Modeling somatic computation with non-neural bioelectric networks

Santosh Manicka,Michael Levin

doi:10.1038/s41598-019-54859-8

Abstract

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.

Highlights

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in nonneural biological systems
This theme is reinforced by classical observations such as the fact that tails grafted onto the side of salamanders slowly remodel into limbs[15], demonstrating the ability of tissue to ascertain its position within the whole, compare its organ-level anatomy with that dictated by the target morphology, and remodel toward that correct anatomical setpoint[16]
We demonstrate the logic-capabilities of Bio-Electric Network (BEN) networks by constructing (1) small elementary logic gates; (2) larger “tissue-level” elementary logic gates; (3) compound logic gates composed from elementary gates; and (4) a pattern detector using standard machine learning methods

Summary

Introduction

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in nonneural biological systems. Nerves may have speed-optimized ancient bioelectric processes that, since the time of bacterial biofilms[18], were already exploited by evolution to implement memory, long-range coordination, and decision-making utilized for maintenance and construction of anatomical structures[19]. In multicellular organisms, these same functions are used to control large-scale patterning[20,21,22,23]. We show that non-neural bioelectric networks can compute logic functions, suggesting one way in which evolution can exploit biophysics for decision-making in cellular systems This provides a new connection between non-neural physiology and a common kind of computational task, expanding the known capabilities of developmental bioelectricity. We extend established connectionist approaches to a more general physiological setting and analyze them to reveal how ionic dynamics in non-neuronal cells could implement both simple and complex logic gates

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Conclusion