Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals. Motivated by the cascade structure of the visual pathway (retina lateral geniculate nucelus (LGN) primary visual cortex, V1) we propose to model its function using lattice filters - signal processing devices for stage-wise decorrelation of temporal signals. Lattice filter models predict neuronal responses consistent with physiological recordings in cats and primates. In particular, they predict temporal receptive fields of two different types resembling so-called lagged and non-lagged cells in the LGN. Moreover, connection weights in the lattice filter can be learned using Hebbian rules in a stage-wise sequential manner reminiscent of the neuro-developmental sequence in mammals.

Early stages of visual processing are thought to decorrelate, or whiten, the incoming temporally varying signals. Because the typical correlation time of natural stimuli, as well as the extent of temporal receptive fields of lateral geniculate nucleus (LGN) neurons, is much greater than neuronal time constants, such decorrelation must be done in stages combining contributions of multiple neurons. We propose to model temporal decorrelation in the visual pathway with the lattice filter, a signal processing device for stage-wise decorrelation of temporal signals. The stage-wise architecture of the lattice filter maps naturally onto the visual pathway (photoreceptors -> bipolar cells -> retinal ganglion cells -> LGN) and its filter weights can be learned using Hebbian rules in a stage-wise sequential manner. Moreover, predictions of neural activity from the lattice filter model are consistent with physiological measurements in LGN neurons and fruit fly second-order visual neurons. Therefore, the lattice filter model is a useful abstraction that may help unravel visual system function.