1,251 resources related to Hippocampus
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2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.
2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)
Neural engineering deals with many aspects of basic and clinical problemsassociated with neural dysfunction including the representation of sensory and motor information, theelectrical stimulation of the neuromuscular system to control the muscle activation and movement, theanalysis and visualization of complex neural systems at multi -scale from the single -cell and to the systemlevels to understand the underlying mechanisms, the development of novel neural prostheses, implantsand wearable devices to restore and enhance the impaired sensory and motor systems and functions.
BMEI is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and informatics. Specific topics include Biomedical imaging and visualization; Biomedical signal processing and analysis; etc.
Bioinformatics, Computational Biology, Biomedical Engineering
This conference provides a high profile forum for disseminating the latest research on electrical and computer engineering with application in information technology. It brings together academic researchers, industrial scientists, and IT professionals from electrical engineering, computer engineering, computer science, and informatics to share late-breaking advances of these interdisciplinary fields.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Takuto Hayashi; Yuko Mizuno-Matsumoto; Shimpei Kohri; Yoshinori Nitta; Mitsuo Tonoike 2014 World Automation Congress (WAC), 2014
The present study investigates phase-locked and non-phase-locked brain responses in theta and alpha frequency bands. A 122-ch whole-head magnetoencephalogram (MEG) was recorded continuously during emotional audio- visual stimuli. The results showed that emotional stimuli induced increased theta band phase-locked activity at 200-400 ms and non-phase-locked activity at 100-600 ms. These activities were stronger with pleasant stimuli over all areas compared ...
Theodoros P. Zanos; Robert E. Hampson; Sam A. Deadwyler; Theodore W. Berger; Vasilis Z. Marmarelis 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
Implementation of neuroprosthetic devices requires a reliable and accurate quantitative representation of the input-output transformations performed by the involved neuronal populations. Nonparametric, data driven models with predictive capabilities are excellent candidates for these purposes. When modeling input-output relations in multi-input neuronal systems, it is important to select the subset of inputs that are functionally and causally related to the output. ...
K. Tateno; T. Hashimoto; S. Ishizuka; K. Nakashima; H. Hayashi 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004
The dentate gyrus selectively transmits the theta rhythm to the hippocampal CA3 region. The random synaptic input to mossy cells determines frequency responses of the dentate gyrus network model. We show preliminary experimental results of this filtering property in vitro, and propose a neural network model which may account for the theta rhythm selection.
Péter Érdi; Mihály Bányai; Balázs Ujfalussy; Vaibhav Diwadkar The 2011 International Joint Conference on Neural Networks, 2011
Schizophrenia is often regarded as a set of symptoms caused by impairments in the cognitive control in macro-networks of the brain. To investigate this hypothesis, an fMRI study involving an associative learning task was conducted with schizophrenia patients and controls. A set of generative models of the BOLD signal generation were defined to describe the interaction of five brain regions ...
Elisa E. Konofagou; James Choi; Ann Lee; Babak Baseri 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009
Current treatments of neurological and neurodegenerative diseases are limited due to the lack of a truly noninvasive, transient, and regionally selective brain drug delivery method. The brain is particularly difficult to deliver drugs to because of the blood-brain barrier (BBB). Over the past few years, we have been developing methods that combine Focused Ultrasound (FUS) and microbubbles in order to ...
Neural Networks, 2006. IJCNN '06. International Joint Conference on, 2006
On this study we developed a mathematical model that would be able to simulate the dynamics of the hippocampal complex and, also, sustaining our hypothesis regarding the generation of the called "Place Fields". It has been described that "Place Fields" would be dependent of egocentric and allocentric coordinates and signals. The electrophysiological parameter is the generation, on the hippocampus, of ...
Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on, 2000
We propose a behavior learning method of a mobile robot using visual information. The proposed system consists of two networks for the state expression and the behavior generation. The state expression network, which is based on self-organizing algorithm, categorizes the robot's states. Also, the behavior generation network acquires the robot's behaviors using the output of the state expression network. Here, ...
Computer-Based Medical Systems (CBMS), 2011 24th International Symposium on, 2011
This work presents a new segmentation model called Similarity Cloud Model (SCM) based on hippocampus feature extraction. The segmentation process is divided in two main operations: localization by similarity and cloud adjustment. The first process uses the cloud to localize the most probable position of the hippocampus in a target volume. Segmentation is completed by a reformulation of the cloud ...
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE, 2006
We present a new method to characterize the nonlinearities resulting from the co-activity of two pathways that converge on a common postsynaptic element. We investigated the nonlinear dynamic interactions between the lateral perforant pathway (LPP) and the medial perforant pathway (MPP) of the hippocampal dentate gyms, and the effects of these cross-pathway interactions on granule cell output. A third order ...
Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on, 2015
As a crucial mental disorder, schizophrenia affects one percent of the world population. Diagnosis of this disorder is now based mainly on the clinical symptoms. As the first study in Iran, we investigate the magnetic resonance imaging (MRI) images of patients with schizophrenia to investigate the imaging biomarkers for helping the diagnosis of this disorder. In this study, we have ...
Deliberation entails the sequential, serial search through possible options. This means that deliberation requires a mechanism to represent the structure of the world, from which predictions can be generated concerning these options and the expectations of the consequences of taking those options. Deliberation requires a mechanism to move mentally through those predictions as well as a mechanism to evaluate and compare those predictions. Neural signals for each of these factors have been found in the rat.
This chapter contains sections titled: 10.1 A Good Hebb start, 10.2 Supervised Learning in Artificial Neural Networks, 10.3 Hebbian Learning Models, 10.4 Unsupervised ANNs for AI, 10.5 Spikes and Plasticity, 10.6 Place Cells and Prediction in the Hippocampus, 10.7 Neural Support for Cognitive Incrementalism, 10.8 Hebb Still Rules
In this paper, we describe how a mobile robot only controlled by visual information can retrieve particular goal locations in an open environment according to the level of associated motivations and to its current location. The interest of our control architecture relies in its simplicity and its robustness. Our robot does not need a precise map nor to learn all the possible positions in the environment to be able to navigate correctly. The neural control architecture used is inspired from neurobiological studies about the brain visual processes and the integration of those information for navigation behaviors. We emphasize the interest to go back and forth between robotics and biology. At last we show neurobiological modelization of the hippocampus has helped us to imagine a solution for scaling our architecture to more complex problems.
A model of the hippocampus as a "cognitive graph" is proposed. It essentially considers the hippocampus as an heteroassociative network that learns temporal sequences of visited places and stores a topological representation of the environment. Using place cells, head-direction cells, and "goal cells", we propose a biologically plausible way of exploiting such a spatial representation for navigation, which does not require complicated graph search algorithms. Simulations show that the resulting animat is able to navigate in continuous environments that contain obstacles. Furthermore, we make experimental predictions on simultaneous recordings of multiple cells in the rat happocampus.
This paper describes the current state of advancement of the Psikharpax project, which aims at producing an artificial rat equipped with control architectures and mechanisms that reproduce as nearly as possible those that have been widely studied in the natural rat. The article first describes the navigation system of Psikharpax, which is inspired from the anatomy and physiology of dedicated structures in the rat's brain, like the hippocampus and the postsubiculum. Then, it defines the animat's action-selection system, which aims at replicating other structures, the basal ganglia. It also explains how navigation and actionselection capacities have been combined thanks to the interconnection of two different ioops in the basal ganglia: a ventral loop that selects the direction of motion, and a dorsal loop that selects other behaviors, like feeding or drinking. Finally, preliminary results on the implementation of learning mechanisms in these structures are also presented.
The process of cognitive search invokes a purposeful and iterative process by which an organism considers information of a potentially diverse nature and selects a particular option that best matches the appropriate criteria. This chapter focuses on the neurobiological basis of such a goal-directed search by parsing the process into its main components, suggested here as initiation, identification of search space, deliberation, action selection, and evaluation and search termination. Unexpected uncertainty is suggested as a key trigger for the onset of the search process. Current data posit that this is represented in the anterior cingulate, parietal, and inferior frontal cortices, suggesting these areas could be particularly important in search initiation. A change in motivational state, likely signaled by a wide range of brain regions including the amygdala, can also play a role at this stage. The neural structures which represent the set of to-be-searched options may vary depending on the search domain (e.g., spatial, visual, linguistic). During deliberation, predictions regarding the consequences of selecting these options are generated and compared, implicating areas of frontal cortex as well as the hippocampus and striatum, which are known to play a role in different aspects of outcome evaluation. Action planning and selection likely involve an interplay between the prefrontal cortex and basal ganglia, whereas search termination could involve the specific neural networks implicated in response inhibition. The influence exerted over the search process by the major ascending neuromodulators (dopamine, norepinephrine/noradrenaline, serotonin, and acetylcholine) is also considered, and a particularly critical role suggested for dopamine and noradrenaline, given their ability to influence cognitive flexibility and arousal. Finally, pathologies of search processes are discussed, both with respect to brain damage and psychiatric illness.
Many neural areas, notably, the hippocampus, show structured, dynamical, population behavior such as coordinated oscillations. It has long been observed that such oscillations provide a substrate for representing analog information in the firing phases of neurons relative to the underlying population rhythm. However, it has become increasingly clear that it is essential for neural populations to represent uncertainty about the information they capture, and the substantial recent work on neural codes for uncertainty has omitted any analysis of oscillatory systems. Here, we observe that, since neurons in an oscillatory network need not only fire once in each cycle (or even at all), uncertainty about the analog quantities each neuron represents by its firing phase might naturally be reported through the degree of concentration of the spikes that it fires. We apply this theory to memory in a model of oscillatory associative recall in hippocampal area CA3. Although it is not well treated in the literature, representing and manipulating uncertainty is fundamental to competent memory; our theory enables us to view CA3 as an effective uncertainty-aware, retrieval system.
This chapter contains sections titled: Introduction The System: Hippocampus General Strategy and System Requirements Proof of Concept: Replacement of CA3 Region of Hippocampal Slice with Biomimetic Device Experimental Characterization of Nonlinear Properties of the Hippocampal Trisynaptic Pathway Nonlinear Dynamic Modeling of CA3 Input/Output Properties Microcircuitry Implementation of CA3 Input/Output Model Conformal, Multisite Electrode Array Interface System Integration: Restoration of Hippocampal Trisynaptic Circuit Dynamics with CA3 Prosthesis Acknowledgments References
This chapter contains sections titled: 2.1 Spinal Cord and Brainstem, 2.2 The Forebrain: An Overview, 2.3 Cortex: Long-Term Memory, 2.4 Basal Ganglia: The Program Sequencer, 2.5 Thalamus: Input and Output, 2.6 Hippocampus: Program Modifications, 2.7 Amygdala: Rating What' s Important, 2.8 How the Brain Programs Itself, 2.9 Summary
Some recent work with autonomous robots has focused on using optical flow for "direct" control of speed and rotation in obstacle avoidance and other simple behaviors. This work has been inspired by work with insects showing similar mechanisms. To extend these behaviors, three methods of maze navigation are investigated in a simulated robot modeled after a real one. A motor-based method places biases iii the obstacle avoidance control law used previously. A perception-based method uses optical flow to detect possibilities for action (e.g., to turn left or right). Both of these require that the agent have a list of biases in order to navigate. The third method, called the Salience Centroid Method, is based on a theory of the role of the hippocampus in rat navigation. This method trades off the memory of the first two for more advanced perceptual processing and allows the most flexible behavior.
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