92,541 research outputs found
Predictability and hierarchy in Drosophila behavior
Even the simplest of animals exhibit behavioral sequences with complex
temporal dynamics. Prominent amongst the proposed organizing principles for
these dynamics has been the idea of a hierarchy, wherein the movements an
animal makes can be understood as a set of nested sub-clusters. Although this
type of organization holds potential advantages in terms of motion control and
neural circuitry, measurements demonstrating this for an animal's entire
behavioral repertoire have been limited in scope and temporal complexity. Here,
we use a recently developed unsupervised technique to discover and track the
occurrence of all stereotyped behaviors performed by fruit flies moving in a
shallow arena. Calculating the optimally predictive representation of the fly's
future behaviors, we show that fly behavior exhibits multiple time scales and
is organized into a hierarchical structure that is indicative of its underlying
behavioral programs and its changing internal states
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Structured modeling for VHDL synthesis
This report will describe a proposed modeling style for the use of the VHSIC Hardware Description Language (VHDL) in design synthesis. We will describe the operations and underlying assumptions of four design models currently understood and used in practice by designers: combinational logic, functional descriptions (involving clocked components such as counters), register transfer (data path) descriptions, and behavioral (instruction set or processor) designs. We will illustrate the various uses of the VHDL description styles (structural, dataflow and behavioral) to represent characteristics of each of these design models. Emphasis is placed on how VHDL constructs should be used in order to synthesize optimal designs
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Boundedly rational versus optimization-based models of strategic thinking and learning in games
The paper is a comment on the article by R. Harstad and R. Selten and considers the tradeoff between bounded rationality and optimization models in the game-theoretic context. The author shows that in most of the models elements of opimization are still retained and that it is thus more productive to further improve the optimization-based modeling rather than to abandon them altogether in favour of bounded rationality
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Behavioral synthesis from VHDL using structured modeling
This dissertation describes work in behavioral synthesis involving the development of a VHDL Synthesis System VSS which accepts a VHDL behavioral input specification and performs technology independent synthesis to generate a circuit netlist of generic components. The VHDL language is used for input and output descriptions. An intermediate representation which incorporates signal typing and component attributes simplifies compilation and facilitates design optimization.A Structured Modeling methodology has been developed to suggest standard VHDL modeling practices for synthesis. Structured modeling provides recommendations for the use of available VHDL description styles so that optimal designs will be synthesized.A design composed of generic components is synthesized from the input description through a process of Graph Compilation, Graph Criticism, and Design Compilation. Experiments were performed to demonstrate the effects of different modeling styles on the quality of the design produced by VSS. Several alternative VHDL models were examined for each benchmark, illustrating the improvements in design quality achieved when Structured Modeling guidelines were followed
The evolutionary origins of hierarchy
Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limited to quantifying this property. However, an open, important
question in evolutionary biology is why hierarchical organization evolves in
the first place. It has recently been shown that modularity evolves because of
the presence of a cost for network connections. Here we investigate whether
such connection costs also tend to cause a hierarchical organization of such
modules. In computational simulations, we find that networks without a
connection cost do not evolve to be hierarchical, even when the task has a
hierarchical structure. However, with a connection cost, networks evolve to be
both modular and hierarchical, and these networks exhibit higher overall
performance and evolvability (i.e. faster adaptation to new environments).
Additional analyses confirm that hierarchy independently improves adaptability
after controlling for modularity. Overall, our results suggest that the same
force--the cost of connections--promotes the evolution of both hierarchy and
modularity, and that these properties are important drivers of network
performance and adaptability. In addition to shedding light on the emergence of
hierarchy across the many domains in which it appears, these findings will also
accelerate future research into evolving more complex, intelligent
computational brains in the fields of artificial intelligence and robotics.Comment: 32 page
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Balancing risks and rewards: the logic of violence
Violence is widespread throughout the natural world, prominent examples being predatory violence between species, seasonal violent competition for mating rights and territories within species and food competition both within and between species. These interactions are generally between unrelated individuals with no social connection. There are, however, examples of violent behaviour which occurs within groups of individuals who otherwise cooperate to live, have significant social bonds and may also be related, and that is the primary focus of this paper. Examples are in the establishment and maintenance of dominance hierarchies, or in infanticide, where (usually) incoming males attempt to kill existing infants in a group. Such violence can seem paradoxical, but in fact is often perfectly logical for the individual perpetrating the violence, as distinct from the group as a whole. We discuss such situations from the perspective of evolutionary game theory, and also consider wider questions of interspecific violence
A Formal Separation Between Strategic and Nonstrategic Behavior
It is common in multiagent systems to make a distinction between "strategic"
behavior and other forms of intentional but "nonstrategic" behavior: typically,
that strategic agents model other agents while nonstrategic agents do not.
However, a crisp boundary between these concepts has proven elusive. This
problem is pervasive throughout the game theoretic literature on bounded
rationality and particularly critical in parts of the behavioral game theory
literature that make an explicit distinction between the behavior of
"nonstrategic" level-0 agents and "strategic" higher-level agents (e.g., the
level-k and cognitive hierarchy models). Overall, work discussing bounded
rationality rarely gives clear guidance on how the rationality of nonstrategic
agents must be bounded, instead typically just singling out specific decision
rules and informally asserting them to be nonstrategic (e.g., truthfully
revealing private information; randomizing uniformly). In this work, we propose
a new, formal characterization of nonstrategic behavior. Our main contribution
is to show that it satisfies two properties: (1) it is general enough to
capture all purportedly "nonstrategic" decision rules of which we are aware in
the behavioral game theory literature; (2) behavior that obeys our
characterization is distinct from strategic behavior in a precise sense
Electrophysiological Correlates of Visual Object Category Formation in a Prototype-Distortion Task
In perceptual learning studies, participants engage in extensive training in the discrimination of visual stimuli in order to modulate perceptual performance. Much of the literature in perceptual learning has looked at the induction of the reorganization of low-level representations in V1. However, much remains to be understood about the mechanisms behind how the adult brain (an expert in visual object categorization) extracts high-level visual objects from the environment and categorically represents them in the cortical visual hierarchy. Here, I used event-related potentials (ERPs) to investigate the neural mechanisms involved in object representation formation during a hybrid visual search and prototype distortion category learning task. EEG was continuously recorded while participants performed the hybrid task, in which a peripheral array of four dot patterns was briefly flashed on a computer screen. In half of the trials, one of the four dot patterns of the array contained the target, a distorted prototype pattern. The remaining trials contained only randomly generated patterns. After hundreds of trials, participants learned to discriminate the target pattern through corrective feedback. A multilevel modeling approach was used to examine the predictive relationship between behavioral performance over time and two ERP components, the N1 and the N250. The N1 is an early sensory component related to changes in visual attention and discrimination (Hopf et al., 2002; Vogel & Luck, 2000). The N250 is a component related to category learning and expertise (Krigolson et al., 2009; Scott et al., 2008; Tanaka et al., 2006). Results indicated that while N1 amplitudes did not change with improved performance, increasingly negative N250 amplitudes did develop over time and were predictive of improvements in pattern detection accuracy
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