Popiel Nicholas J M, Khajehabdollahi Sina, Abeyasinghe Pubuditha M, Riganello Francesco, Nichols Emily S, Owen Adrian M, Soddu Andrea
Department of Physics and Astronomy, Western University, 151 Richmond St, London, ON N6A 3K7, Canada.
Faculty of Medicine Nursing and Health Sciences, Monash University, Wellington Rd, Clayton VIC 3800, Australia.
Entropy (Basel). 2020 Mar 16;22(3):339. doi: 10.3390/e22030339.
Integrated Information Theory (IIT) posits that integrated information ( Φ ) represents the quantity of a conscious experience. Here, the generalized Ising model was used to calculate Φ as a function of temperature in toy models of fully connected neural networks. A Monte-Carlo simulation was run on 159 normalized, random, positively weighted networks analogous to small five-node excitatory neural network motifs. Integrated information generated by this sample of small Ising models was measured across model parameter spaces. It was observed that integrated information, as an order parameter, underwent a phase transition at the critical point in the model. This critical point was demarcated by the peak of the generalized susceptibility (or variance in configuration due to temperature) of integrated information. At this critical point, integrated information was maximally receptive and responsive to perturbations of its own states. The results of this study provide evidence that Φ can capture integrated information in an empirical dataset, and display critical behavior acting as an order parameter from the generalized Ising model.
整合信息理论（IIT）假定整合信息（Φ）代表有意识体验的量值。在此，广义伊辛模型被用于在全连接神经网络的玩具模型中计算作为温度函数的Φ。对159个归一化、随机、正加权的网络进行了蒙特卡罗模拟，这些网络类似于小型五节点兴奋性神经网络基序。通过该小型伊辛模型样本生成的整合信息在模型参数空间中进行了测量。据观察，作为一个序参量的整合信息在模型的临界点经历了相变。这个临界点由整合信息的广义磁化率峰值（或由于温度导致的构型方差）划定。在这个临界点，整合信息对其自身状态的扰动具有最大的接受性和响应性。本研究结果提供了证据，表明Φ可以在经验数据集中捕捉整合信息，并展现出作为广义伊辛模型序参量的临界行为。
