School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK.
School of Computer and Communication Sciences and Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Centre for Neural Circuits and Behaviour, University of Oxford, Oxford OX1 3SR, UK.
Neuron. 2014 Jun 18;82(6):1394-406. doi: 10.1016/j.neuron.2014.04.045.
Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a class of cortical architectures with strong and random excitatory recurrence that is stabilized by intricate, fine-tuned inhibition, optimized from a control theory perspective. Such networks transiently amplify specific activity states and can be used to reliably execute multidimensional movement patterns. Similar to the experimental observations, these transients must be preceded by a steady-state initialization phase from which the network relaxes back into the background state by way of complex internal dynamics. In our networks, excitation and inhibition are as tightly balanced as recently reported in experiments across several brain areas, suggesting inhibitory control of complex excitatory recurrence as a generic organizational principle in cortex.
运动皮层中的神经元群体在执行肢体运动时会表现出复杂的、大振幅的暂态动力学。具有随机分配突触的传统网络模型无法再现这种行为。在这里，我们引入了一类具有强随机兴奋再现的皮质结构，通过从控制理论的角度进行优化，由复杂的精细抑制来稳定兴奋再现。这样的网络可以暂时放大特定的活动状态，并可用于可靠地执行多维运动模式。与实验观察相似，这些瞬态必须先经过稳态初始化阶段，网络通过复杂的内部动力学从该阶段松弛回背景状态。在我们的网络中，兴奋和抑制像最近在几个脑区的实验中报道的那样紧密平衡，这表明复杂兴奋再现的抑制控制是皮质中的一种通用组织原则。
