Biological computations: limitations of attractor-based formalisms and the need for transients (2404.10369v1)

Abstract: Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, e.g., via cell surface receptors or sensory organs. Integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processes can be understood as a type of biological computation. Taking as a basis generic features of biological computations, such as real-time responsiveness or robustness and flexibility of the computation, we highlight the limitations of the current attractor-based framework for understanding computations in biological systems. We argue that frameworks based on transient dynamics away from attractors are better suited for the description of computations performed by neuronal and signaling networks. In particular, we discuss how quasi-stable transient dynamics from ghost states that emerge at criticality have a promising potential for developing an integrated framework of computations, that can help us understand how living system actively process information and learn from their continuously changing environment.

• The paper demonstrates that traditional attractor-based models are insufficient for capturing the dynamic adaptability required by biological systems.
• It employs the EGFR network example to illustrate how stable states fail to accommodate continuous, real-time environmental fluctuations.
• The study advocates for transient dynamics, highlighting ghost states as a promising approach for mimicking flexible, adaptive cellular responses.

Analyzing the Limitations of Attractor-Based Formalisms in Biological Computations

The paper "Biological computations: limitations of attractor-based formalisms and the need for transients" systematically examines the inherent complexities in biological computations that transcend the traditional attractor-based frameworks typically used in computational models. The authors, Koch et al., posit that since living organisms engage with continuous, non-stationary inputs, they require a computational understanding that accommodates real-time responsiveness and dynamic adaptability.

Limitations of Attractor-Based Models

The attractor-based model, primarily inspired by theories from mathematical and computational disciplines, hinges on the notion that systems converge to stable states when subjected to perturbations. Current understanding, extrapolated from machine computations, suggests that these states, or attractors, are sufficient for modeling cognitive processes and cellular responses. This paper challenges this notion, pointing out that such frameworks fail under conditions where biological systems must continuously adapt to fluctuating environments, such as those seen in immune responses or behavioral adaptations in animals.

Evidence Against Attractor Models in Cellular Computation

Using the example of the epidermal growth factor receptor (EGFR) network, the paper illustrates the shortcomings of attractor-based computation. Cells responding to spatiotemporal signals encounter limitations when the system's dynamics are described solely by stable states due to the inherent need for real-time adaptation. In scenarios requiring responsiveness to temporal variations, attractor-based models fall short as they do not accommodate the flexibility necessary to integrate evolving environmental cues effectively.

Transients and Quasi-Stable Dynamics

Koch et al. advocate for a transient-dynamics-based approach, where quasi-stable states that emerge at criticality provide a better model for biological processes. The concept of ghost states, as discussed in the paper, offers one such promising avenue. These states, characterized by temporary stability, facilitate memory retention and real-time processing, maintaining responsiveness without requiring a system to reside indefinitely in a defined attractor.

Transients, particularly those described by ghost states, enable biological computations to adapt and process information dynamically. This model offers enhanced flexibility, evidenced by its ability to process signals with varying frequencies or interruptions—an advantage in realistic biological settings that require immediate and adaptable responses.

Future Research Directions

The paper underscores the potential of transient-based computational approaches in forming a cohesive framework applicable to both neural and cellular systems across different scales. Emphasizing the need for further studies into these transient mechanisms, it suggests that future research should focus on elucidating the geometric and dynamical properties that could unify our understanding of natural computations. An integrated framework might offer insights into unresolved questions about anticipatory processes, adaptability, and lifelong learning in biological entities.

In conclusion, this paper calls for a paradigm shift from traditional attractor-based models to transient dynamics, highlighting their promise in mirroring the nuanced, real-time adaptability seen in living systems. By exploring how transient dynamics can better simulate the essential features of biological computation, this research paves the way for innovative computational models that more closely align with the complex nature of life.