Shared principles of learning in ecological and neural networks

Ecological and neural networks share fundamental principles of adaptation and learning. Here, we reformulate the Lotka-Volterra model of species interactions as a neural network model. This transformation enables adaptive modification of interaction coefficients based on the difference between expected (i.e. predicted) and actual population size. This mechanism is analogous to synaptic plasticity in the brain, where neurons minimize the discrepancy between actual and expected activity. Our simulations reveal that ecological systems can ‘learn,’ adjusting interactions to support larger predator populations under changing environmental conditions. This suggests that ecological networks, like neural networks, self-organize through predictive adaptation, which was shown to also maximize metabolic energy. Thus, prediction-based adjustment of interactions may offer a unifying framework for understanding biological complexity across scales: from the single-cell level (i.e. neurons) to the interactions at the level of animal species.