Cabessa Jérémie, Villa Alessandro E P
Laboratory of Mathematical Economics (LEMMA), Université Paris 2-Panthéon-Assas, 75005 Paris, France.
Neuroheuristic Research Group, University of Lausanne, CH-1015 Lausanne, Switzerland.
Chaos. 2018 Oct;28(10):106318. doi: 10.1063/1.5042312.
Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.
简要介绍了布尔递归神经网络的研究，重点是由在极限环中观察到的不同吸引子序列所决定的吸引子动力学。我们将这个框架应用于基底神经节 - 丘脑皮质回路的简化模型，其中每个脑区由有向图中的一个“神经元”节点表示。控制参数范围从影响所有细胞的神经元兴奋性到由新的自适应可塑性规则修改的靶向局部连接，以及对影响外部信息流的交互式反馈的调节，使得网络动力学能够在由高度不连续边界界定的稳定域之间切换，并通过特定配置达到非常高的复杂性水平。简要讨论了这种方法在脑回路研究方面的意义。
