A model of learning temporal delays, representative of adaptive myelination

The proposed model comprises of three layers (Figure 1A.); the first layer represents the input (different bar orientations and corresponding spatial locations (Figure 1B.)) to the Self Organizing Map (SOM) (second layer). For every bar orientation, a different neuron in the SOM is activated for each spatial location (Figure 1C.). This sequence of static outputs are cascaded depending on the direction of motion for each orientation and fed to the third layer as dynamic spike trains (Figure 1D.). The weights between the second and the third layer are trained by Hebbian learning and normalized after each input presentation. Furthermore, the delays are simultaneously trained using the STDP algorithm wherein the pre-synaptic spikes are input spike trains, time shifted by temporal delays. The post synaptic spikes are calculated by integrating the Post Synaptic Potentials (PSPs), for a given threshold and the neuron having the maximum amplitude of the integrated PSP is chosen as the winner. Simulation of such a network results in different neurons activated in response to motions of bars of different orientations. In contemporary neural network studies, temporal delays are typically ignored or held constant. However, the plasticity of conduction delays adds a novel dimension to the study of neural information processing. Moreover, future exploration in this domain could possibly explain the correlations of hyper and hypo synchrony of neural firing with disorders such as dyslexia and schizophrenia [3].