A dynamical systems view of neuroethology: Uncovering stateful computation in natural behaviors. (April 2022) - Robson, Drew N.; Li, Jennifer M.
Abstract: State-dependent computation is key to cognition in both biological and artificial systems. Alan Turing recognized the power of stateful computation when he created the Turing machine with theoretically infinite computational capacity in 1936. Independently, by 1950, ethologists such as Tinbergen and Lorenz also began to implicitly embed rudimentary forms of state-dependent computation to create qualitative models of internal drives and naturally occurring animal behaviors. Here, we reformulate core ethological concepts in explicitly dynamical systems terms for stateful computation. We examine, based on a wealth of recent neural data collected during complex innate behaviors across species, the neural dynamics that determine the temporal structure of internal states. We will also discuss the degree to which the brain can be hierarchically partitioned into nested dynamical systems and the need for a multi-dimensional state-space model of the neuromodulatory system that underlies motivational and affective states. Highlights: Stateful computation is embedded in ethological models of natural behavior and motivation. Core ethological concepts can be reformulated in dynamical systems terms. A series of nested dynamical systems may underlie some innate behavioral hierarchies. A scalable trigger and linearly decaying state together set intrinsic state durations. A multidimensional state space model may be essential for understanding the highly recurrent neuromodulatory system.