Long Range Wireless Technology

Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of network. We describe method to derive spiketimingdependent plasticity rules for selfrepair also provide the basis for unsupervised learning of new tasks.

Here, selfrepair is illustrated for model of the mammalian olfactory system in which the computational task is that of odor recognition. Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of network.

In this olfactory example, the derived rule has qualitative similarity with experimental results seen in spiketimingdependent plasticity. Ongoing network selfrepair is thus necessary. Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of network. Here, selfrepair is illustrated for model of the mammalian olfactory system in which the computational task is that of odor recognition. Unsupervised learning of new tasks.

Ongoing network selfrepair is thus necessary. Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of network. These plasticity rules for selfrepair, based on the firing patterns of functioning network. The particular plasticity rule derived for network depends on the network and task.

Ongoing network selfrepair is thus necessary. Here, selfrepair is illustrated for model of the mammalian olfactory system in which the computational task is that of odor recognition. In this olfactory example, the derived rule has qualitative similarity with experimental results seen in spiketimingdependent plasticity. We describe method to derive spiketimingdependent plasticity rules for selfrepair also provide the basis for unsupervised learning of new tasks by using the derived selfrepair rule is demonstrated by learning to recognize new odors.