Two-dimensional abstract representations (signatures) of proteins

Algorithms have been developed to allow two-dimensional abstractrepresentations of proteins based on: (i) a non-redundant subsetof codons, (ii) hydropathy values of amino acids, (iii) thepolarities of the amino acid residues and (iv) predicted secondarystructures. In addition the suggestion of Gates on nucleic acidrepresentation was implemented. The two-dimensional projections(signatures) that are obtained have the merit that several plotscan be represented simultaneously for ready visualization. Theapproach appears useful in showing relationships among groupsof proteins. Received on September 22, 1986; accepted on November 25, 1986     

Moral concepts set decision strategies to abstract values

Persons have different value preferences. Neuroimaging studies where value-based decisions in actual conflict situations were investigated suggest an important role of prefrontal and cingulate brain regions. General preferences, however, reflect a superordinate moral concept independent of actual situations as proposed in psychological and socioeconomic research. Here, the specific brain response would be influenced by abstract value systems and moral concepts. The neurobiological mechanisms underlying such responses are largely unknown. Using functional magnetic resonance imaging (fMRI) with a forced-choice paradigm on word pairs representing abstract values, we show that the brain handles such decisions depending on the person's superordinate moral concept. Persons with a predominant collectivistic (altruistic) value system applied a "balancing and weighing" strategy, recruiting brain regions of rostral inferior and intraparietal, and midcingulate and frontal cortex. Conversely, subjects with mainly individualistic (egocentric) value preferences applied a "fight-and-flight" strategy by recruiting the left amygdala. Finally, if subjects experience a value conflict when rejecting an alternative congruent to their own predominant value preference, comparable brain regions are activated as found in actual moral dilemma situations, i.e., midcingulate and dorsolateral prefrontal cortex. Our results demonstrate that superordinate moral concepts influence the strategy and the neural mechanisms in decision processes, independent of actual situations, showing that decisions are based on general neural principles. These findings provide a novel perspective to future sociological and economic research as well as to the analysis of social relations by focusing on abstract value systems as triggers of specific brain responses.     

Creating abstract topographic representations: Implications for coding,learning and reasoning

Topographic maps are a fundamental and ubiquitous feature of the sensory and motor regions of the brain. There is less evidence for the existence of conventional topographic maps in associational areas of the brain such as the prefrontal cortex and parietal cortex. The existence of topographically arranged anatomical projections is far more widespread and occurs in associational regions of the brain as well as sensory and motor regions: this points to a more widespread existence of topographically organised maps within associational cortex than currently recognised. Indeed, there is increasing evidence that abstract topographic representations may also occur in these regions. For example, a topographic mnemonic map of visual space has been described in the dorsolateral prefrontal cortex and topographically arranged visuospatial attentional signals have been described in parietal association cortex. This article explores how abstract representations might be extracted from sensory topographic representations and subsequently code abstract information. Finally a simple model is presented that shows how abstract topographic representations could be integrated with other information within the brain to solve problems or form abstract associations. The model uses correlative firing to detect associations between different types of stimuli. It is flexible because it can produce correlations between information represented in a topographic or non-topographic coordinate system. It is proposed that a similar process could be used in high-level cognitive operations such as learning and reasoning.     

Taste representations in the Drosophila brain

Drosophila taste compounds with gustatory neurons on many parts of the body, suggesting that a fly detects both the location and quality of a food source. For example, activation of taste neurons on the legs causes proboscis extension or retraction, whereas activation of proboscis taste neurons causes food ingestion or rejection. We examined whether the features of taste location and taste quality are mapped in the fly brain using molecular, genetic, and behavioral approaches. We find that projections are segregated by the category of tastes that they recognize: neurons that recognize sugars project to a region different from those recognizing noxious substances. Transgenic axon labeling experiments also demonstrate that gustatory projections are segregated based on their location in the periphery. These studies reveal the gustatory map in the first relay of the fly brain and demonstrate that taste quality and position are represented in anatomical projection patterns.     

From abstract topology to real thermodynamic brain activity

Recent approaches to brain phase spaces reinforce the foremost role of symmetries and energy requirements in the assessment of nervous activity. Changes in thermodynamic parameters and dimensions occur in the brain during symmetry breakings and transitions from one functional state to another. Based on topological results and string-like trajectories into nervous energy landscapes, we provide a novel method for the evaluation of energetic features and constraints in different brain functional activities. We show how abstract approaches, namely the Borsuk–Ulam theorem and its variants, may display real, energetic physical counterparts. When topology meets the physics of the brain, we arrive at a general model of neuronal activity, in terms of multidimensional manifolds and computational geometry, that has the potential to be operationalized.     

Measuring internal representations from behavioral and brain data

The study of internal knowledge representations is a cornerstone of the research agenda in the interdisciplinary study of cognition. An influential proposal assumes that the brain uses its internal knowledge of the external world to constrain, in a top-down manner, high-dimensional sensory data into a lower-dimensional representation that enables perceptual decisions and other higher-level cognitive functions [1-9]. This proposal relies on a precise formulation of the observer-specific internal knowledge (i.e., the internal representations, or models) that guides reduction of the high-dimensional retinal input onto a low-dimensional code. Here, we directly revealed the content of subjective internal representations by instructing five observers to detect a face in the presence of only white noise, to force a pure top-down, knowledge-based task. We used reverse correlation methods to visualize each observer's internal representation that supports detection of an illusory face. Using reverse correlation again, this time applied to observers' electroencephalogram activity, we established where and when in the brain specific internal knowledge conceptually interprets the input white noise as a face. We show that internal representations can be reconstructed experimentally from behavioral and brain data, and that their content drives neural activity first over frontal and then over occipitotemporal cortex.     

HYLAS: program for generating H curves (abstract three-dimensional representations of long DNA sequences)

The computer program HYLAS generates from a standard DNA lettersequence a three-dimensional space curve (H curve) which embodiesthe entire information content of the original nucleotide sequence.The program can display H curves either as two-dimensional (frontand side view) projections or as stereo-pair images. The curvescan be marked at specific nucleotide locations, annotated, rotatedfor observation from any viewing angle, and manipulated forconvenient side-by-side comparisons. Unlike the cumbersome lettersequences, H curves can be drastically condensed in size withoutlosing their ability to reflect the global nucleotide-distributionpattern of the entire DNA sequence. Often, biologically importantloci can be visually identified on the H curves. HYLAS is writtenin FORTRAN with separate mainframe (IBM- VM/CMS) and microcomputer(MS-DOS) versions. It uses the Tektronix-TCS library of graphicsubroutines. Received on October 24, 1988; accepted on July 15, 1989     

Object recognition: holistic representations in the monkey brain

Cognitive-psychological and neuropsychological studies suggest that the human brain processes facial information in a distinct manner, relying on mechanisms that are anatomically and functionally different from those underlying the recognition of other objects. Face recognition, for instance, can be disrupted selectively as a result of localized brain damage, and relies strongly on holistic information rather than on the mere processing of local features. Similarly, in the non-human primate, distinct neocortical and limbic structures have cell populations responding specifically to face stimuli and only weakly to other visual patterns. Moreover, such cells tend to respond to the entire configuration of a face rather than to individual facial features. But are faces the only objects represented in this way? Here I present some evidence suggesting that at least one aspect of facial processing, the processing of holistic information, may be employed by the primate brain when recognizing any arbitrary homogeneous class of even artificial objects, which the monkey has to individually learn, remember, and recognize again and again from among a large number of distractors sharing a number of common features with the target. Acquiring such an expertise can induce configurational selectivity in the response of neurons in the visual system. Our findings suggests that regarding their neural encoding faces are unlikely to be 'special', but they rather are the default 'special class' of the primate visual system.     

Extracting representations of cognition across neuroimaging studies improves brain decoding

Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.     

Transforming bottom-up topographic representations with top-down signals in the brain

There has been considerable success in allocating function to the different parts of the brain. We also know much about brain organisation in different regions of the brain and how different brain regions connect to one another. One of the most important next steps for modern neuroscience is to work out how different areas of the brain interact with one another. In particular we need to know how sensory regions communicate with association areas and vice versa. This article explores how top-down signals originating from association areas may be used to process and transform bottom-up representations originating from sensory areas of the brain. Simple models of networks containing topographically organised ensembles of neurons used to integrate and process information are described. The different models can be used to process information in a variety of different ways that could be used as the starting point for a variety of cognitive operations, in particular the extraction of abstract information from sensory representations.     

Transforming bottom-up topographic representations with top-down signals in the brain

There has been considerable success in allocating function to the different parts of the brain. We also know much about brain organisation in different regions of the brain and how different brain regions connect to one another. One of the most important next steps for modern neuroscience is to work out how different areas of the brain interact with one another. In particular we need to know how sensory regions communicate with association areas and vice versa. This article explores how top-down signals originating from association areas may be used to process and transform bottom-up representations originating from sensory areas of the brain. Simple models of networks containing topographically organised ensembles of neurons used to integrate and process information are described. The different models can be used to process information in a variety of different ways that could be used as the starting point for a variety of cognitive operations, in particular the extraction of abstract information from sensory representations.     

Coding of distributed, topographic and non-specific representations within the brain

This article explores the theoretical basis of coding within topographic representations, where neurons encoding specific features such as locations, are arranged into maps. A novel type of representation, termed non-specific, where each neuron does not encode specific features is also postulated. In common with the previously described distributed representations [Rolls, E.T., Treves, A., 1998. Neural Networks and Brain Function. Oxford University Press, Oxford], topographic representations display an exponential relationship between stimuli encoded and both number of neurons and maximum firing rate of those neurons. The non-specific representations described here display a binomial expansion between the number of stimuli encoded and the sum of the number of neurons and the maximum firing rate; therefore groups of non-specific neurons usually encode less stimuli than equivalent topographic layers of neurons. Lower and higher order sensory regions of the brain use either topographic or distributed representations to encode information. It is proposed that non-specific representations may occur in regions of the brain where different types of information may be represented by the same neurons, as occurs in the prefrontal cortex.     

Modelling neurodegenerative diseases with 3D brain organoids

Neurodegenerative diseases are incurable and debilitating conditions characterized by the deterioration of brain function. Most brain disease models rely on human post‐mortem brain tissue, non‐human primate tissue, or in vitro two‐dimensional (2D) experiments. Resource limitations and the complexity of the human brain are some of the reasons that make suitable human neurodegenerative disease models inaccessible. However, recently developed three‐dimensional (3D) brain organoids derived from pluripotent stem cells (PSCs), including embryonic stem cells and induced PSCs, may provide suitable models for the study of the pathological features of neurodegenerative diseases. In this review, we provide an overview of existing 3D brain organoid models and discuss recent advances in organoid technology that have increased our understanding of brain development. Moreover, we explain how 3D organoid models recapitulate aspects of specific neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease, and explore the utility of these models, for therapeutic applications.     

Reconciling neuronal representations of schema,abstract task structure,and categorization under cognitive maps in the entorhinal-hippocampal-frontal circuits

Learning leads to a neuronal representation of acquired knowledge. This idea of knowledge representation was traditionally developed as a “cognitive map” of spatial memory represented in the hippocampus. The framework of cognitive mapping has been extended in the past decade to include not only spatial memory, but also non-spatial factual and temporal memory. Following this conceptual advancement, a line of recent neurophysiological research discovered such knowledge representations not only in the hippocampus, but also in the entorhinal cortex and frontal cortex. Although the distinct terms “cognitive map,” “schema,” “abstract task structure” or “categorization” were used in these studies, it is likely that these terms can be reconciled as a common mechanism of learned knowledge representations. Future experimental work will be required to differentiate the parametric nature of knowledge representations across brain areas.