  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.
