Zero-sum Hebbian learning rules that reinforce well correlated inputs have been used by others to model the competitive self-organization of afferents from the lateral geniculate nucleus to produce orientation selectivity and ocular dominance columns. However, the application of these simple Hebbian rules to the development of direction selectivity (DS) is problematic because the best correlated inputs are those that are well correlated in both the preferred and nonpreferred directions of motion. Such afferents would combine to produce non-DS cortical units. Afferents that are in spatiotemporal quadrature would combine to produce DS cortical units, but are poorly correlated in the nonpreferred direction. In this paper, the development of DS is reduced to the problem of associating a pair of units in spatiotemporal quadrature in the face of competition from a third, non-quadrature unit. As expected, simple Hebbian learning rules perform poorly at associating the quadrature pair. However, two additional Hebb-type learning rules, postsynaptic gating and BCM (Bienenstock, Cooper and Munro), improve performance. Results from this three-input model are shown to generalize to a larger network. We conclude that learning rules in which the postsynaptic response determines the magnitude and/or direction of synaptic change perform better than simple Hebbian rules at establishing direction selectivity.
ASJC Scopus subject areas
- Neuroscience (miscellaneous)