An organism’s ability to control the timing and direction of energy flow both within its body and out to the surrounding environment is vital to maintaining proper function. When physically interacting with an external target, the mechanical energy applied by the organism can be transferred to the target as several types of output energy, such as target deformation, target fracture, or as a transfer of momentum. The particular function being performed will dictate which of these results is most adaptive to the organism. Chewing food favors fracture, whereas running favors the transfer of momentum from the appendages to the ground. Here, we explore the relationship between deformation, fracture, and momentum transfer in biological puncture systems. Puncture is a widespread behavior in biology requiring energy transfer into a target to allow fracture and subsequent insertion of the tool. Existing correlations between both tool shape and tool dynamics with puncture success do not account for what energy may be lost due to deformation and momentum transfer in biological systems. Using a combination of pendulum tests and particle tracking velocimetry (PTV), we explored the contributions of fracture, deformation and momentum to puncture events using a gaboon viper fang. Results on unrestrained targets illustrate that momentum transfer between tool and target, controlled by the relative masses of the two, can influence the extent of fracture achieved during high-speed puncture. PTV allowed us to quantify deformation throughout the target during puncture and tease apart how input energy is partitioned between deformation and fracture. The relationship between input energy, target deformation and target fracture is non-linear; increasing impact speed from 2.0 to 2.5 m/s created no further fracture, but did increase deformation while increasing speed to 3.0 m/s allowed an equivalent amount of fracture to be achieved for less overall deformation. These results point to a new framework for examining puncture systems, where the relative resistances to deformation, fracture and target movement dictate where energy flows during impact. Further developing these methods will allow researchers to quantify the energetics of puncture systems in a way that is comparable across a broad range of organisms and connect energy flow within an organism to how that energy is eventually transferred to the environment.
Sources of predictive information in dynamical neural networks
Abstract Behavior involves the ongoing interaction between an organism and its environment. One of the prevailing theories of adaptive behavior is that organisms are constantly making predictions about their future environmental stimuli. However, how they acquire that predictive information is still poorly understood. Two complementary mechanisms have been proposed: predictions are generated from an agent’s internal model of the world or predictions are extracted directly from the environmental stimulus. In this work, we demonstrate that predictive information, measured using bivariate mutual information, cannot distinguish between these two kinds of systems. Furthermore, we show that predictive information cannot distinguish between organisms that are adapted to their environments and random dynamical systems exposed to the same environment. To understand the role of predictive information in adaptive behavior, we need to be able to identify where it is generated. To do this, we decompose information transfer across the different components of the organism-environment system and track the flow of information in the system over time. To validate the proposed framework, we examined it on a set of computational models of idealized agent-environment systems. Analysis of the systems revealed three key insights. First, predictive information, when sourced from the environment, can be reflected in any agent irrespective of its ability to perform a task. Second, predictive information, when sourced from the nervous system, requires special dynamics acquired during the process of adapting to the environment. Third, the magnitude of predictive information in a system can be different for the same task if the environmental structure changes.
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- NSF-PAR ID:
- 10286540
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-020-73380-x
