Ivan Shpurov and Tom Froese

Entropy 2022, 24(12), 1840

Social insects such as honey bees exhibit complex behavioral patterns, and their distributed behavioral coordination enables decision-making at the colony level. It has, therefore, been proposed that a high-level description of their collective behavior might share commonalities with the dynamics of neural processes in brains. Here, we investigated this proposal by focusing on the possibility that brains are poised at the edge of a critical phase transition and that such a state is enabling increased computational power and adaptability. We applied mathematical tools developed in computational neuroscience to a dataset of bee movement trajectories that were recorded within the hive during the course of many days. We found that certain characteristics of the activity of the bee hive system are consistent with the Ising model when it operates at a critical temperature, and that the system’s behavioral dynamics share features with the human brain in the resting state.

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Naoki Igarashi, Yukihiko Okada, Hiroki Sayama & Yukie Sano 
Scientific Reports volume 12, Article number: 21484 (2022)

Public memories of significant events shared within societies and groups have been conceptualized and studied as collective memory since the 1920s. Thanks to the recent advancement in digitization of public-domain knowledge and online user behaviors, collective memory has now become a subject of rigorous quantitative investigation using large-scale empirical data. Earlier studies, however, typically considered only one dynamical process applied to data obtained in just one specific event category. Here we propose a two-phase mathematical model of collective memory decay that combines exponential and power-law phases, which represent fast (linear) and slow (nonlinear) decay dynamics, respectively. We applied the proposed model to the Wikipedia page view data for articles on significant events in five categories: earthquakes, deaths of notable persons, aviation accidents, mass murder incidents, and terrorist attacks. Results showed that the proposed two-phase model compared favorably with other existing models of collective memory decay in most of the event categories. The estimated model parameters were found to be similar across all the event categories. The proposed model also allowed for detection of a dynamical switching point when the dominant decay dynamics exhibit a phase shift from exponential to power-law. Such decay phase shifts typically occurred about 10 to 11 days after the peak in all of the five event categories.

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Márton Pósfai, Balázs Szegedy, Iva Bačić, Luka Blagojević, Miklós Abért, János Kertész, László Lovász, Albert-László Barabási
The emergence of detailed maps of physical networks, like the brain connectome, vascular networks, or composite networks in metamaterials, whose nodes and links are physical entities, have demonstrated the limits of the current network science toolset. Indeed, link physicality imposes a non-crossing condition that affects both the evolution and the structure of a network, in a way that is not captured by the adjacency matrix alone, the starting point of all graph-based approaches. Here we introduce a meta-graph that helps us discover an exact mapping between linear physical networks and independent sets, a central concept in graph theory. The mapping allows us to analytically derive both the onset of physical effects and the emergence of a jamming transition, and show that physicality impacts the network structure even when the total volume of the links is negligible. Finally, we construct the meta-graphs of several real physical networks, allowing us to predict functional features, like synapse formation in the brain connectome, in agreement with the empirical data. Overall, we find that once present, physicality fundamentally alters the structure of a network, changes that must be quantified to understand the underlying systems.

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Ramona Roller, Maximilian Schich, Hyejin Youn and Mikhail Tamm

Advances in Complex SystemsVol. 25, No. 05n06, 2202002 (2022)

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Andy Clark

Australasian Journal of Philosophy

How do intelligent agents spawn and exploit integrated processing regimes spanning brain, body, and world? The answer may lie in the ability of the biological brain to select actions and policies in the light of counterfactual predictions—predictions about what kinds of futures will result if such-and-such actions are launched. Appeals to the minimization of ‘counterfactual prediction errors’ (the ones that would result under various scenarios) already play a leading role in attempts to apply the basic toolkit of the neurocomputational theory known as ‘predictive processing’ to higher cognitive functions such as policy selection and planning. In this paper, I show that this also leads naturally to the discovery and use of extended processing regimes defined across heterogeneous mixtures of biological and non-biological resources. This solves a long-standing puzzle concerning the ‘recruitment’ of the right non-neural processing resources at the right time. It reveals how (and why) human brains spawn and maintain extended human minds.

Read the full article at: www.tandfonline.com