Feedforward circuits have been shown to be very powerful as models of vision.  However, these architectures are apparently incapable of dealing with many visual tasks that the human visual system finds simple, such as identifying occluded figures.  Data from the Allen Institute for Brain Science Cortical Activity Map (CAM) project may give us the opportunity to explore this question.  What is the functional connectivity of cells in mouse V1 ...

The goal of the visual recognition system is to extract abstract features from the retinal image that correlate with real-world objects. The visual system achieves this goal by tuning layer-wise the synaptic weights defining neurons’ receptive fields. One essential feature of the resulting network is that it must be selective but simultaneously invariant: the same feature can appear under changing retinal positions, sizes, rotational angles or partial occlusion. We know that neurons in the adult macaque cortex show selectivity and invariance, but little is known about how they acquire these properties during early development. This is a conceptual gap with important implications for models of visual recognition.

Many of the structures humans impose on the world, such as conceptual categories and intuitive theories, are naturally expressible in symbolic or logical form. Despite the fact that learning problems are discrete, relatively noise-free, and easily represented on a computer, the performance of the best symbolic learning algorithms lags far behind that of humans. This project uses a combination of experimentation and modeling to understand and replicate human skill in symbolic domains.

Sensory systems encode natural scenes with representations of increasing complexity. We seek to learn auditory representations of intermediate complexity, to better understand the structure of sound and to generate hypotheses about neural encoding mechanisms.