Information theory, predictability and the emergence of complex life

Seoane, Luís F.; Solé, Ricard V.

doi:10.1098/rsos.172221

Cited by 30 publications

(42 citation statements)

References 57 publications

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“…We demonstrate that in such a setting, a more complex strategy involving effectively averaging over a batch of molecules can be advantageous if correlations are long-ranged, but noise is substantial. This is similar to the result in [51] that a more complex predictive model is advantageous in a more complex environment, but, in this paper, we give an explicit physical model for how our machines measure and exploit the environment. In our design, the complexity of the mechanism involved the ability to couple to multiple inputs simultaneously; we predict that alternatives (such as systems with larger memories and more possible decision) would also show the potential for improved performance.…”

Section: Discussionsupporting

confidence: 78%

Biochemical Szilard engines for memory-limited inference

2019

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By designing and leveraging an explicit molecular realisation of a measurement-and-feedbackpowered Szilard engine, we investigate the extraction of work from complex environments by minimal machines with finite capacity for memory and decision-making. Living systems perform inference to exploit complex structure, or correlations, in their environment, but the physical limits and underlying cost/benefit trade-offs involved in doing so remain unclear. To probe these questions, we consider a minimal model for a structured environment-a correlated sequence of moleculesand explore mechanisms based on extended Szilard engines for extracting the work stored in these non-equilibrium correlations. We consider systems limited to a single bit of memory making binary 'choices' at each step. We demonstrate that increasingly complex environments allow increasingly sophisticated inference strategies to extract more free energy than simpler alternatives, and argue that optimal design of such machines should also consider the free energy reserves required to ensure robustness against fluctuations due to mistakes. perform work [3,4]. A more subtle and equally fundamental possibility is exploiting structure across multiple bits in the array-its entropy can be low due to correlations within the data, rather than an overall bias at the level of individual bits [5][6][7][8][9]. However, the principles of designing devices to optimally exploit correlations in general settings remain unclear [9].Although inspired by the physics of computation, the question of how to exploit correlations is also of fundamental biological relevance. If organisms existed in a homogeneous non-equilibrium environment, there would be no need to develop sophisticated information-processing machinery to survive. However, from the chemotaxis system of E. coli to the brains of humans, complex molecular and cellular networks have been evolved to exploit the fact that the environment exhibits correlated fluctuations. These systems rely on the fact that what is sensed at a certain point in space and time contains information about nearby points [8]: they have evolved even though they are costly to maintain, and despite the fact that the information obtained is limited by features such as the memory and processing power available [11]. However, the fundamental trade-offs that determine the sophistication of these systems are not fully explored.In this paper we take steps towards unifying these two perspectives on the exploitation of correlations. We first present a molecular design for a measurement-and-feedback device (a Szilard engine [12]) in which the mechanics of the feedback is explicit within the molecular system. We then leverage this construct to propose biomolecular machines that make repeated binary choices about how to act based on measurements of their environment (an array of 'molecular bits'). These machines use their single bit of memory to extract chemical work from correlated arrays, demonstrating that it is possible to design minimal biophysical sy...

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Section: Discussionsupporting

confidence: 78%

Biochemical Szilard engines for memory-limited inference

2019

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“…More advanced creatures possess richer networks that allow complex forms of learning in which memories and goal states are implemented by settings (configurations) of internal mechanism several biochemical steps removed from the actual state in question (perhaps the origin of symbolic representation in more complex minds). Reactive homeostasis evolves into predictive allostasis (Schulkin and Sterling, 2019), under the pressure to predict signals from environment and other elements of the biosphere (Seoane and Sole, 2018). Importantly, memory can serve as the beginning of modularity because learning essentially groups diverse stimuli into compressed representations: complex states of affairs become remembered as compact biophysical engrams -this is the essence of the kind of modularity when a simple biophysical event kicks off the formation of a complex morphogenetic cascade such as building a "hand" in embryogenesis.…”

Section: A B Cmentioning

confidence: 99%

The Computational Boundary of a “Self”: Developmental Bioelectricity Drives Multicellularity and Scale-Free Cognition

2019

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All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, and molecular networks) that are themselves complex and competent in their own native contexts. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan creatures' bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger organism. In this article, I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: "Scale-Free Cognition." I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface -the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundarya cognitive "light cone" which defines the scale and limits of its cognition. I hypothesize that higher level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress -the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity -the ability of all cells to form electrical networks that process informationsuggest a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the basic drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology.

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“…For example, research on non-equilibrium thermodynamics has developed an understanding of ecosystem services, and even information in human societies (Odum, 1988) that is explicitly related to thermodynamic entropy and its related information (Jorgensen et al, 2000). Extensive work has employed structural information concepts and theory to understand macrostates in biological and ecological systems (Sole et al, 1996;Harte, 2011;Seoane and Solé, 2018).…”

Section: Examples Of Information Measures In Ecological Studiesmentioning

confidence: 99%