Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus

A major challenge in studying sensory processing is to under-stand the meaning of the neural messages encoded in the spiking activity of neurons. From the recorded responses in a sensory circuit, what information can we extract about the outside world? Here we used a linear decoding technique to reconstruct spatiotemporal visual inputs from ensemble re-sponses in the lateral geniculate nucleus (LGN) of the cat. From the activity of 177 cells, we have reconstructed natural scenes with recognizable moving objects. The quality of reconstruction depends on the number of cells. For each point in space, the quality of reconstruction begins to saturate at six to eight pairs of on and off cells, approaching the estimated coverage factor in the LGN of the cat. Thus, complex visual inputs can be reconstructed with a simple decoding algorithm, and these analyses provide a basis for understanding ensemble coding in the early visual pathway. Key words: LGN; reconstruction; natural scenes; ensemble responses; cat; visual system The foundation of our current knowledge of sensory processing was established by characterizing neuronal responses to variou