimental findings to derive a neurobiologically plausible, dynamic, hierarchical model of birdsong generation and transform it into a functional model of birdsong recognition. The generation model consists of neuronal rate models and includes critical anatomical components like the premotor song-control nucleus HVC (proper name), the premotor nucleus RA (robust nucleus of the arcopallium), and a model of the syringeal and respiratory organs. We use Bayesian inference of this dynamical system to derive a possible mechanism for how birds can efficiently and robustly recognize the songs of their conspecifics in an online fashion. Our results indicate that the specific way birdsong is generated enables a listening bird to robustly and rapidly perceive embedded information at multiple time scales of a song. The resulting mechanism can be useful for investigating the functional roles of auditory recognition areas and providing predictions for future birdsong experiments.     

Somatostatin Interneurons Promote Neuronal Synchrony in the Neonatal Hippocampus

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Dynamic causal models of neural system dynamics: current state and future extensions

Complex processes resulting from interaction of multiple elements can rarely be understood by analytical scientific approaches alone; additional, mathematical models of system dynamics are required. This insight, which disciplines like physics have embraced for a long time already, is gradually gaining importance in the study of cognitive processes by functional neuroimaging. In this field, causal mechanisms in neural systems are described in terms of effective connectivity. Recently, dynamic causal modelling (DCM) was introduced as a generic method to estimate effective connectivity from neuroimaging data in a Bayesian fashion. One of the key advantages of DCM over previous methods is that it distinguishes between neural state equations and modality-specific forward models that translate neural activity into a measured signal. Another strength is its natural relation to Bayesian model selection (BMS) procedures. In this article, we review the conceptual and mathematical basis of DCM and its implementation for functional magnetic resonance imaging data and event-related potentials. After introducing the application of BMS in the context of DCM, we conclude with an outlook to future extensions of DCM. These extensions are guided by the long-term goal of using dynamic system models for pharmacological and clinical applications, particularly with regard to synaptic plasticity.     

A Bayesian Attractor Model for Perceptual Decision Making

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.     

Atmospheric water vapor as driver of litter decomposition in Mediterranean shrubland and grassland during rainless seasons

Litter production in many drought‐affected ecosystems coincides with the beginning of an extended season of no or limited rainfall. Because of lack of moisture litter decomposition during such periods has been largely ignored so far, despite potential importance for the overall decay process in such ecosystems. To determine drivers and extent of litter decay in rainless periods, a litterbag study was conducted in Mediterranean shrublands, dwarf shrublands and grasslands. Heterogeneous local and common straw litter was left to decompose in open and shaded patches of various field sites in two study regions. Fresh local litter lost 4–18% of its initial mass over about 4 months without rainfall, which amounted to 15–50% of total annual decomposition. Lab incubations and changes in chemical composition suggested that litter was degraded by microbial activity, enabled by absorption of water vapor from the atmosphere. High mean relative humidity of 85% was measured during 8–9 h of most nights, but the possibility of fog deposition or dew formation at the soil surface was excluded. Over 95% of the variation in mass loss and changes in litter nitrogen were explained by characteristics of water‐vapor uptake by litter. Photodegradation induced by the intense solar radiation was an additional mechanism of litter decomposition as indicated by lignin dynamics. Lignin loss from litter increased with exposure to ultraviolet radiation and with initial lignin concentration, together explaining 90%–97% of the variation in lignin mass change. Our results indicate that water vapor, solar radiation and litter quality controlled decomposition and changes in litter chemistry during rainless seasons. Many regions worldwide experience transient periods without rainfall, and more land area is expected to undergo reductions in rainfall as a consequence of climate change. Therefore, absorption of water vapor might play a role in decomposition and nutrient cycling in an increasing number of ecosystems.     

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.     

Parental care trade‐offs in the inter‐brood phase in Barn Swallows Hirundo rustica

In seasonal environments with limited time and energy resources, double‐brooded birds face trade‐offs in the timing of their two reproductive attempts and in the effort allocated to the first and the second broods. In the Barn Swallow Hirundo rustica a long care period for the first brood enhances the survival of first‐brood chicks, but also delays the start of the second brood, which in turn reduces the survival prospects of second‐brood chicks. Probably as a response to this trade‐off, double‐brooded Barn Swallows reduce the period of post‐fledging care for first‐brood fledglings. By radiotracking whole families, we investigated the determinants of this behaviour and its consequences for the survival of the first‐brood fledglings. The end of the females’ investment in post‐fledging care of the first brood was related to the beginning of egg synthesis for the second clutch. With the start of egg synthesis, females significantly reduced provisioning rates to the first‐brood fledglings to less than one‐fifth of the previous rates, while the proportion of time they spent foraging remained high. Assuming that the females’ foraging success was constant, we conclude that their energy income was allocated to egg production rather than fledgling provision. Males did not compensate for the females’ reduced feeding rates. Thus the start of egg production for the second clutch had a marked effect on the quantity of food received by first‐brood fledglings. In parallel with the changes in parental behaviour and provisioning rates, we observed a marked drop in the daily survival rate of first‐brood chicks. These results support the hypothesis that females face a strong trade‐off in the allocation of energy to subsequent broods. Energy allocation to a second clutch involves a cost in terms of reduced provisioning, and as a result the survival of first‐brood chicks is compromised. This is probably outweighed by the improved success of an early second brood.     

Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks

Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in standard RNN models may be an over-simplification of what real neuronal networks compute. Here, we suggest that the RNN approach may be made computationally more powerful by its fusion with Bayesian inference techniques for nonlinear dynamical systems. In this scheme, we use an RNN as a generative model of dynamic input caused by the environment, e.g. of speech or kinematics. Given this generative RNN model, we derive Bayesian update equations that can decode its output. Critically, these updates define a 'recognizing RNN' (rRNN), in which neurons compute and exchange prediction and prediction error messages. The rRNN has several desirable features that a conventional RNN does not have, e.g. fast decoding of dynamic stimuli and robustness to initial conditions and noise. Furthermore, it implements a predictive coding scheme for dynamic inputs. We suggest that the Bayesian inversion of RNNs may be useful both as a model of brain function and as a machine learning tool. We illustrate the use of the rRNN by an application to the online decoding (i.e. recognition) of human kinematics.