480 research outputs found

    Using an insect mushroom body circuit to encode route memory in complex natural environments

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    Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images

    Automated Track Change Detection Technology for Enhanced Railroad Safety Assessment

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    693JJ619C000004The Rail Transportation and Engineering Center at the University of Illinois at Urbana-Champaign and Railmetrics, Inc. evaluated the use of 3D laser scanning, Deep Convolutional Neural Networks (DCNNs), and change detection technology for railway track safety inspections. Researchers evaluated the potential use of these combined technologies to provide value-added inspection data to traditional track inspection methods. The project was conducted between April 2019 and October 2020. Field testing was completed on the High Tonnage Loop at the Transportation Technology Center in Pueblo, Colorado

    Synaptically activated burst-generating conductances may underlie a group-pacemaker mechanism for respiratory rhythm generation in mammals

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    Breathing, chewing, and walking are critical life-sustaining behaviors in mammals that consist essentially of simple rhythmic movements. Breathing movements in particular involve the diaphragm, thorax, and airways but emanate from a network in the lower brain stem. This network can be studied in reduced preparations in vitro and using simplified mathematical models that make testable predictions. An iterative approach that employs both in vitro and in silico models argues against canonical mechanisms for respiratory rhythm in neonatal rodents that involve reciprocal inhibition and pacemaker properties. We present an alternative model in which emergent network properties play a rhythmogenic role. Specifically, we show evidence that synaptically activated burst-generating conductances-which are only available in the context of network activity-engender robust periodic bursts in respiratory neurons. Because the cellular burst-generating mechanism is linked to network synaptic drive we dub this type of system a group pacemaker. © 2010 Elsevier B.V

    Towards Sensorimotor Coupling of a Spiking Neural Network and Deep Reinforcement Learning for Robotics Application

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    Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the great achievements of deep reinforcement learning in various domains including finance,medicine, healthcare, video games, robotics and computer vision.Deep neural network was started with multi-layer perceptron (1stgeneration) and developed to deep neural networks (2ndgeneration)and it is moving forward to spiking neural networks which are knownas3rdgeneration of neural networks. Spiking neural networks aim to bridge the gap between neuroscience and machine learning, using biologically-realistic models of neurons to carry out computation. In this thesis, we first provide a comprehensive review on both spiking neural networks and deep reinforcement learning with emphasis on robotic applications. Then we will demonstrate how to develop a robotics application for context-aware scene understanding to perform sensorimotor coupling. Our system contains two modules corresponding to scene understanding and robotic navigation. The first module is implemented as a spiking neural network to carry out semantic segmentation to understand the scene in front of the robot. The second module provides a high-level navigation command to robot, which is considered as an agent and implemented by online reinforcement learning. The module was implemented with biologically plausible local learning rule that allows the agent to adopt quickly to the environment. To benchmark our system, we have tested the first module on Oxford-IIIT Pet dataset and the second module on the custom-made Gym environment. Our experimental results have proven that our system is able present the competitive results with deep neural network in segmentation task and adopts quickly to the environment

    REPRESENTATION LEARNING FOR ACTION RECOGNITION

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    The objective of this research work is to develop discriminative representations for human actions. The motivation stems from the fact that there are many issues encountered while capturing actions in videos like intra-action variations (due to actors, viewpoints, and duration), inter-action similarity, background motion, and occlusion of actors. Hence, obtaining a representation which can address all the variations in the same action while maintaining discrimination with other actions is a challenging task. In literature, actions have been represented either using either low-level or high-level features. Low-level features describe the motion and appearance in small spatio-temporal volumes extracted from a video. Due to the limited space-time volume used for extracting low-level features, they are not able to account for viewpoint and actor variations or variable length actions. On the other hand, high-level features handle variations in actors, viewpoints, and duration but the resulting representation is often high-dimensional which introduces the curse of dimensionality. In this thesis, we propose new representations for describing actions by combining the advantages of both low-level and high-level features. Specifically, we investigate various linear and non-linear decomposition techniques to extract meaningful attributes in both high-level and low-level features. In the first approach, the sparsity of high-level feature descriptors is leveraged to build action-specific dictionaries. Each dictionary retains only the discriminative information for a particular action and hence reduces inter-action similarity. Then, a sparsity-based classification method is proposed to classify the low-rank representation of clips obtained using these dictionaries. We show that this representation based on dictionary learning improves the classification performance across actions. Also, a few of the actions consist of rapid body deformations that hinder the extraction of local features from body movements. Hence, we propose to use a dictionary which is trained on convolutional neural network (CNN) features of the human body in various poses to reliably identify actors from the background. Particularly, we demonstrate the efficacy of sparse representation in the identification of the human body under rapid and substantial deformation. In the first two approaches, sparsity-based representation is developed to improve discriminability using class-specific dictionaries that utilize action labels. However, developing an unsupervised representation of actions is more beneficial as it can be used to both recognize similar actions and localize actions. We propose to exploit inter-action similarity to train a universal attribute model (UAM) in order to learn action attributes (common and distinct) implicitly across all the actions. Using maximum aposteriori (MAP) adaptation, a high-dimensional super action-vector (SAV) for each clip is extracted. As this SAV contains redundant attributes of all other actions, we use factor analysis to extract a novel lowvi dimensional action-vector representation for each clip. Action-vectors are shown to suppress background motion and highlight actions of interest in both trimmed and untrimmed clips that contributes to action recognition without the help of any classifiers. It is observed during our experiments that action-vector cannot effectively discriminate between actions which are visually similar to each other. Hence, we subject action-vectors to supervised linear embedding using linear discriminant analysis (LDA) and probabilistic LDA (PLDA) to enforce discrimination. Particularly, we show that leveraging complimentary information across action-vectors using different local features followed by discriminative embedding provides the best classification performance. Further, we explore non-linear embedding of action-vectors using Siamese networks especially for fine-grained action recognition. A visualization of the hidden layer output in Siamese networks shows its ability to effectively separate visually similar actions. This leads to better classification performance than linear embedding on fine-grained action recognition. All of the above approaches are presented on large unconstrained datasets with hundreds of examples per action. However, actions in surveillance videos like snatch thefts are difficult to model because of the diverse variety of scenarios in which they occur and very few labeled examples. Hence, we propose to utilize the universal attribute model (UAM) trained on large action datasets to represent such actions. Specifically, we show that there are similarities between certain actions in the large datasets with snatch thefts which help in extracting a representation for snatch thefts using the attributes from the UAM. This representation is shown to be effective in distinguishing snatch thefts from regular actions with high accuracy.In summary, this thesis proposes both supervised and unsupervised approaches for representing actions which provide better discrimination than existing representations. The first approach presents a dictionary learning based sparse representation for effective discrimination of actions. Also, we propose a sparse representation for the human body based on dictionaries in order to recognize actions with rapid body deformations. In the next approach, a low-dimensional representation called action-vector for unsupervised action recognition is presented. Further, linear and non-linear embedding of action-vectors is proposed for addressing inter-action similarity and fine-grained action recognition, respectively. Finally, we propose a representation for locating snatch thefts among thousands of regular interactions in surveillance videos

    A sensory system for robots using evolutionary artificial neural networks.

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    The thesis presents the research involved with developing an Intelligent Vision System for an animat that can analyse a visual scene in uncontrolled environments. Inspiration was drawn both from Biological Visual Systems and Artificial Image Recognition Systems. Several Biological Systems including the Insect, Toad and Human Visual Systems were studied alongside popular Pattern Recognition Systems such as fully connected Feedforward Networks, Modular Neural Networks and the Neocognitron. The developed system, called the Distributed Neural Network (DNN) was based on the sensory-motor connections in the common toad, Bufo Bufo. The sparsely connected network architecture has features of modularity enhanced by the presence of lateral inhibitory connections. It was implemented using Evolutionary Artificial Neural Networks (EANN). A novel method called FUSION was used to train the DNN, which is an amalgamation of several concepts of learning in Artificial Neural Networks such as Unsupervised Learning, Supervised Learning, Reinforcement Learning, Competitive Learning, Self-organisation and Fuzzy Logic. The DNN has unique feature detecting capabilities. When the DNN was tested using images that comprised of combination of features used in the training set, the DNN was successful in recognising individual features. The combinations of features were never used in the training set. This is a unique feature of the DNN trained using Fusion that cannot be matched by any other popular ANN architecture or training method. The system proved to be robust in dealing with New and Noisy Images. The unique features of the DNN make the network suitable for applications in robotics such as obstacle avoidance and terrain recognition, where the environment is unpredictable. The network can also be used in the field of Medical Imaging, Biometrics (Face and Finger Print Recognition) and Quality Inspection in the Food Processing Industry and applications in other uncontrolled environments

    The role of the dopamine D4 receptor in modulating state-dependent gamma oscillations

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    Rhythmic oscillations in neuronal activity display variations in amplitude (power) over a range of frequencies. Attention and cognitive performance correlate with increases in cortical gamma oscillations (40-70Hz) that are generated by the coordinated firing of glutamatergic pyramidal neurons and GABAergic interneurons, and are modulated by dopamine. In the medial prefrontal cortex (mPFC) of rats, gamma power increases during treadmill walking, or after administration of an acute subanesthetic dose of the NMDA receptor antagonist ketamine. Ketamine is also used to mimic symptoms of schizophrenia, including cognitive deficits, in healthy humans and rodents. Additionally, the ability of a drug to modify ketamine-induced gamma power has been proposed to predict its pro-cognitive therapeutic efficacy. However, the mechanism underlying ketamine-induced gamma oscillations is poorly understood. We hypothesized that gamma oscillations induced by walking and ketamine would be generated by a shared mechanism in the mPFC and one of its major sources of innervation, the mediodorsal thalamus (MD). Recordings from chronically implanted electrodes in rats showed that both treadmill walking and ketamine increased gamma power, firing rates, and spike-gamma LFP correlations in the mPFC. By contrast, in the MD, treadmill walking increased all three measures, but ketamine decreased firing rates and spike-gamma LFP correlations while increasing gamma power. Therefore, walking- and ketamine-induced gamma oscillations may arise from a shared circuit in the mPFC, but different circuits in the MD. Recent work in normal animals suggests that dopamine D4 receptors (D4Rs) synergize with the neuregulin/ErbB4 signaling pathway to modulate gamma oscillations and cognitive performance. Consequently, we hypothesized that drugs targeting the D4Rs and ErbB receptors would show pro-cognitive potential by reducing ketamine-induced gamma oscillations in mPFC. However, when injected before ketamine, neither the D4R agonist nor antagonist altered ketamine’s effects on gamma power or firing rates in the mPFC, but the pan-ErbB antagonist potentiated ketamine’s increase in gamma power, and prevented ketamine from increasing firing rates. This indicates that D4Rs and ErbB receptors influence gamma power via distinct mechanisms that interact with NMDA receptor antagonism differently. Our results highlight the value of using ketamine-induced changes in gamma power as a means of testing novel pharmaceutical agents
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