Hsu David, Tang Aonan, Hsu Murielle, Beggs John M
Department of Neurology, University of Wisconsin, Madison, Wisconsin 53792, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Oct;76(4 Pt 1):041909. doi: 10.1103/PhysRevE.76.041909. Epub 2007 Oct 11.
A spontaneously active neural system that is capable of continual learning should also be capable of homeostasis of both firing rate and connectivity. Experimental evidence suggests that both types of homeostasis exist, and that connectivity is maintained at a state that is optimal for information transmission and storage. This state is referred to as the critical state. We present a simple stochastic computational Hebbian learning model that incorporates both firing rate and critical homeostasis, and we explore its stability and connectivity properties. We also examine the behavior of our model with a simulated seizure and with simulated acute deafferentation. We argue that a neural system that is more highly connected than the critical state (i.e., one that is "supercritical") is epileptogenic. Based on our simulations, we predict that the postseizural and postdeafferentation states should be supercritical and epileptogenic. Furthermore, interventions that boost spontaneous activity should be protective against epileptogenesis.
一个能够持续学习的自发活动神经系统，也应该能够实现放电率和连接性的稳态。实验证据表明这两种稳态均存在，并且连接性维持在对信息传输和存储而言最优的状态。这种状态被称为临界状态。我们提出了一个简单的随机计算赫布学习模型，该模型纳入了放电率和临界稳态，并探讨了其稳定性和连接性属性。我们还通过模拟癫痫发作和模拟急性传入神经阻滞来研究模型的行为。我们认为，一个连接性高于临界状态（即“超临界”）的神经系统具有致痫性。基于我们的模拟，我们预测癫痫发作后和传入神经阻滞后的状态应该是超临界且具有致痫性的。此外，增强自发活动的干预措施应该对癫痫发生具有保护作用。
