Lana Sinapayen, Atsushi Masumori, and Takashi Ikegami

Front. Comput. Neurosci., 22 January 2020

 

Complex environments provide structured yet variable sensory inputs. To best exploit information from these environments, organisms must evolve the ability to anticipate consequences of new stimuli, and act on these predictions. We propose an evolutionary path for neural networks, leading an organism from reactive behavior to simple proactive behavior and from simple proactive behavior to induction-based behavior. Based on earlier in-vitro and in-silico experiments, we define the conditions necessary in a network with spike-timing dependent plasticity for the organism to go from reactive to proactive behavior. Our results support the existence of specific evolutionary steps and four conditions necessary for embodied neural networks to evolve predictive and inductive abilities from an initial reactive strategy.

Source: www.frontiersin.org

Lorenzo Zino, Alessandro . Rizzo, Maurizio Porfiri

European Journal of Control

 

The complexity of interaction patterns among individuals in social systems plays a fundamental role on the inception and spreading of epidemic outbreaks. Empirical evidence has shown that the network of social interactions may co-evolve with the spread of the disease at comparable time-scales. Time-varying features have also been documented in the study of the propensity of individuals toward social activity, leading to the emergence of burstiness and temporal clustering. These temporal network dynamics are not independent of the disease evolution, whereby infected individuals could experience changes in their tendency to form connections, spontaneously or due to exogenous control policies. Neglecting these phenomena in modeling epidemics could lead to dangerous mispredictions of an outbreak and ineffective control interventions. In this paper, we propose a mathematically tractable modeling framework that relies on a limited number of parameters and encapsulates all these instances of complex phenomena through the lens of activity driven networks. Hawkes processes, Markov chains, and stability theory are leveraged to assist in the analysis of the framework and the formulation of theory-based control interventions. Our mathematical findings confirm the intuition that bursty activity patterns, typical of humans, facilitate epidemic spreading, while behavioral changes aiming at individual isolation could accelerate the eradication of epidemics. The proposed tools are demonstrated on a real-world case of influenza spreading in Italy. Overall, this work contributes new insight into the theory of temporal networks, laying the foundations for the analysis and control of spreading processes over networks with complex interaction patterns.

Source: www.sciencedirect.com

The dendritic arms of some human neurons can perform logic operations that once seemed to require whole neural networks.

Source: www.quantamagazine.org

F. Molnar, T. Nishikawa, and A.E. Motter,
Nature Physics (2020), doi:10.1038/s41567-019-0742-y.

Symmetry breaking—the phenomenon in which the symmetry of a system is not inherited by its stable states—underlies pattern formation, superconductivity and numerous other effects. Recent theoretical work has established the possibility of converse symmetry breaking, a phenomenon in which the stable states are symmetric only when the system itself is not. This includes scenarios in which interacting entities are required to be non-identical in order to exhibit identical behaviour, such as in reaching consensus. Here we present an experimental demonstration of this phenomenon. Using a network of alternating-current electromechanical oscillators, we show that their ability to achieve identical frequency synchronization is enhanced when the oscillators are tuned to be suitably non-identical and that converse symmetry breaking persists for a range of noise levels. These results have implications for the optimization and control of network dynamics in a broad class of systems whose function benefits from harnessing uniform behaviour.

Source: www.nature.com