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Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired re ...
Although it is widely believed that reinforcement learning is a suitable tool for describing behavioral learning, the mechanisms by which it can be implemented in networks of spiking neurons are not fully understood. Here, we show that different learning r ...
Suppose we train an animal in a conditioning experiment. Can one predict how a given animal, under given experimental conditions, would perform the task? Since various factors such as stress, motivation, genetic background, and previous errors in task perf ...
Reinforcement learning in neural networks requires a mechanism for exploring new network states in response to a single, nonspecific reward signal. Existing models have introduced synaptic or neuronal noise to drive this exploration. However, those types o ...
Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic pla ...
Contextual elements can strongly modulate visual performance. For example, performance deteriorates when a vernier is flanked by neighboring lines. On a neural level, such contextual modulation is often explained by local spatial interactions such as later ...
Association for Research in Vision and Ophthalmology2011
Attending to an object can strongly modulate the neural processing of this object. Using average EEG, it was shown that small differences in the focus of attention yield large and long lasting changes in brain dynamics (Plomp et al, 2009). Here, we show th ...
Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, ...
We study the dynamics of a network consisting of N diffusively coupled, stable- limit-cycle oscillators on which individual frequencies are parametrized by wk,k=1,...,N. We introduce a learning rule which influences the wk by driving the system t ...
Perceptual learning is the ability to modify perception through practice. As a form of brain plasticity, perceptual learning has been studied for more than thirty years in different fields including psychology, neurophysiology and computational neuroscienc ...
