Large-scale model of mammalian thalamocortical systems Export

@article{citeulike:2445142, abstract = {10.1073/pnas.0712231105 The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. () It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. () It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on labeling and three-dimensional reconstruction of single neurons of cat visual cortex. () It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales.}, author = {Izhikevich, Eugene M. and Edelman, Gerald M.}, citeulike-article-id = {2445142}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0712231105}, citeulike-linkout-1 = {http://www.pnas.org/content/105/9/3593.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/105/9/3593.full.pdf}, citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/105/9/3593}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/18292226}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=18292226}, day = {4}, doi = {10.1073/pnas.0712231105}, journal = {Proceedings of the National Academy of Sciences}, month = {March}, number = {9}, pages = {3593--3598}, posted-at = {2009-08-19 10:22:05}, priority = {2}, title = {Large-scale model of mammalian thalamocortical systems}, url = {http://dx.doi.org/10.1073/pnas.0712231105}, volume = {105}, year = {2008} }

TY - JOUR ID - citeulike:2445142 L3 - citeulike-article-id:2445142 N2 - 10.1073/pnas.0712231105 The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. () It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. () It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on labeling and three-dimensional reconstruction of single neurons of cat visual cortex. () It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales. IS - 9 JF - Proceedings of the National Academy of Sciences EP - 3598 TI - Large-scale model of mammalian thalamocortical systems VL - 105 SP - 3593 AU - Izhikevich, Eugene AU - Edelman, Gerald PY - 2008/03/04/ UR - http://dx.doi.org/10.1073/pnas.0712231105 ER -