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诺贝尔奖获得者埃德尔曼的最新研究:脑的多尺度模拟

诺贝尔奖获得者埃德尔曼的最新研究:脑的多尺度模拟

(2009-06-21 13:20:37)
 

Gerald Edelman的工作将神经网络尽量的细节尽量模拟得清楚(例如离子的流动),因此可以通过方程得到模拟的电活动。在Defense Advanced Research Projects Agency(DARPA)上举行的足球赛(Segway Soccer)中,他的研究所生产出的机器人完败了(一共5局5胜)卡内基米隆大学基于人工智能的机器人。这类机器人被他称为人工意识体,学名叫Brain Based Device。能回忆过去预测未来,未来或许能掌握言语能力。


在PNAS上的文章解读了这样设计的系统具有的一些有趣特性:

PNAS (2008) 105:3593-3598
Abstract

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. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) 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 vitro 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.

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