Collective behavior of interacting locally synchronized oscillations in neuronal networks |
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| Authors: | Mahdi Jalili |
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| Institution: | 1. Department of Neuroscience, the Scripps Research Institute, Center for Complex Systems & Brain Sciences, College of Science, Florida Atlantic University, Jupiter, Florida;2. Department of Psychology, Center for Complex Systems & Brain Sciences, College of Science, Florida Atlantic University, Jupiter, Florida;3. Department of Medicine and Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York;1. Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam;2. Sylics (Synaptologics BV), Amsterdam, The Netherlands;3. Department of Developmental Biology, Max-Delbrück-Center for Molecular Medicine, Berlin, Germany;1. Department of Pediatrics, Pritzker School of Medicine, The University of Chicago, Chicago, IL;2. NeuroScience, Inc, Osceola, WI;1. Center of Neuromodulation, Department of Neurosurgery, Wexner Medical Center, Ohio State University, 480 Medical Center Drive, Columbus, OH 43210, USA;2. Department of Neurosurgery, Sheba Medical Center, Tel Hashomer, Israel |
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| Abstract: | Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh–Rose type with electrical and/or chemical couplings. We construct large-scale models using networks of such units which capture the essential features of the dynamics of cells and their connectivity patterns. The profile of the spike synchronization is then investigated considering different model parameters such as strength and ratio of excitatory/inhibitory connections. We also show that transmission time-delay might enhance the spike synchrony. The influence of spike-timing-dependence-plasticity is also studies on the spike synchronization. |
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