Adaptive behavior

…Researchers compare biological behavior with robot experiments, computer simulations and mathematical models to evaluate principles and architectures of adaptive behavior. The programme covers collective intelligence, learning and skill acquisition, artificial perception and manipulation, and physical and social constraints on behavior. A cross-kingdom intelligence symposium includes Paco Calvo, Jens Krause, Vera Meyer and Ute Schmid. The meeting runs 19–22 October 2026 in

…various aspects of life, like adaptive behavior, memory, and anxiety, but first there need to be reliable paradigms. Recently, attention has been directed toward establishing such paradigms in birds. We administered two tasks assessing neophilia and neophobia in pigeons to measure both behaviors in the s

Selective visual attention is a key feature of adaptive behavior, improving stimulus detection and response speed. Although it is well established that several measures of neural activity are modulated by attention in many brain areas, it is unclear whether and how these changes in activity correlate with behavior and how different cell types contribute to these mechanisms. To unravel the link between the behavioral benefits induced by spatial attention and its neuronal mechanisms, we analyzed neuronal responses obtained from simultaneous extracellular recordings in PFC and visual area V4 of t

Adaptive behavior in a dynamically changing environment requires learning the value of actions, and updating these values in response to changes in the environment. Several studies have investigated the role of the striatum, and its key neuromodulators, dopamine (DA) and acetylcholine (Ach), in this process. However, these studies usually investigated these neuromodulatory signals in animals which had undergone extensive pre-training, and were thus experts at flexibly adapting to new action values. In addition, most of these studies did not explore the dynamics of these signals simultaneously,

Adaptive behavior is guided by dynamic evaluations of the reward environment, which can be influenced by factors like reward frequency and internal motivational state. We implemented a behavioral task in water deprived headfixed mice that included fluctuating probabilities of reward following auditory stimuli. Mice adjusted their anticipatory licking, a proxy for reward expectation, according to both the recent reward rate and their diminishing motivation for reward. We developed a reinforcement learning model that revealed the synergistic impacts of recent reward history, overall reward rate,

…mental states, using this to inform behavior. However, it is unclear whether or how ToM could emerge through learning to act in a social environment. We sought to identify network mechanisms of ToM in a recurrent neural network trained to play a competitive neuroscience task that requires ToM. The network, trained using deep reinforcement learni