Parallel Computational Subunits in Dentate Granule Cells Generate Multiple Place Fields
Figure 3
Independent feature detection with Habbian synapses.
(A) Synaptic plasticity separates the inputs. Before learning the total synaptic input to a dendritic subunit come from a Gaussian distribution (600 samples are shown with grey circles) with the calculated density function shown on the right (Eq. 5). During the learning process each branch learns its largest input and the response increases to the learned input (blue circle), while it decreases to all other inputs (black circles). The black and blue Gaussian curves show the density functions for the non-learned and learned inputs, respectively (Eq. 19). (B), (D): Color-coded joint distribution of the somatic activation and the maximal dendritic input () in the linear (B) and quadratic (D) case. The horizontal lines indicate the firing threshold. (C), (E): The distribution of the maximal dendritic input when the cell fires (K(U*) in red) and the probability of firing with a given input (H(U) in blue). The distribution of U* is shown in the background. In the linear case, 50% of firing occurs when one of the branches receives its preferred input, while with quadratic integration function more than 95%. Parameters: R = 0.01, N = 30.