Neural mechanisms for learning hierarchical structures of information

Spatial and temporal information from the environment is often hierarchically organized, so is our knowledge formed about the environment. Identifying the meaningful segments embedded in hierarchically structured information is crucial for cognitive functions, including visual, auditory, motor, memory, and language processing. Segmentation enables the grasping of the links between isolated entities, offering the basis for reasoning and thinking. Importantly, the brain learns such segmentation without external instructions. Here, we review the underlying computational mechanisms implemented at the single-cell and network levels. The network-level mechanism has an interesting similarity to machine-learning methods for graph segmentation. The brain possibly implements methods for the analysis of the hierarchical structures of the environment at multiple levels of its processing hierarchy.     

Neural mechanisms of reward-related motor learning

The analysis of the neural mechanisms responsible for reward-related learning has benefited from recent studies of the effects of dopamine on synaptic plasticity. Dopamine-dependent synaptic plasticity may lead to strengthening of selected inputs on the basis of an activity-dependent conjunction of sensory afferent activity, motor output activity, and temporally related firing of dopamine cells. Such plasticity may provide a link between the reward-related firing of dopamine cells and the acquisition of changes in striatal cell activity during learning. This learning mechanism may play a special role in the translation of reward signals into context-dependent response probability or directional bias in movement responses.     

Neural mechanisms of social learning and decision-making

One of the hallmarks of human society is the ubiquitous interactions among individuals. Indeed, a significant portion of human daily routine decision making is socially related. Normative economic theory, namely game theory, has prescribed the canonical decision strategy when "rational" social agents have full information about the decision environment. In reality, however, social decision is often influenced by the trait and state parameters of selves and others. Therefore, understanding the cognitive and neural processes of inferring the decision parameters is pivotal for social decision making. Recently, both correlational and causal non-invasive neuroimaging studies have started to reveal the critical neural computations underlying social learning and decision-making, and highlighted the unique roles of "social" brain structures such as temporal-parietal junction(TPJ) and dorsomedial prefrontal cortex(dmPFC). Here we review recent advances in social decision neuroscience and maintain the focus on how the inference about others is dynamically acquired during social learning, as well as how the prosocial(altruistic)behavior results from orchestrated interactions of different brain regions specified under the social utility framework. We conclude by emphasizing the importance of combining computational decision theory with the identification of neural mechanisms that represent, evaluate and integrate value related social information and generate decision variables guiding behavioral output in the complex social environment.     

Neural circuit mechanisms linking courtship and reward in Drosophila males

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An integrated web interface for large-scale characterization of sequence data

Large-scale genome projects require the analysis of large amounts of raw data. This analysis often involves the application of a chain of biology-based programs. Many of these programs are difficult to operate because they are non-integrated, command-line driven, and platform-dependent. The problem is compounded when the number of data files involved is large, making navigation and status-tracking difficult. To demonstrate how this problem can be addressed, we have created a platform-independent Web front end that integrates a set of programs used in a genomic project analyzing gene function by transposon mutagenesis in Saccharomyces cerevisiae. In particular, these programs help define a large number of transposon insertion events within the yeast genome, identifying both the precise site of transposon insertion as well as potential open reading frames disrupted by this insertion event. Our Web interface facilitates this analysis by performing the following tasks. Firstly, it allows each of the analysis programs to be launched against multiple directories of data files. Secondly, it allows the user to view, download, and upload files generated by the programs. Thirdly, it indicates which sets of data directories have been processed by each program. Although designed specifically to aid in this project, our interface exemplifies a general approach by which independent software programs may be integrated into an efficient protocol for large-scale genomic data processing. Electronic Publication     

Neural mechanisms and computations underlying stress effects on learning and memory

Stress has complex effects on memory function that can vary depending on the type of information that is learned and in relation to inter-individual characteristics. Recent work has also shown that stress can switch performance between memory systems, biasing it toward habit in detriment of spatial or goal-directed strategies. In addition, novel synaptic mechanisms have been implicated in the effects of stress in plasticity and memory. Computational modeling is emerging as a useful approach to integrate and to ascertain neural and cognitive computations underlying different effects of stress in memory. Having provided novel explanations for the inverted-U-shaped relationship between stress and cognitive performance, model-based analysis studies can improve our understanding of diverse effects of stress in cognition and psychopathology.     

Engineered chromosome regions with altered sequence composition demonstrate hierarchical large-scale folding within metaphase chromosomes

Mitotic chromosome structure and DNA sequence requirements for normal chromosomal condensation remain unknown. We engineered labeled chromosome regions with altered scaffold-associated region (SAR) sequence composition as a formal test of the radial loop and other chromosome models. Chinese hamster ovary cells were isolated containing high density insertions of a transgene containing lac operator repeats and a dihydrofolate reductase gene, with or without flanking SAR sequences. Lac repressor staining provided high resolution labeling with good preservation of chromosome ultrastructure. No evidence emerged for differential targeting of SAR sequences to a chromosome axis within native chromosomes. SAR sequences distributed uniformly throughout the native chromosome cross section and chromosome regions containing a high density of SAR transgene insertions showed normal diameter and folding. Ultrastructural analysis of two different transgene insertion sites, both spanning less than the full chromatin width, clearly contradicted predictions of simple radial loop models while providing strong support for hierarchical models of chromosome architecture. Specifically, an approximately 250-nm-diam folding subunit was visualized directly within fully condensed metaphase chromosomes. Our results contradict predictions of simple radial loop models and provide the first unambiguous demonstration of a hierarchical folding subunit above the level of the 30-nm fiber within normally condensed metaphase chromosomes.     

Evolution of homologous physiological mechanisms based on protein sequence data.

1. Genetic duplications can give rise to homologous physiological mechanisms that include structurally related protein components. There are many such examples of related proteins within the human body. 2. Evolutionary histories showing the origins and subsequent divergences of these distantly related proteins can be derived from the protein sequences and correlated with the functional characteristics of these proteins. 3. The hormones related to glucagon provide an example of homology of physiological mechanisms and emergence of new functions subsequent to gene duplications. 4. The proteins related to troponin C illustrate the participation of distantly related proteins in the same mechanism (muscle contraction), the relationship of proteins characteristic of a specialized tissue to proteins found in all eukaryote cells, and the correlation of genetic duplications with the evolutionary appearance of different types of muscle.     

Neural circuit mechanisms for pattern detection and feature combination in olfactory cortex

Odors are initially encoded in the brain as a set of distinct physicochemical characteristics but are ultimately perceived as a unified sensory object--a "smell." It remains unclear how chemical features encoded by diverse odorant receptors and segregated glomeruli in the main olfactory bulb (MOB) are assembled into integrated cortical representations. Combining patterned optical microstimulation of MOB with in vivo electrophysiological recordings in anterior piriform cortex (PCx), we assessed how cortical neurons decode complex activity patterns distributed across MOB glomeruli. PCx firing was insensitive to single-glomerulus photostimulation. Instead, individual cells reported higher-order combinations of coactive glomeruli resembling odor-evoked sensory maps. Intracellular recordings revealed a corresponding circuit architecture providing each cortical neuron with weak synaptic input from a distinct subpopulation of MOB glomeruli. PCx neurons thus detect specific glomerular ensembles, providing an explicit neural representation of chemical feature combinations that are the hallmark of complex odor stimuli.     

Neural mechanisms underlying uninstructed orofacial movements during reward-based learning behaviors

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VariScan: Analysis of evolutionary patterns from large-scale DNA sequence polymorphism data

SUMMARY: VeriScan is a software package for the analysis of DNA sequence polymorphisms at the whole genome scale. Among other features, the software (1) can conduct many population genetic analyses; (2) incorporates a multiresolution wavelet transform-based method that allows capturing relevant information from DNA polymorphism data; (3) facilitates the visualization of the results in the most commonly used genome browsers.     

Detecting genomic deletions from high-throughput sequence data with unsupervised learning

Background

Structural variation (SV), which ranges from 50 bp to \(\sim\) 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. Three types of signals, including discordant read-pairs, reads depth and split reads, are commonly used for SV detection from high-throughput sequence data. Many tools have been developed for detecting SVs by using one or multiple of these signals.

Results

In this paper, we develop a new method called EigenDel for detecting the germline submicroscopic genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates, and then it clusters similar candidates by using unsupervised learning methods. After that, EigenDel uses a carefully designed approach for calling true deletions from each cluster. We conduct various experiments to evaluate the performance of EigenDel on low coverage sequence data.

Conclusions

Our results show that EigenDel outperforms other major methods in terms of improving capability of balancing accuracy and sensitivity as well as reducing bias. EigenDel can be downloaded from https://github.com/lxwgcool/EigenDel.

     

Neural hierarchical models of ecological populations

Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This article describes a class of hierarchical models parameterised by neural networks – neural hierarchical models. The derivation of such models analogises the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non‐detection time series for hundreds of species, providing insights into colonisation and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modelling that combines the function representation power of neural networks with the inferential capacity of hierarchical models.     

Neural mechanisms of learning: an analysis of imprinting in the domestic chick

Learning is a complex set of processes involving the acquisition and storage of information. Imprinting in the domestic chick was studied to analyse the neural basis of storage. The recently hatched chick learns the characteristics of a visually conspicuous object by being exposed to it. When a chick is trained in this way, biochemical changes can be detected in the dorsal part of the forebrain. Through a series of experiments it was shown that these changes are unlikely to be non-specific consequences of training, but more probably reflect some aspect of the storage process. By using a radioautographic technique to localize the brain region more precisely, part of the hyperstriatum ventrale was implicated in this process. Bilateral destruction of the region before imprinting prevented acquisition, and bilateral destruction after imprinting impaired retention. After exposure for 140 min to an imprinting stimulus there was an increase in the area of contact between presynaptic and postsynaptic elements in the region. This effect was found on the left side only. Sequential lesions to left and right sides confirmed that there is a hemispheric asymmetry in the role of the region in the storage of information. The area receives input from the visual pathways and possibly from other sensory pathways, and projects to regions that are thought to be involved in the control of locomotor and viscero-endocrine functions. The results afford an opportunity for the further analysis both of storage and of the whole set of neural processes that underlie imprinting in the domestic chick.U     

Neural and psychological mechanisms underlying appetitive learning: links to drug addiction

The complexity of drug addiction mirrors the complexity of the psychological processes that motivate animals to work for any reinforcer, be it a natural reward or a drug. Here, we review the role of the nucleus accumbens, together with its dopaminergic and cortical innervation, in responding to reinforcement. One important contribution made by the nucleus accumbens is to the process through which neutral stimuli, once paired with a reinforcer such as a drug, have the capacity to motivate behaviour. This process may be one of several contributing to addiction, and it may be amenable to pharmacological intervention.     

Neural mechanisms of human perceptual learning: electrophysiological evidence for a two-stage process

Background

Humans and other animals change the way they perceive the world due to experience. This process has been labeled as perceptual learning, and implies that adult nervous systems can adaptively modify the way in which they process sensory stimulation. However, the mechanisms by which the brain modifies this capacity have not been sufficiently analyzed.

Methodology/Principal Findings

We studied the neural mechanisms of human perceptual learning by combining electroencephalographic (EEG) recordings of brain activity and the assessment of psychophysical performance during training in a visual search task. All participants improved their perceptual performance as reflected by an increase in sensitivity (d'') and a decrease in reaction time. The EEG signal was acquired throughout the entire experiment revealing amplitude increments, specific and unspecific to the trained stimulus, in event-related potential (ERP) components N2pc and P3 respectively. P3 unspecific modification can be related to context or task-based learning, while N2pc may be reflecting a more specific attentional-related boosting of target detection. Moreover, bell and U-shaped profiles of oscillatory brain activity in gamma (30–60 Hz) and alpha (8–14 Hz) frequency bands may suggest the existence of two phases for learning acquisition, which can be understood as distinctive optimization mechanisms in stimulus processing.

Conclusions/Significance

We conclude that there are reorganizations in several neural processes that contribute differently to perceptual learning in a visual search task. We propose an integrative model of neural activity reorganization, whereby perceptual learning takes place as a two-stage phenomenon including perceptual, attentional and contextual processes.