Rapid learning of spatial representations for goal-directed navigation based on a novel model of hippocampal place fields.

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.

Distinct Coordination Strategies Associated with the Drop Vertical Jump Task.

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.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task.

Humans commonly engage in tasks that require or are made more efficient by coordinating with other humans. In this paper we introduce a task dynamics approach for modeling multi-agent interaction and decision making in a pick and place task where an agent must move an object from one location to another and decide whether to act alone or with a partner. Our aims were to identify and model (1) the affordance related dynamics that define an actor's choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actor's hand movements when moving to grasp, relocate, or pass the object. Using a virtual reality pick and place task, we demonstrate that both the decision to pass or not pass an object and the movement trajectories of the participants can be characterized in terms of a behavioral dynamics model. Simulations suggest that the proposed behavioral dynamics model exhibits features observed in human participants including hysteresis in decision making, non-straight line trajectories, and non-constant velocity profiles. The proposed model highlights how the same low-dimensional behavioral dynamics can operate to constrain multiple (and often nested) levels of human activity and suggests that knowledge of what, when, where and how to move or act during pick and place behavior may be defined by these low dimensional task dynamics and, thus, can emerge spontaneously and in real-time with little a priori planning.

Memristive device based learning for navigation in robots.

Biomimetic robots have gained attention recently for various applications ranging from resource hunting to search and rescue operations during disasters. Biological species are known to intuitively learn from the environment, gather and process data, and make appropriate decisions. Such sophisticated computing capabilities in robots are difficult to achieve, especially if done in real-time with ultra-low energy consumption. Here, we present a novel memristive device based learning architecture for robots. Two terminal memristive devices with resistive switching of oxide layer are modeled in a crossbar array to develop a neuromorphic platform that can impart active real-time learning capabilities in a robot. This approach is validated by navigating a robot vehicle in an unknown environment with randomly placed obstacles. Further, the proposed scheme is compared with reinforcement learning based algorithms using local and global knowledge of the environment. The simulation as well as experimental results corroborate the validity and potential of the proposed learning scheme for robots. The results also show that our learning scheme approaches an optimal solution for some environment layouts in robot navigation.

Presurgical language localization with visual naming associated ECoG high- gamma modulation in pediatric drug-resistant epilepsy.

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.

Connectivity and thought: the influence of semantic network structure in a neurodynamical model of thinking.

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.

A modular neural model of motor synergies.

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.

Investigating the predictability of essential genes across distantly related organisms using an integrative approach.

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.

Neural dynamics of idea generation and the effects of priming.

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.

Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams.

This paper considers a heterogeneous team of cooperating 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.

Phase transition in a swarm algorithm for self-organized construction.

This paper reports on a system where very simple, noncommunicating mobile agents in a cellular (lattice) environment use purely local rules to construct connected structures from initially randomly distributed building blocks. We study the effect of block density on the final structure, demonstrating a percolationlike phase transition: Low block densities lead to the formation of small, disconnected structures but a single connected structure emerges abruptly beyond a critical density. The empirical study of the structure at the transition point shows scaling behavior, providing strong evidence for criticality. We also demonstrate that a simple change of rules can completely change the phase-transition effect. The results have implications for the self-organized construction of complex structures by swarms.

Network capacity analysis for latent attractor computation.

Attractor networks have been one of the most successful paradigms in neural computation, and have been used as models of computation in the nervous system. Recently, we proposed a paradigm called 'latent attractors' where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus--a brain region of fundamental significance for memory and spatial learning. Latent attractor networks are a special case of associative memory networks. The model studied here consists of a two-layer recurrent network with attractors stored in the recurrent connections using a clipped Hebbian learning rule. The firing in both layers is competitive--K winners take all firing. The number of neurons allowed to fire, K, is smaller than the size of the active set of the stored attractors. The performance of latent attractor networks depends on the number of such attractors that a network can sustain. In this paper, we use signal-to-noise methods developed for standard associative memory networks to do a theoretical and computational analysis of the capacity and dynamics of latent attractor networks. This is an important first step in making latent attractors a viable tool in the repertoire of neural computation. The method developed here leads to numerical estimates of capacity limits and dynamics of latent attractor networks. The technique represents a general approach to analyse standard associative memory networks with competitive firing. The theoretical analysis is based on estimates of the dendritic sum distributions using Gaussian approximation. Because of the competitive firing property, the capacity results are estimated only numerically by iteratively computing the probability of erroneous firings. The analysis contains two cases: the simple case analysis which accounts for the correlations between weights due to shared patterns and the detailed case analysis which includes also the temporal correlations between the network's present and previous state. The latter case predicts better the dynamics of the network state for non-zero initial spurious firing. The theoretical analysis also shows the influence of the main parameters of the model on the storage capacity.

Communicating with noise: How chaos and noise combine to generate secure encryption keys.

An approach for the secure transmission of encrypted messages using chaos and noise is presented in this paper. The method is based on the synchronization of certain types of chaotic oscillators in response to a common noise input. This allows two distant oscillators to generate identical output which can be used as a key for encryption and decryption of a message signal. The noiselike synchronizing input-which contains no message information-is communicated to identical oscillators in the transmitter and the receiver over a public channel. The encrypted message is also sent over a public channel, while the key is never transmitted at all. The chaotic nature of the oscillators which generate the key and the randomness of the signal driving the process combine to make the recovery of the key by an eavesdropper extremely difficult. We evaluate system performance with respect to security and robustness and show that a robust and secure system can be obtained. (c) 1998 American Institute of Physics.