Biological neural systems encode and transmit information as patterns of activity tracing complex trajectories in high-dimensional state-spaces, inspiring alternative paradigms of information processing. Heteroclinic networks, naturally emerging in artificial neural systems, are networks of saddles in state-space that provide a transparent approach to generate complex trajectories via controlled switches among interconnected saddles. External signals induce specific switching sequences, thus dynamically encoding inputs as trajectories. Recent works have focused either on computational aspects of heteroclinic networks, i.e. Heteroclinic Computing, or their stochastic properties under noise. Yet, how well such systems may transmit information remains an open question. Here we investigate the information transmission properties of heteroclinic networks, studying them as communication channels. Choosing a tractable but representative system exhibiting a heteroclinic network, we investigate the mutual information rate (MIR) between input signals and the resulting sequences of states as the level of noise varies. Intriguingly, MIR does not decrease monotonically with increasing noise. Intermediate noise levels indeed maximize the information transmission capacity by promoting an increased yet controlled exploration of the underlying network of states. Complementing standard stochastic resonance, these results highlight the constructive effect of stochastic facilitation (i.e. noise-enhanced information transfer) on heteroclinic communication channels and possibly on more general dynamical systems exhibiting complex trajectories in state-space.
翻译:生物神经系统将信息编码和传送,作为在高维状态空间追踪复杂轨迹的活动模式,激励信息处理的替代模式。自然在人工神经系统中出现的外科临床网络,是州空间的马鞍网络,通过相互连接的马鞍,为通过控制开关产生复杂的轨迹提供了透明的方法。外部信号诱发特定的切换序列,从而动态地将输入作为轨迹进行编码。最近的工作要么侧重于高维状态空间的外科临床网络的计算方面,要么在噪音下鼓励异端计算机,或其异端特性。然而,这种系统如何能很好地传递信息仍然是一个未决问题。在这里,我们调查异端网络的信息传输特性,将其作为沟通渠道加以研究。选择一个可移动但具有代表性的系统,展示异端临床网络,我们调查输入信号和由此产生的国家序列之间的相互信息速度(MIR),因为噪音程度不同。令人感知的是,在动态通信中,MIR不会随着噪音的增加而减少单质的噪音。中间噪音网络的传播特性水平确实使信息传递能力最大化。