Components of change and the evolution of learning in theory and experiment

A neural network model for the evolution of learning in changing environments

Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a clear effect on the evolved learning strategy: a shorter lifespan or a broader resource distribution lead to fewer learning episodes and larger learning rates. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment.

Learning in single cell organisms

The survival of all species requires appropriate behavioral responses to environmental challenges. Learning is one of the key processes to acquire information about the environment and adapt to changing and uncertain conditions. Learning has long been acknowledged in animals from invertebrates to vertebrates but remains a subject of debate in non-animal systems such a plants and single cell organisms. In this review I will attempt to answer the following question: are single cell organisms capable of learning? I will first briefly discuss the concept of learning and argue that the ability to acquire and store information through learning is pervasive and may be found in single cell organisms. Second, by focusing on habituation, the simplest form of learning, I will review a series of experiments showing that single cell organisms such as slime molds and ciliates display habituation and follow most of the criteria adopted by neuroscientists to define habituation. Then I will discuss disputed evidence suggesting that single cell organisms might also undergo more sophisticated forms of learning such as associative learning. Finally, I will stress out that the challenge for the future is less about whether or not to single cell organisms fulfill the definition of learning established from extensive studies in animal systems and more about acknowledging and understanding the range of behavioral plasticity exhibited by such fascinating organisms.

What are the costs of learning? Modest trade-offs and constitutive costs do not set the price of fast associative learning ability in a parasitoid wasp

Learning ability has been associated with energetic costs that typically become apparent through trade-offs in a wide range of developmental, physiological, and life-history traits. Costs associated with learning ability can be either constitutive or induced, depending on whether they are always incurred or only when information is actively learned and memorized. Using lines of the parasitoid wasp Nasonia vitripennis that were selected for fast associative learning ability, we assessed a range of traits that have previously been identified as potential costs associated with learning. No difference in longevity, lipid reserves, tibia length, egg load, or fecundity was observed between the selected and control lines. All of these traits are considered to potentially lead to constitutive costs in the setup of this study. A gradual reversal to baseline learning after two forms of relaxed selection was indicative of a small constitutive cost of learning ability. We also tested for a trade-off with other memory types formed at later stages, but found no evidence that the mid-term memory that was selected for caused a decrease in performance of other memory types. In conclusion, we observe only one minor effect of a constitutive cost and none of the other costs and trade-offs that are reported in the literature to be of significant value in this case. We, therefore, argue for better inclusion of ecological and economic costs in studies on costs and benefits of learning ability.