| The human genome (containing ∼ 1010 bits of information) is unlikely to fully specify the connectivity between neurons in our brains---such a "wiring diagram" requires ∼ 1014 bits. Physiological evidence suggests that the genome instead specifies plasticity rules through which the brain self-organizes in response to experience. As systems neuroscientists, we seek to understand those rules and, by extension, our brains. In this thesis, I will use this approach to study the primary visual cortex (V1)---the brain region that receives visual inputs from the eyes, via a relay station called the lateral geniculate nucleus. I first study the statistical structure of natural images, which provide the visual experience that shapes V1. Then, I introduce a biophysically motivated model for visual cortex, which adapts to natural image statistics in order to efficiently encode them---in this case, the neural plasticity rules can be shown to optimize this "efficient" representation. I then demonstrate that this model can account for several features of V1 physiology, including the features to which V1 neurons respond ("receptive fields"), and the developmental trends in the sparseness of V1 activity. I will conclude that efficient coding models can be implemented within the constraints imposed by the neural substrate, and that efficient coding principles may yield a parsimonious systems-level understanding of visual cortex. |
