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Antenna array synthesis for desired radiation characteristics is a challenging field of research in electromagnetics. When an array synthesis problem is visualized as an optimization problem several design parameters involved in synthesis process are considered as degrees of freedom. Every combination of design parameters forms a synthesis technique. In this paper, certain emphasis is given to analyse the techniques of linear array (LA) design using evolutionary computing tools. Novel computing tools like Flower Pollination Algorithm is used for LA synthesis using different degrees of freedom and compared with a conventional Tchebycheff method. Accelerated Particle Swarm Optimization (APSO) is also employed to study the consistency of the technique with the employed computing tool. Radiation patterns are generated with optimized SLL and Tchebycheff beamwidth constraint. [ABSTRACT FROM AUTHOR]

We formulate the problem of oxygen delivery to neural tissue as a problem of association. Input to a pool of neurons in one brain area must be matched in space and time with metabolic inputs from the vascular network via the glial network. We thus have a model in which neural, glial, and vascular layers are connected bidirectionally, in that order. Connections between neuro-glial and glial-vascular stages are trained by an unsupervised learning mechanism such that input to the neural layer is sustained by the precisely patterned delivery of metabolic inputs from the vascular layer via the glial layer. Simulations show that the capacity of such a system to sustain patterns is weak when the glial layer is absent. Capacity is higher when a glial layer is present and increases with the layer size. The proposed formulation of neurovascular interactions raises many intriguing questions about the role of glial cells in cerebral circulation. [ABSTRACT FROM AUTHOR]

We present a computational model of basal ganglia as a key player in exploratory behavior. The model describes exploration of a virtual rat in a simulated water pool experiment. The virtual rat is trained using a reward-based or reinforcement learning paradigm which requires units with stochastic behavior for exploration of the system's state space. We model the Subthalamic Nucleus-Globus Pallidus externa (STN-GPe) segment of the basal ganglia as a pair of neuronal layers with oscillatory dynamics, exhibiting a variety of dynamic regimes such as chaos, traveling waves and clustering. Invoking the property of chaotic systems to explore state-space, we suggest that the complex exploratory dynamics of STN-GPe system in conjunction with dopamine-based reward signaling from the Substantia Nigra pars compacta (SNc) present the two key ingredients of a reinforcement learning system. [ABSTRACT FROM AUTHOR]

Osborne Reynolds's seminal idea of stretching and folding being the basis of fluid mixing has a direct bearing on the interpretation of mixing processes involving dynamical systems tools, in particular, horseshoe maps. Horseshoes offer the only direct, mathematically rigorous, experimental verification of chaos in a flow. In this work these ideas are formalized and developed, with the goal of exploiting the concepts in experimental mixing studies, particularly in the case of alternating doubly symmetric flows. Methods to represent and to identify horseshoes are developed. Application examples to three different flows focusing primarily on errors arising from imperfect placement and reconstruction-are presented. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

We present a neural network model of basal ganglia that departs from the classical Go/NoGo picture of the function of its key pathways-the direct pathway (DP) and the indirect pathway (IP). In classical descriptions of basal ganglia function, the DP is known as the Go pathway since it facilitates movement and the IP is called the NoGo pathway since it inhibits movement. Between these two regimes, in the present model, we posit that there is a third Explore regime, which denotes random exploration of the space of actions. The proposed model is instantiated in a simple action selection task. Striatal dopamine is assumed to switch between DP and IP activation. The IP is modeled as a loop of the subthalamic nucleus (STN) and the globus pallidus externa (GPe), capable of producing chaotic activity. Simulations reveal that, while the system displays Go and NoGo regimes for extreme values of dopamine, at intermediate values of dopamine, it exhibits a new Explore regime denoting a random exploration of the space of action alternatives. The exploratory dynamics originates from the chaotic activity of the STN-GPe loop. When applied to the standard card choice experiment used in the imaging studies of Daw, O'Doherty, Dayan, Seymour, and Dolan (2006), the model favorably describes the exploratory behavior of human subjects., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

We model the role played by the Basal Ganglia (BG) in the generation of voluntary saccadic eye movements. The BG model explicitly represents key nuclei like the striatum (caudate), Substantia Nigra pars reticulata (SNr) and compata (SNc), the Subthalamic Nucleus (STN), the two pallidal nuclei and Superior Colliculus. The model is cast within the Reinforcement Learning (RL) framework, with the dopamine representing the temporal difference error, the striatum serving as the critic, and the indirect pathway playing the role of the explorer. Performance of the model is evaluated on a set of tasks such as feature and conjunction searches, directional selectivity and a successive saccade task. Behavioral phenomena such as independence of search time on number of distractors in feature search and linear increase in search time with number of distractors in conjunction search are observed. It is also seen that saccadic reaction times are longer and search efficiency is impaired on diminished BG contribution, which corroborates with reported data obtained from Parkinson's Disease (PD) patients., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

We present a computational model that highlights the role of basal ganglia (BG) in generating simple reaching movements. The model is cast within the reinforcement learning (RL) framework with correspondence between RL components and neuroanatomy as follows: dopamine signal of substantia nigra pars compacta as the temporal difference error, striatum as the substrate for the critic, and the motor cortex as the actor. A key feature of this neurobiological interpretation is our hypothesis that the indirect pathway is the explorer. Chaotic activity, originating from the indirect pathway part of the model, drives the wandering, exploratory movements of the arm. Thus, the direct pathway subserves exploitation, while the indirect pathway subserves exploration. The motor cortex becomes more and more independent of the corrective influence of BG as training progresses. Reaching trajectories show diminishing variability with training. Reaching movements associated with Parkinson's disease (PD) are simulated by reducing dopamine and degrading the complexity of indirect pathway dynamics by switching it from chaotic to periodic behavior. Under the simulated PD conditions, the arm exhibits PD motor symptoms like tremor, bradykinesia and undershooting. The model echoes the notion that PD is a dynamical disease.

Parkinsonian handwriting is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen velocity. In this paper we present a computational model of handwriting generation that highlights the role of the basal ganglia, particularly the indirect pathway. Whereas reduced dopamine levels resulted in reduced letter size, transition of STN-GPe dynamics from desynchronized (normal) to synchronized (PD) condition resulted in increased fluctuations in velocity in the model. We also present handwriting data from PD patients (n=34) who are at various stages of disease and had taken medication various lengths of time before the handwriting sessions. The patient data are compared with those of age-matched controls. PD handwriting statistically exhibited smaller size and larger velocity fluctuation compared to normal handwriting.

Although the full physiological significance of vasomotion is still debated, it is generally thought to have a role in optimizing tissue oxygenation parameters. We study the effect of vasomotion rhythm in skeletal muscle on oxygen transport using a computational model. The model is used: (i) to test a novel hypothesis that "vasomotors" form a chemical network in which the rhythm adapts to meet oxygen demand in skeletal muscle and (ii) to study the contribution of desynchronized/chaotic vasomotion in optimizing oxygen delivery to skeletal muscle. We formulate a 2D grid model of skeletal muscle consisting of an interleaved arrangement of vessels and muscle fibers fired by a motor neuronal network. The vasomotors too form a network interacting by chemical means. When positive (negative) synapses dominate, the neuronal network exhibits synchronized (desynchronized) activity. Similarly, when positive (negative) chemical interactions dominate, the vessels exhibit synchronized (desynchronized) activity. Optimal oxygenation is observed when both neuronal network and vasomotor network exhibit desynchronous activity. Muscle oxygenation is thought to result by interactions between the fiber/neuron network and the vessel network; optimal oxygenation depends on precise rhythm-related conditions on the two networks. The model provides interesting insights into the phenomenon of muscle fatigue.

Memory in the nervous system is essentially a network effect, resulting from activity-dependent synaptic modification in a network of neurons. Like the nervous system, the heart is a network of cardiac cells electrically coupled by gap junctions. The heart too has memory, termed cardiac memory, whereby the effect of an external electrical activation persists long after the presentation of stimulus is terminated. We have earlier proposed that adaptation of gap junctions, as a function of membrane voltages of the cells that are coupled by the gap junctions, is related to cardiac memory [V.S. Chakravarthy, J. Ghosh, On Hebbian-like adaption in heart muscle: a proposal for "Cardiac Memory", Biol. Cybern. 76 (1997) 207, J. Krishnan, V.S. Chakravarthy, S. Radhakrishnan, On the role of gap junctions on cardiac memory effect, Comput. Cardiol. 32 (2005) 13]. Using the proposed mechanism, we demonstrate memory effect using computational models of interacting cell pairs. In this paper, we address the biological validity of the proposed mechanism of gap junctional adaptation. It is known from electrophysiology of gap junctions that the conductance of these channels adapts as a function of junctional voltage. At a first sight, this form of voltage dependence seems to be at variance with the form required by our mechanism. But we show, with the help of a theoretical model, that the proposed mechanism of voltage-dependent adaptation of gap junctions, is compatible with the known voltage-sensitivity of gap junctions observed in electrophysiological studies. Our analysis suggests a new significance of the voltage-sensitivity of gap junctions and its possible link to the phenomenon of cardiac memory.

We propose a model of a capillary network in which chaotic capillary activity is crucial for efficient oxygenation of a muscle fiber. Tissue oxygenation by microcirculation is controlled by a complex pattern of opening and closing of precapillary sphincters, a phenomenon known as vasomotion. We model the individual precapillary sphincter as a non-linear oscillator that exhibits perfectly periodic vasomotion in isolation. The precapillary sphincters surrounding an active fiber are considered as a network; specific modes of interaction within this network result in complex patterns of vasomotion. In our model, efficient oxygenation of the fiber depends crucially on the mode of interaction among the vasomotions of the individual capillaries. Network interactions that lead to chaotic vasomotion are found to be essential for meeting the tissue oxygen demands precisely. Interactions that cause regular rhythmic patterns of vasomotion fail to meet oxygenation demands accurately.

Vasomotion refers to spontaneous variations in the lumen size of small vessels, with a plausible role in regulation of various aspects of microcirculation. We propose a computational model of vasomotion in skeletal muscle in which the pattern of vasomotion is shown to critically determine the efficiency of oxygenation of a muscle fiber. In this model, precapillary sphincters are modeled as nonlinear oscillators. We hypothesize that these sphincters interact via exchange of vasoactive substances. As a consequence, vasomotion is described as a phenomenon associated with a network of nonlinear oscillators. As a specific instance, we model the vasomotion of precapillary sphincters surrounding an active fiber. The sphincters coordinate their rhythms so as to minimize oxygen deficit in the fiber. Our modeling studies indicate that efficient oxygenation of the fiber depends crucially on the mode of interaction among the vasomotions of individual sphincters. While chaotic forms of vasomotion enhanced oxygenation, regular patterns of vasomotion failed to meet the oxygenation demand accurately.