Rutishauser Ueli, Slotine Jean-Jacques, Douglas Rodney
Computation and Neural Systems, California Institute of Technology, Pasadena, California, United States of America; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America; Departments of Neurosurgery, Neurology and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, United States of America.
Nonlinear Systems Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Comput Biol. 2015 Jan 24;11(1):e1004039. doi: 10.1371/journal.pcbi.1004039. eCollection 2015 Jan.
Previous explanations of computations performed by recurrent networks have focused on symmetrically connected saturating neurons and their convergence toward attractors. Here we analyze the behavior of asymmetrical connected networks of linear threshold neurons, whose positive response is unbounded. We show that, for a wide range of parameters, this asymmetry brings interesting and computationally useful dynamical properties. When driven by input, the network explores potential solutions through highly unstable 'expansion' dynamics. This expansion is steered and constrained by negative divergence of the dynamics, which ensures that the dimensionality of the solution space continues to reduce until an acceptable solution manifold is reached. Then the system contracts stably on this manifold towards its final solution trajectory. The unstable positive feedback and cross inhibition that underlie expansion and divergence are common motifs in molecular and neuronal networks. Therefore we propose that very simple organizational constraints that combine these motifs can lead to spontaneous computation and so to the spontaneous modification of entropy that is characteristic of living systems.
以往对递归网络执行的计算的解释主要集中在对称连接的饱和神经元及其向吸引子的收敛上。在这里，我们分析了线性阈值神经元的非对称连接网络的行为，其正响应是无界的。我们表明，在广泛的参数范围内，这种不对称性带来了有趣且在计算上有用的动力学特性。当由输入驱动时，网络通过高度不稳定的“扩展”动力学探索潜在解。这种扩展由动力学的负散度引导和约束，这确保了解空间的维度持续降低，直到达到可接受的解流形。然后系统在这个流形上稳定收缩至其最终解轨迹。构成扩展和散度基础的不稳定正反馈和交叉抑制是分子和神经元网络中的常见模式。因此，我们提出，结合这些模式的非常简单的组织约束可以导致自发计算，从而导致熵的自发修改，这是生命系统的特征。
