The Origin of Microscopic Noise in Biological Brains and its Role in Stabilizing Mesoscopic Chaotic Dynamics

摘要

Recent developments in chaotic dynamics have been dominated by deterministic models that are low-dimensional, autonomous, stationary, and noise-free. Chaos in brains emerges from synaptic interactions of immense numbers of microscopic neurons that stimulate and yet constrain each other, creating mesoscopic order from microscopic disorder. Brain subsystems are nonstationary, unstable, constantly bombarded by sensory input, changing in functional dimension on interactions with other brain parts, and sustained by noise, yet they display a high degree of reliability and metastability in the proper conditions. Attempts to simulate brain dynamics with digital models encounter the limits expressed in numerical instabilities imposed by finite approximations, attractor crowding, collapse into quasiperiodic solutions, the lack of shadowing trajectories, and the curse of "infinite sensitivity to the initial conditions." Analog embodiments may take advantage of the use of continuous variables, highly parallel integrative operations, and reliance on internally generated noise that is not only unavoidable but essential for normal function.