The AHA Model

Salmonids are important fish species in aquaculture in countries in the temperate zone. Optimisation of feeding in next-generation precision fish farming requires developing models for decision support and process control. Black box ML and AI models are often very efficient but have drawbacks, such as requiring large amount of training data and reduced performance in novel situations where no data are available. Thus, developing realistic process models of fish appetite, feeding decisions, feed intake, energetics and growth is necessary. Such models are essential for predicting fish performance, for example, feed intake, waste from uneaten feed and faeces, growth, in novel ‘what if’ scenario testing. We have built a conceptual model based on a review of major neurophysiological mechanisms and feedback loops controlling appetite and food intake in fish. Building on this, we have developed the FishMet model: a new extensible stochastic simulation framework that represents the basic feedback loops controlling appetite, feeding decisions, energy budget and growth in salmonid fish. The appetite and feeding decision model in FishMet is the novel advance, while the bioenergetic part follows the established theory. The model is supported by server-based components and open API for data assimilation and on-demand model execution that allows to use FishMet as a digital twin. We demonstrate relatively good prediction of stomach and gut digesta transit and food intake in the rainbow trout Oncorhynchus mykiss. The digital twin also demonstrated good prediction of growth and feeding efficiency in a pilot scale experiment on the Atlantic salmon Salmo salar. We discuss the concept of the digital twin and the directions of further development of the model as an applied predictive tool. doi:10.1002/aff2.70064

Combining biological research and mathematical modeling has opened the door to a new approach to fish farming.

We meet professor of physiology, Ivar Rønnestad and researcher, Sergey Budaev at the High Technology Center in Bergen. Both work at the Department of Biosciences at the University of Bergen and are behind …

Aquaculture of Atlantic salmon Salmo salar is in transition to precision fish farming and digitalization. As it is easier, cheaper and safer to study a digital replica than the system itself, a model of the fish can potentially improve monitoring and prediction of facilities and operations and replace live fish in many what-if experiments. Regulators, consumers and voters also want insight into how it is like to be a salmon in aquaculture. However, such information is credible only if natural physiology and behaviour of the living fish is adequately represented. To be able to predict salmon behaviour in unfamiliar, confusing and stressful situations, the modeller must aim for a sufficiently realistic behavioural model based on the animal's proximate robustness mechanisms. We review the knowledge status and algorithms for how evolution has formed fish to control decisions and set priorities for behaviour and ontogeny. Teleost body control is through genes, hormones, nerves, muscles, sensing, cognition and behaviour, the latter being agentic, predictive and subjective, also in a man-made environment. These are the challenges when constructing the digital salmon. This perspective is also useful for modelling other domesticated and wild animals in Anthropocene environments. doi:10.1002/aff2.153

New paper just appeared in Royal Society Open Science: "Computational animal welfare: Towards cognitive architecture models of animal sentience, emotion and wellbeing"

New paper in Frontiers in Ecology and Evolution: "Decision-making from the animal perspective: Bridging ecology and subjective cognition"

New paper in Biologically Inspired Cognitive Architectures: "AHA: A general cognitive architecture for Darwinian agents"