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. Author manuscript; available in PMC: 2012 Apr 1.
Published in final edited form as: J Comput Neurosci. 2010 Sep 10;30(2):501–513. doi: 10.1007/s10827-010-0275-y

Figure 1.

Figure 1

Temporal representations created by RDE. A. Neurons in the recurrent layer of our network model are stimulated by retinal activation via the LGN. L is the matrix defining lateral excitation. B. With a linear neuron model, time is encoded by the exponential decay rate of an activity variable V. C. In the spiking neuron model, evoked activity (shown by spike rasters, where each row represents a single neuron in the network, and the resultant histogram) in a responsive sub-population of the network persists until the time of reward. In both models, the stimulus is active during the period marked by the gray bar and the reward time is indicated by the dashed line. See (Gavornik et al., 2009) for details of learning with RDE.