Neural coding in the retina

Due to its experimental accessibility and early position in the visual stream, the retina provides a beautiful opportunity for exploring sensory coding and information processing in large populations of neurons.

Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E. & Chichilnisky, E. (2005). Structure and precision of retinal light responses analyzed with a noisy integrate-and-fire model. Journal of Neuroscience 25: 11003-11013.

Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky, E. & Simoncelli, E. (2008). Spatiotemporal correlations and visual signaling in a complete neuronal population. Nature 454: 995-999.

Lalor, E., Ahmadian, Y. & Paninski, L. (2009). The relationship between optimal and biologically plausible decoding of stimulus velocity in the retina. Journal of the Optical Society of America A (special issue on ideal observers and efficiency) 26: B25-42.

Paninski, L., Ahmadian, Y., Ferreira, D., Koyama, S., Rahnama, K., Vidne, M., Vogelstein, J. & Wu, W. (2009). A new look at state-space models for neural data. Journal of Computational Neuroscience (special issue on statistical analysis of neural data).

Ahmadian, Y., Pillow, J. & Paninski, L. (2010). Efficient Markov Chain Monte Carlo methods for decoding population spike trains. Neural Computation.

Pillow, J., Ahmadian, Y. & Paninski, L. (2010). Model-based decoding, information estimation, and change-point detection in multi-neuron spike trains. Neural Computation.

Babadi, B., Casti, A., Xiao, Y. & Paninski, L. (2010) Visual response in LGN neurons beyond the monosynaptic retinogeniculate transmission. Journal of Vision.

Field, G., Gauthier, J., Sher, A. et al. (2010). Mapping a neural circuit: a complete input-output diagram in the primate retina. Nature.

Liam Paninski's research