Grossberg Stephen, Versace Massimiliano
Department of Cognitive and Neural Systems, Center for Adaptive Systems, Center of Excellence for Learning in Education, Science, and Technology, Boston University, 677 Beacon Street, Boston, MA 02215, USA.
Brain Res. 2008 Jul 7;1218:278-312. doi: 10.1016/j.brainres.2008.04.024. Epub 2008 Apr 22.
This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single-cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current source densities and local field potentials; and single-cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, learning, and consciousness. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.
本文开发了同步匹配自适应共振理论(SMART)神经模型,以解释大脑如何协调丘脑皮质和皮质皮质处理的多个层次,从而快速学习并稳定记住有关不断变化的世界的重要信息。该模型阐明了自下而上和自上而下的过程如何协同工作以实现这一目标,特别是学习、期望、注意力、共振和同步过程是如何协调的。该模型首次阐明了以下各级大脑组织如何共存,以实现调节大脑表征的快速学习和稳定记忆的认知处理特性:单细胞特性,如放电动力学、放电时间依赖可塑性(STDP)和乙酰胆碱调节;详细的层状丘脑和皮质电路设计及其相互作用;聚合细胞记录,如电流源密度和局部场电位;以及γ和β频域中的单细胞和大规模区域间振荡。特别是,该模型预测了多个皮质区域的层状电路如何与初级和高阶特定丘脑核以及非特定丘脑核相互作用,以进行注意力视觉学习和信息处理。该模型模拟了神经元放电在脑区内和脑区之间如何同步,并触发STDP。自下而上的自适应滤波输入模式与学习到的自上而下的期望之间的匹配会导致γ振荡,从而支持注意力、共振、学习和意识。不匹配会抑制学习,同时在深层皮质层启动的重置和假设检验操作期间引起β振荡。学习到识别码的通用性由乙酰胆碱介导的警觉过程控制。