Communications of the ACM
The Strength of Weak Learnability
Machine Learning
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Self-Organizing Maps
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Computing Information in Neuronal Spikes
Neural Processing Letters
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Neurons are electrically active structures determined by the evolution of ion-specific pumps and channels that allow the transfer of charges under the influence of electric fields and concentration gradients. Extensive studies of spike timing of neurons and the relationship to learning exist. However, the properties of spatial activations during action potential in the context of learning have to our knowledge not been consistently studied. We examined spatial propagation of electrical signal for many consecutive spikes using recorded information from tetrodes in freely behaving rats before and during rewarded T-maze learning tasks. Analyzing spatial spike propagation in expert medium spiny neurons with the charge movement model we show that electrical flow has directionality which becomes organized with behavioral learning. This implies that neurons within a network may behave as "weak learners" attending to preferred spatial directions in the probably approximately correct sense. Importantly, the organization of spatial electrical activity within the neuronal network could be interpreted as representing a change in spatial activation of neuronal ensemble termed "strong learning." Together, the subtle yet critical modulations of electrical flow directivity during weak and strong learning represent the dynamics of what happens in the neuronal network during acquisition of a behavioral task.