Topic: synaptic masks

The human brain quickly adapts to a rapidly changing environment, often faster than what is thought to be possible by way of synaptic plasticity. Real-time control adaptation can only be achieved through faster processes such as neuromodulation. A number of neuromodulation models based on gain regulation have been proposed recently. But most of them utilize neuron-wide regulation and can not accommodate the simultaneous impact of different types of neurotransmitters on different parts of the dendritic tree. Models of synaptic gating and connectivity-tuning demonstrate that using enough additio

The human brain quickly adapts to a changing environment, often faster than can be explained by synaptic plasticity. One of the main candidates to account for such rapid changes is neuromodulation. We explore how modulation can assist a recurrent neural network to accommodate a range of variations of the tasks it is initially trained on. There are a number of approaches allowing for rapid acquisition of new tasks, for example, the neuronal gain modulation model [1]. However, we found that by affecting the input-output function of a neuron such an approach can neither address the variation of a