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Introduction: Coordination of multiple degrees of freedom in the performance of dynamic and complex motor tasks presents a challenging neuromuscular control problem. Experiments have inferred that humans exhibit self-organized, preferred coordination patterns, which emerge due to actor and task constraints on performance. The purpose of this study was to determine if the set of effective coordination strategies that exist for a task centers on a small number of robust, invariant patterns of behavior. Methods: Kinetic movement patterns computed from a cohort of 780 primarily female adolescent athletes performing a drop vertical jump (DVJ) task were analyzed to discover distinct groups into which individuals could be classified based on the similarity of movement coordination solutions. Results: Clustering of reduced-dimension joint moment of force time series revealed three very distinct, precisely delineated movement profiles that persisted across trials, and which exhibited different functional performance outcomes, despite no other apparent group differences. The same analysis was also performed on a different task—a single-leg drop landing—which also produced distinct movement profiles; however, the three DVJ profiles did not translate to this task as group assignment was inconsistent between these two tasks. Conclusion: The task demands of the DVJ and single-leg drop—successful landing, reversal of downward momentum, and, in the case of the DVJ, vertical propulsion toward a maximally positioned target—constrain movement performance such that only a few successful outcomes emerge. Discovery of the observed strategies in the context of associated task constraints may help our understanding of how injury risk movement patterns emerge during specific tasks, as well as how the natural dynamics of the system may be exploited to improve these patterns. [ABSTRACT FROM AUTHOR]

Motion-based segmentation is a very important capability for computer vision and video analysis. It depends fundamentally on the system's ability to estimate optic flow using temporally proximate image frames. This is often done using block-matching. However, block-matching is sensitive to the presence of observational noise, which is inevitable in real images. Also, images often include regions of homogeneous intensity, where block-matching is problematic. A better method in this case is to estimate motion at the region level. In the approach described in this paper, we have attempted to address the noise-sensitivity and texture-insufficiently problems using a two-pathway system. The pixel-level pathway is a multilayer pulse-coupled neural network (PCNN)-like locally coupled network used to correct outliers in the block-matching motion estimates and produce improved estimates in regions with sufficient texture. In contrast, the region-level pathway is used to estimate the motion for regions with little intensity variation. In this pathways a PCNN network first partitions intensity images into homogeneous regions, and, a motion vector is then determined for the whole region. The optic flows from both pathways are fused together based on the estimated intensity variation. The fused optic flow is then segmented by a one-layer PCNN network. Results on synthetic and real images are presented to demonstrate that the accuracy of segmentation is improved significantly by. taking advantage of the complementary strengths and weaknesses of the two pathways. [ABSTRACT FROM AUTHOR]

Place cell activity in the hippocampus depends in a complex way on the information from two major sensory sources: External (e.g., visual) cues and internal cues (self-motion, vestibular cues). Dissociation of the two inputs in the presence of the animal modifies the place fields in ways that depend on the magnitude of the difference: If the mismatch is small, place fields typically follow the external cues, but if the mismatch is large, their activity is determined mainly by the internal cues. The dynamics underlying the interaction between the external and internal cues in the hippocampus is not completely known, but is an important issue in understanding the hippocampus’ role in spatial learning and navigation. In this paper, we propose an attractor model of the hippocampus which captures the interaction between external and internal sensory inputs to the hippocampus. The proposed mechanism accounts for the difference seen in the experimental results in the case of a small versus a large discrepancy between the visual and idiothetic information. [Copyright &y& Elsevier]

Cells throughout the rodent hippocampal system show place-specific patterns of firing called place fields, creating a coarse-coded representation of location. The dependencies of this place code - or cognitive map - on sensory cues have been investigated extensively, and several computational models have been developed to explain them. However, place representations also exhibit strong dependence on spatial and behavioral context, and identical sensory environments can produce very different place codes in different situations. Several recent studies have proposed models for the computational basis of this phenomenon, but it is still not completely understood. In this article, we present a very simple connectionist model for producing context-dependent place representations in the hippocampus. We propose that context dependence arises in the dentate gyrus-hilus (DGH) system, which functions as a dynamic selector, disposing a small group of granule and pyramidal cells to fire in response to afferent stimulus while depressing the rest. It is hypothesized that the DGH system dynamics has "latent attractors," which are unmasked by the afferent input and channel system activity into subpopulations of cells in the DG, CA3, and other hippocampal regions as observed experimentally. The proposed model shows that a minimally structured hippocampus-like system can robustly produce context-dependent place codes with realistic attributes. [ABSTRACT FROM AUTHOR]

The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior—potentially with implications for rapid learning, quick memory consolidation, and behavioral planning. Several promising models for robotic navigation and reinforcement learning have been proposed based on these and previous findings. Most of these models, however, use carefully engineered neural networks, and sometimes require long learning periods. In this paper, we present a self-organizing model incorporating place cells and replay, and demonstrate its utility for rapid one-shot learning in non-trivial environments with obstacles. [ABSTRACT FROM AUTHOR]

Describes a variant of covariance learning that works for correlated stimuli in feedforward networks with competitive networks. Intuitive mathematical interpretation of the nonlinear rule; Covariance learning used for associative memory in feedforward and recurrent neural network paradigms.

Determines how chaos and noise combine to generate secure encryption keys. Synchronization of discrete-time chaotic maps by a common noiselike input signal; Noise immunity; Issues in any secure communication system; Enhancement of key security.

Abstract. Based on theoretical issues and neurobiological evidence, considerable interest has recently focused on dynamic computational elements in neural systems. Such elements respond to stimuli by altering their dynamical behavior rather than by changing a scalar output. In particular, neural oscillators capable of chaotic dynamics represent a potentially very rich substrate for complex spatiotemporal information processing. However, the response properties of such systems must be studied in detail before they can be used as computational elements in neural models. In this paper, we focus on the response of a very simple discrete-time neural oscillator model to a fixed input. We show that the oscillator responds to the stimulus through a fairly complex set of bifurcations, and shows critical switching between attractors. This information can be used to construct very sophisticated dynamic computational elements with well-understood response properties. Examples of such elements are presented in the paper. We end with a brief discussion of simple architectures for networks of dynamical elements, and the relevance of our results to neurobiological models. [ABSTRACT FROM AUTHOR]

We present the results of an ongoing collaboration between computer science and psychology researchers that employs Natural Language Processing (NLP) methods to examine the trajectory of semantic space used during group idea generation sessions. Specifically, we track and estimate the region of semantic space being used and the degree to which new ideas expand that space. We present a visualization of this space mapping endeavor and compare human ratings of creativity dimensions (i.e., novelty, task-relevance, and elaboration) to algorithm-based estimations of those same dimensions. The semantic space mapping and algorithm development projects can be used to deliver real-time feedback to human creative groups in order to optimize the collaborative creativity process. The overall goal of this research is to increase the "survival" of novel ideas and their elaboration in the collaborative ideation and subsequent decision processes. [ABSTRACT FROM AUTHOR]

Abstract: Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals’ movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We investigate the synergies generated by this model systematically with respect to various parameters, and compare them to those observed in experiments. [Copyright &y& Elsevier]

Abstract: Understanding cognition has been a central focus for psychologists, neuroscientists and philosophers for thousands of years, but many of its most fundamental processes remain very poorly understood. Chief among these is the process of thought itself: the spontaneous emergence of specific ideas within the stream of consciousness. It is widely accepted that ideas, both familiar and novel, arise from the combination of existing concepts. From this perspective, thought is an emergent attribute of memory, arising from the intrinsic dynamics of the neural substrate in which information is embedded. An important issue in any understanding of this process is the relationship between the emergence of conceptual combinations and the dynamics of the underlying neural networks. Virtually all theories of ideation hypothesize that ideas arise during the thought process through association, each one triggering the next through some type of linkage, e.g., structural analogy, semantic similarity, polysemy, etc. In particular, it has been suggested that the creativity of ideation in individuals reflects the qualitative structure of conceptual associations in their minds. Interestingly, psycholinguistic studies have shown that semantic networks across many languages have a particular type of structure with small-world, scale free connectivity. So far, however, these related insights have not been brought together, in part because there has been no explicitly neural model for the dynamics of spontaneous thought. Recently, we have developed such a model. Though simplistic and abstract, this model attempts to capture the most basic aspects of the process hypothesized by theoretical models within a neurodynamical framework. It represents semantic memory as a recurrent semantic neural network with itinerant dynamics. Conceptual combinations arise through this dynamics as co-active groups of neural units, and either dissolve quickly or persist for a time as emergent metastable attractors and are recognized consciously as ideas. The work presented in this paper describes this model in detail, and uses it to systematically study the relationship between the structure of conceptual associations in the neural substrate and the ideas arising from this system’s dynamics. In particular, we consider how the small-world and scale-free characteristics influence the effectiveness of the thought process under several metrics, and show that networks with both attributes indeed provide significant advantages in generating unique conceptual combinations. [Copyright &y& Elsevier]

Abstract: Idea generation is a fundamental attribute of the human mind, but the cognitive and neural mechanisms underlying this process remain unclear. In this paper, we present a dynamic connectionist model for the generation of ideas within a brainstorming context. The key hypothesis underlying the model is that ideas emerge naturally from itinerant attractor dynamics in a multi-level, modular semantic space, and the potential surface underlying this dynamics is itself shaped dynamically by task context, ongoing evaluative feedback, inhibitory modulation, and short-term synaptic modification. While abstract, the model attempts to capture the interplay between semantic representations, working memory, attentional selection, reinforcement signals, and modulation. We show that, once trained on a set of contexts and ideas, the system can rapidly recall stored ideas in familiar contexts, and can generate novel ideas by efficient, multi-level dynamical search in both familiar and unfamiliar contexts. We also use a simplified continuous-time instantiation of the model to explore the effect of priming on idea generation. In particular, we consider how priming low-accessible categories in a connectionist semantic network can lead to the generation of novel ideas. The mapping of the model onto various regions and modulatory processes in the brain is also discussed briefly. [Copyright &y& Elsevier]

The control of networked multivehicle systems designed to perform complex coordinated tasks is currently an important and challenging field of research. This paper addresses a cooperative search problem where a team of uninhabited aerial vehicles (UAVs) seeks to find targets of interest in an uncertain environment. We present a practical framework for online planning and control of a group of UAVs for cooperative search based on two interdependent tasks: (i) incrementally updating ‘cognitive maps’ used as the representation of the environment through new sensor readings; (ii) continuously planning the path for each vehicle based on the information obtained through the search. We formulate the cooperative search problem and develop a decentralized strategy based on an opportunistic cooperative learning method, where the emergent coordination among vehicles is enabled by letting each vehicle consider other vehicles' actions in its path planning procedure. By using the developed strategy, physically feasible paths for the vehicles to follow are generated, where constraints on aerial vehicles, including physical maneuverabilities, are considered and the dynamic nature of the environment is taken into account. We also present some mathematical analysis of the developed search strategy. Our analysis shows that this strategy guarantees a complete search of the environment and is robust to a partial loss of UAVs. A lower bound on the search time for any strategy and a relaxed upper bound for the proposed strategy are given. Simulation results are used to illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper considers a heterogeneous team of co- operating unmanned aerial vehicles (UAVs) drawn from several distinct classes and engaged in a search and action mission over a spatially extended battlefield with targets of several types. During the mission, the UAVs seek to confirm and verifiably destroy suspected targets and discover, confirm, and verifiably destroy unknown targets. The locations of some (or all) targets are unknown a priori, requiring them to be located using cooperative search. In addition, the tasks to be performed at each target location by the team of cooperative UAVs need to be coordinated. The tasks must, therefore, be allocated to UAVs in real time as they arise, while ensuring that appropriate vehicles are assigned to each task. Each class of UAVs has its own sensing and attack capabilities, so the need for appropriate assignment is paramount. In this paper, an extensive dynamic model that captures the stochastic nature of the cooperative search and task assignment problems is developed, and algorithms for achieving a high level of performance are designed. The paper focuses on investigating the value of predictive task assignment as a function of the number of unknown targets and number of UAVs. In particular, it is shown that there is a tradeoff between search and task response in the context of prediction. Based on the results, a hybrid algorithm for switching the use of prediction is proposed, which balances the search and task response. The performance of the proposed algorithms is evaluated through Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Discusses the major dimensions of the empirical research on place-cell activity and the development of computational models to explain various characteristics of place fields. Background on the discovery of place cells; Information that determines where cells fire; Relationship of the hippocampus to the rest of the brain.

Objective This prospective study compared presurgical language localization with visual naming-associated high-γ modulation ( HGM) and conventional electrical cortical stimulation ( ECS) in children with intracranial electrodes. Methods Patients with drug-resistant epilepsy who were undergoing intracranial monitoring were included if able to name pictures. Electrocorticography ( ECoG) signals were recorded during picture naming (overt and covert) and quiet baseline. For each electrode the likelihood of high-γ (70-116 Hz) power modulation during naming task relative to the baseline was estimated. Electrodes with significant HGM were plotted on a three-dimensional (3D) cortical surface model. Sensitivity, specificity, and accuracy were calculated compared to clinical ECS. Results Seventeen patients with mean age of 11.3 years (range 4-19) were included. In patients with left hemisphere electrodes (n = 10), HGM during overt naming showed high specificity (0.81, 95% confidence interval [ CI] 0.78-0.85), and accuracy (0.71, 95% CI 0.66-0.75, p < 0.001), but modest sensitivity (0.47) when ECS interference with naming (aphasia or paraphasic errors) and/or oral motor function was regarded as the gold standard. Similar results were reproduced by comparing covert naming-associated HGM with ECS naming sites. With right hemisphere electrodes (n = 7), no ECS-naming deficits were seen without interference with oral-motor function. HGM mapping showed a high specificity (0.81, 95% CI 0.78-0.84), and accuracy (0.76, 95% CI 0.71-0.81, p = 0.006), but modest sensitivity (0.44) compared to ECS interference with oral-motor function. Naming-associated ECoG HGM was consistently observed over Broca's area (left posterior inferior-frontal gyrus), bilateral oral/facial motor cortex, and sometimes over the temporal pole. Significance This study supports the use of ECoG HGM mapping in children in whom adverse events preclude ECS, or as a screening method to prioritize electrodes for ECS testing. [ABSTRACT FROM AUTHOR]

Many creative activities take place in a group context, whether in short-term meetings, work teams, or by means of electronic interaction. The group creative process necessarily involves the exchange of ideas or information. Recent models of group creativity have focused on the cognitive underpinnings of this type of group creative process, primarily based on the group brainstorming literature. The authors describe an elaborated computational version of their cognitive model of group creativity and related computational models, and highlight some plausible neural bases for various involved processes. The major findings and theoretical perspectives in this literature are summarized and some potentially fruitful empirical and theoretical directions are highlighted. It is hoped that this comprehensive treatment can be a basis for integrating the present literature and providing useful predictions for further research on this topic. [ABSTRACT FROM PUBLISHER]

The article reports that the neural network research is witnessing a significant shift of emphasis towards temporal coding, which uses time as an essential dimension in neural representations. Temporal coding is passionately debated in neuroscience and related fields, but in the last few years a large volume of physiological and behavioral data has emerged that supports a key role for temporal coding in the brain. In neural networks, extensive research is undertaken under the topics of nonlinear dynamics, oscillatory and chaotic networks, spiking neurons, and pulse-coupled networks. Various information processing tasks have been investigated using temporal coding, including scene analysis, figure-ground separation, classification, associative learning, inference, and motor control.