Emergence of binocular disparity selectivity through Hebbian learning

Abstract Neural responses in the early visual cortex strongly reflect the statistics of our environment. Although this has been described extensively in literature through various encoding hypotheses, an explanation as to how the cortex develops the structures to support these encoding schemes remains elusive. Here, using the more realistic example of binocular vision as opposed to monocular luminance-field images, we show that a simple Hebbian coincidence-detector is capable of accounting for the emergence of binocular, disparity selective, receptive fields. We propose a model based on spike-timing dependent plasticity (STDP) which not only converges to realistic single-cell and population characteristics, but also demonstrates how known biases in natural statistics may influence population encoding and downstream correlates of behaviour. Our modelling results suggest that Hebbian coincidence-detection is an important computational principle, and could provide a biologically plausible mechanism for the emergence of selectivity to natural statistics in the early sensory cortex.