Krasnow Institute for Advanced Study

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The Krasnow Institute seeks to expand understanding of mind, brain, and intelligence by conducting research at the intersection of the separate fields of cognitive psychology, neurobiology, and the computer-driven study of artificial intelligence and complex adaptive systems. These separate disciplines increasingly overlap and promise progressively deeper insight into human thought processes. The Institute also examines how new insights from cognitive science research can be applied for human benefit in the areas of mental health, neurological disease, education, and computer design.

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A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia
(Frontiers Media, 2015-03-25) Faghihi, Faramarz; Moustafa, Ahmed A.
Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed.
A Neural Mechanism for Background Information-Gated Learning Based on Axonal-Dendritic Overlaps
(Public Library of Science, 2015-03-13) Mainetti, Matteo; Ascoli, Giorgio A.
Experiencing certain events triggers the acquisition of new memories. Although necessary, however, actual experience is not sufficient for memory formation. One-trial learning is also gated by knowledge of appropriate background information to make sense of the experienced occurrence. Strong neurobiological evidence suggests that long-term memory storage involves formation of new synapses. On the short time scale, this form of structural plasticity requires that the axon of the pre-synaptic neuron be physically proximal to the dendrite of the post-synaptic neuron. We surmise that such “axonal-dendritic overlap” (ADO) constitutes the neural correlate of background information-gated (BIG) learning. The hypothesis is based on a fundamental neuroanatomical constraint: an axon must pass close to the dendrites that are near other neurons it contacts. The topographic organization of the mammalian cortex ensures that nearby neurons encode related information. Using neural network simulations, we demonstrate that ADO is a suitable mechanism for BIG learning. We model knowledge as associations between terms, concepts or indivisible units of thought via directed graphs. The simplest instantiation encodes each concept by single neurons. Results are then generalized to cell assemblies. The proposed mechanism results in learning real associations better than spurious co-occurrences, providing definitive cognitive advantages.
A representative sample of web-accessible Neuroinformatics resources surveyed for its availability and contribution to the community in the past 5 years (2010 - 2014)
(2015-02-11) Polavaram, Sridevi
An Agent-Based Model of Climate Change and Conflict among Pastoralists in East Africa
(International Environmental Modelling and Software Society (iEMSs), 2010) Hailegiorgis, A.B.; Kennedy, W.G.; Balan, G.C.; Bassett, J.K.; Gulden, T.
Presented is an agent-based model of human-environment interaction and conflict in East Africa using the MASON agent-based simulation environment. The model focuses on the complex interaction of pastoral groups with their environment and other emerging external actors. The model supports the observation that increased seasonal rainfall variability and droughts create tremendous stress on pastoralists groups and challenges their long-term resilience and adaptive response mechanisms.
An Agent-Based Model of Conflict in East Africa and the Effect of Watering Holes
(Conference on Behavior Representation in Modeling and Simulation, 2010-03) Kennedy, W.G.; Hailegiorgis, A.B.; Rouleau, M.; Bassett, J.K.; Coletti, M.; Balan, G.C.; Gulden, T.
An agent-based model conflict between herdsmen in east Africa using the MASON agent-based simulation environment is presented. Herders struggle to keep their herds fed and watered in a GIS-based, spatially diverse environment with data-driven seasonal cycles. The model produces realistic carrying capacity dynamics and basically plausible conflict dynamics. With the rather basic set of behaviors, herders come into conflict over limited resources and one clan is eventually eliminated. We find that greater environmental scarcity leads to faster domination by a single group. At the same time, we note that there is tremendous variability from run to run in the rate and timing of the transition from a conflict-prone, multi-clan environment to hegemony of a single group.
Augmenting Weak Semantic Cognitive Maps with an “Abstractness” Dimension
(Hindawi Publishing, 2013-04-29) Samsonovich, Alexei
The emergent consensus on dimensional models of sentiment, appraisal, emotions, and values is on the semantics of the principal dimensions, typically interpreted as valence, arousal, and dominance. The notion of weak semantic maps was introduced recently as distribution of representations in abstract spaces that are not derived from human judgments, psychometrics, or any other a priori information about their semantics. Instead, they are defined entirely by binary semantic relations among representations, such as synonymy and antonymy. An interesting question concerns the ability of the antonymy-based semantic maps to capture all “universal” semantic dimensions. The present work shows that those narrow weak semantic maps are not complete in this sense and can be augmented with other semantic relations. Specifically, including hyponym-hypernym relations yields a new semantic dimension of the map labeled here “abstractness” (or ontological generality) that is not reducible to any dimensions represented by antonym pairs or to traditional affective space dimensions. It is expected that including other semantic relations (e.g., meronymy/holonymy) will also result in the addition of new semantic dimensions to the map. These findings have broad implications for automated quantitative evaluation of the meaning of text and may shed light on the nature of human subjective experience.
Chemical crosslinkers enhance detection of receptor interactomes
(Frontiers Media, 2014-01-07) Corgiat, Brian A.; Nordman, Jacob C.; Kabbani, Nadine
Receptor function is dependent on interaction with various intracellular proteins that ensure the localization and signaling of the receptor. While a number of approaches have been optimized for the isolation, purification, and proteomic characterization of receptor–protein interaction networks (interactomes) in cells, the capture of receptor interactomes and their dynamic properties remains a challenge. In particular, the study of interactome components that bind to the receptor with low affinity or can rapidly dissociate from the macromolecular complex is difficult. Here we describe how chemical crosslinking (CC) can aid in the isolation and proteomic analysis of receptor–protein interactions. The addition of CC to standard affinity purification and mass spectrometry protocols boosts the power of protein capture within the proteomic assay and enables the identification of specific binding partners under various cellular and receptor states. The utility of CC in receptor interactome studies is highlighted for the nicotinic acetylcholine receptor as well as several other receptor types. A better understanding of receptors and their interactions with proteins spearheads molecular biology, informs an integral part of bench medicine which helps in drug development, drug action, and understanding the pathophysiology of disease.
Comparing Agent-Based Computational Simulation Models in Cross-Cultural Research
(SAGE, 2011-05) Cioffi-Revilla, Claudio
Mel Ember was co-Principal Investigator in the Mason-HRAF Joint Project on Eastern Africa, a multiyear project aimed at developing and analyzing advanced computational agent-based models of human societies across 10 countries and 12 ecosystems. A major unsolved challenge in this kind of social science research is to devise a systematic way to compare, contrast, and communicate different models of social dynamics along relevant dimensions and characteristics, given the inherent complexity of most computational agent-based models. This article proposes a viable systematic framework for comparing models and illustrates its application using some of the models that Mel helped inspire and develop as senior project participant.
Complex Polities in the Age of Modern States
(International Studies Association, 2011-03-16) Cioffi-Revilla, Claudio
Complex polities are political systems composed of both official "vertical" state institutions as well as one or more alternative set of "horizontal" institutions, such as religious, economic, paramilitary, or even criminal organizations. Both vertical and horizontal polities that compose complex polities have policy-making capacity engaged in the provision of public (and in some cases private) goods aimed at addressing various societal needs. While complex polities have existed since early antiquity, from a world historical perspective it is only since ca. 1500 CE and the formation of modern European states that contending vertical and horizontal polities have produced specialized institutions in competition and collaboration with the state. Moreover, complex polities for global governance also appear in the world system since ca. 1500 CE. This paper will present a theory of complex polities based on a computational perspective that is implemented in agent-based models of coupled socio-techno-natural systems - i.e., systems of governance that integrate societies and natural environments through artificial systems that mediate between the two at many scales, from local to global.
Conflict in Complex Socio-Natural Systems: Using Agent-Based Modeling to Understand the Behavioral Roots of Social Unrest within the Mandera Triangle
(Human Behavior-Computational Modeling and Interoperability Conference, 2009-06) Rouleau, Mark; Coletti, Mark; Bassett, Jeffrey K.; Hailegiorgis, Atesmachew B.; Gulden, Tim; Kennedy, William G.
Conflict resolution research relies upon a deep understanding of human behavior within highly complex socionatural systems. Scholars must isolate the source of conflict among individuals reacting to the feedback of changing socionatural conditions. Fortunately, the oft-obscured roots of conflict typically surface at critical points of change within the system. We use the Mandera Triangle region of East Africa as an example of this surfacing of behavioral drivers. Our research fuses a wide range of backgrounds to construct a simulation model of Mandera and to gain a better understanding of the roots of human behavior in relation to social conflict.
Controlling Seizure-Like Events by Perturbing Ion Concentration Dynamics with Periodic Stimulation
(Public Library of Science, 2013-09-16) Owen, Jeremy A.; Barreto, Ernest; Cressman, John R.
We investigate the effects of adding periodic stimulation to a generic, conductance-based neuron model that includes ion concentration dynamics of sodium and potassium. Under conditions of high extracellular potassium, the model exhibits repeating, spontaneous, seizure-like bursting events associated with slow modulation of the ion concentrations local to the neuron. We show that for a range of parameter values, depolarizing and hyperpolarizing periodic stimulation pulses (including frequencies lower than 4 Hz) can stop the spontaneous bursting by interacting with the ion concentration dynamics. Stimulation can also control the magnitude of evoked responses to modeled physiological inputs. We develop an understanding of the nonlinear dynamics of this system by a timescale separation procedure that identifies effective nullclines in the ion concentration parameter space. Our results suggest that the manipulation of ion concentration dynamics via external or endogenous stimulation may play an important role in neuronal excitability, seizure dynamics, and control.
Dynamical structure underlying inverse stochastic resonance and its implications
(American Physical Society, 2013-10-31) Uzuntarla, Muhammet; Cressman, John R.; Ozer, Mahmut; Barreto, Ernest
We investigate inverse stochastic resonance (ISR), a recently reported phenomenon in which the spiking activity of a Hodgkin-Huxley model neuron subject to external noise exhibits a pronounced minimum as the noise intensity increases.We clarify the mechanism that underlies ISR and show that its most surprising features are a consequence of the dynamical structure of the model. Furthermore, we show that the ISR effect depends strongly on the procedures used to measure it. Our results are important for the experimentalist who seeks to observe the ISR phenomenon.
Employment Growth through Labor Flow Networks
(Public Library of Science, 2013-05-02) Guerrero, Omar A.; Axtell, Robert L.
It is conventional in labor economics to treat all workers who are seeking new jobs as belonging to a labor pool, and all firms that have job vacancies as an employer pool, and then match workers to jobs. Here we develop a new approach to study labor and firm dynamics. By combining the emerging science of networks with newly available employment micro-data, comprehensive at the level of whole countries, we are able to broadly characterize the process through which workers move between firms. Specifically, for each firm in an economy as a node in a graph, we draw edges between firms if a worker has migrated between them, possibly with a spell of unemployment in between. An economy's overall graph of firm-worker interactions is an object we call the labor flow network (LFN). This is the first study that characterizes a LFN for an entire economy. We explore the properties of this network, including its topology, its community structure, and its relationship to economic variables. It is shown that LFNs can be useful in identifying firms with high growth potential. We relate LFNs to other notions of high performance firms. Specifically, it is shown that fewer than 10% of firms account for nearly 90% of all employment growth. We conclude with a model in which empirically-salient LFNs emerge from the interaction of heterogeneous adaptive agents in a decentralized labor market.
Environmental influences on neural systems of relational complexity
(Frontiers, 2013-09-26) Kalbfleisch, M. Layne; deBettencourt, Megan T.; Kopperman, Rebecca; Banasiak, Meredith; Roberts, Joshua M.; Halavi, Maryam
Constructivist learning theory contends that we construct knowledge by experience and that environmental context influences learning. To explore this principle, we examined the cognitive process relational complexity (RC), defined as the number of visual dimensions considered during problem solving on a matrix reasoning task and a well-documented measure of mature reasoning capacity. We sought to determine how the visual environment influences RC by examining the influence of color and visual contrast on RC in a neuroimaging task. To specify the contributions of sensory demand and relational integration to reasoning, our participants performed a non-verbal matrix task comprised of color, no-color line, or black-white visual contrast conditions parametrically varied by complexity (relations 0, 1, 2). The use of matrix reasoning is ecologically valid for its psychometric relevance and for its potential to link the processing of psychophysically specific visual properties with various levels of RC during reasoning. The role of these elements is important because matrix tests assess intellectual aptitude based on these seemingly context-less exercises. This experiment is a first step toward examining the psychophysical underpinnings of performance on these types of problems. The importance of this is increased in light of recent evidence that intelligence can be linked to visual discrimination. We submit three main findings. First, color and black-white visual contrast (BWVC) add demand at a basic sensory level, but contributions from color and from BWVC are dissociable in cortex such that color engages a “reasoning heuristic” and BWVC engages a “sensory heuristic.” Second, color supports contextual sense-making by boosting salience resulting in faster problem solving. Lastly, when visual complexity reaches 2-relations, color and visual contrast relinquish salience to other dimensions of problem solving.
Evolutionary Computation and Agent-based Modeling: Biologically-inspired Approaches for Understanding Complex Social Systems
(Kluwer, 2012-06-18) Cioffi-Revilla, Claudio; De Jong, Kenneth; Bassett, Jeffrey
Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results sug- gest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change.
Functional Genomic Analyses of Two Morphologically Distinct Classes of Drosophila Sensory Neurons: Post-Mitotic Roles of Transcription Factors in Dendritic Patterning
(Public Library of Science, 2013-08-15) Iyer, Eswar Prasad R.; Iyer, Srividya Chandramouli; Sullivan, Luis; Wang, Dennis; Meduri, Ramakrishna; Graybeal, Lacey L.; Cox, Daniel N.
Background Neurons are one of the most structurally and functionally diverse cell types found in nature, owing in large part to their unique class specific dendritic architectures. Dendrites, being highly specialized in receiving and processing neuronal signals, play a key role in the formation of functional neural circuits. Hence, in order to understand the emergence and assembly of a complex nervous system, it is critical to understand the molecular mechanisms that direct class specific dendritogenesis. Methodology/Principal Findings We have used the Drosophila dendritic arborization (da) neurons to gain systems-level insight into dendritogenesis by a comparative study of the morphologically distinct Class-I (C-I) and Class-IV (C-IV) da neurons. We have used a combination of cell-type specific transcriptional expression profiling coupled to a targeted and systematic in vivo RNAi functional validation screen. Our comparative transcriptomic analyses have revealed a large number of differentially enriched/depleted gene-sets between C-I and C-IV neurons, including a broad range of molecular factors and biological processes such as proteolytic and metabolic pathways. Further, using this data, we have identified and validated the role of 37 transcription factors in regulating class specific dendrite development using in vivo class-specific RNAi knockdowns followed by rigorous and quantitative neurometric analysis. Conclusions/Significance This study reports the first global gene-expression profiles from purified Drosophila C-I and C-IV da neurons. We also report the first large-scale semi-automated reconstruction of over 4,900 da neurons, which were used to quantitatively validate the RNAi screen phenotypes. Overall, these analyses shed global and unbiased novel insights into the molecular differences that underlie the morphological diversity of distinct neuronal cell-types. Furthermore, our class-specific gene expression datasets should prove a valuable community resource in guiding further investigations designed to explore the molecular mechanisms underlying class specific neuronal patterning.
Geographic Information Systems (GIS) and Spatial Agent-Based Model (ABM) Simulations for Sustainable Development
(Association for Computing Machinery (ACM), 2011-10) Cioffi-Revilla, Claudio; Roger, J. Daniel; Hailegiorgis, Atesmachew
In recent years the interdisciplinary field of Computational Social Science has developed theory and methodologies for building spatial Agent-Based Social Simulation (ABSS) models of human societies that are situated in ecosystems with land cover and climate. This article explains the needs and demand for Geographic Information Systems (GIS) in these types of agent-based models, with an emphasis on models applied to Eastern Africa and Inner Asia and relevance for understanding and analyzing development issues. The models are implemented with the MASON (Multi-Agent Simulator Of Networks and Neighborhoods) system, an open-source simulation environment in the Java language and suitable for developing ABSS models with GIS for representing spatial features.
GeoMason: Geospatial Support for MASON
(Department of Computer Science, George Mason University, 2010) Sullivan, Keith; Coletti, Mark; Luke, Sean
MASON is a free, open-source Java-based discrete event multi-agent simulation toolkit that has been used to model network intrusions, unmanned aerial vehicles, nomadic migrations, and farmer/herder conflicts, among others. Many multi-agent models use georeferenced data which represent such things as road networks, rivers, vegetation coverage, population, and topology. However, MASON does not directly support georeferenced data. Therefore practitioners using MASON must hand craft such support, which may be difficult and error prone. In this paper we describe newly added geospatial functionality in MASON that addresses this problem. We discuss the design of this functionality, called GeoMASON, and its use and limitations. Finally, we give examples on how to import and manipulate georeferenced data.
In silico prediction of phosphorylation of NS3 as an essential mechanism for Dengue virus replication and the antiviral activity of Quercetin
(Biology, 2019) Alomair, Lamya Abdulaziz; Almsned, Fahad; Ullah, Aman; Jafri, M. Saleet
Infection by Dengue virus is a global health problem for which there have been challenges to obtaining a cure. Current vaccines can only be narrowly applied in ongoing clinical trials. We employed computational methods to predict therapeutic efficacy based on structure-function relationships between human host kinases and viral Nonstructural Protein 3(NS3) in an effort to understand the therapeutic effect of inhibitors of viral replication. Phosphorylation at each of two most evolutionarily conserved sites, S137 and T189 compared to the unphosphorylated state were studied with molecular dynamics and docking simulations. The simulations suggested that phosphorylation at S137 caused a greater structural change than phosphorylation at T189. Docking studies supported the idea that phosphorylation at S137 increased the binding affinity between NS3 and NS5,whereas, phosphorylation at T189 decreased it. The interaction of NS3 and NS5is essential for viral replication. Docking studies with the antiviral plant flavonoid Quercetin with NS3 indicated that Quercetin physically occluded theS137 phosphorylation site. Taken together these findings suggest a specific site and mechanism by which Quercetin inhibits Dengue and possible other flaviviruses.
Integrating Fast and Slow Cognitive Processes
(International Conference on Cognitive Modeling, 2010-08) Kennedy, William, G.; Bugjska, Magdalena
Human reactions appear to be controlled by two separate types of mental processes: one fast, automatic, and unconscious and the other slow, deliberate, and conscious. With the attention in the literature focused on the taxonomy of the two processes, there is little discussion of how they interact. In this paper, we focus on modeling the slower process’s ability to inhibit the fast process. We present computational cognitive models in which different strategies allow a human to consciously inhibit an undesirable fast response. These general strategies include (a) blocking sensory input, (b), blocking or interrupting the fast process’s response, and (c) slowing down or delaying processing by introducing additional task. Furthermore, we discuss an approach to learning such strategies based on the inference of the causes and effects of the fast process.

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